From pdhami06 at gmail.com Mon Oct 1 03:58:21 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 30 Sep 2018 21:58:21 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster Message-ID: Dear FieldTrip community, I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From L.M.Talamini at uva.nl Mon Oct 1 13:52:49 2018 From: L.M.Talamini at uva.nl (Talamini, Lucia) Date: Mon, 1 Oct 2018 11:52:49 +0000 Subject: [FieldTrip] Job post for the Fieldtrip mailing list Message-ID: Postdoctoral position on Sleep and Memory University of Amsterdam Job description We are looking for a postdoctoral scientist to join our research on manipulation of sleep and memories using EEG-guided neurostimulation. You will use a well-validated and highly flexible closed-loop neurostimulation (CLNS) method, developed at the UvA-Sleep and Memory lab. We have recently using this method deepen sleep and that to boost or depress individual memories. Responsibilities will also involve co-supervision of interns and junior investigators working on other CLNS projects. The lab The research will be executed at the UvA’s state of the art Sleep and Memory Lab, embedded in the Brain and Cognition Group, Department of Psychology, University of Amsterdam. The lab takes part in the interdisciplinary ABC (Amsterdam Brain and Cognitive Science Center). Requirements Applicants should have a PhD in (cognitive) neuroscience, bio-electrical signal analysis, or a related field. The candidate should have extensive experience in with EEG signal analysis (preferably involving methods development and application) and strong programming skills, e.g. in MATLAB and/or Python. Excellent scientific writing skills in English are required and some level of IT and engineering know-how will be considered a plus. Finally, flexibility with regard to working hours is expected in view of the type of research. Appointment Initial appointment will be for approximately 10 months, in case of full time employment. Part-time employment can be discussed. The appointment may be extended using expected ulterior funding. The salary will be in accordance with the university regulations for academic personnel (Collective Labor Agreement Dutch Universities). Job application For further information about this position please contact Dr. L.M. Talamini (phone: +31 6 4764 5932; e-mail: L.M.Talamini at uva.nl). Please send your application letter, curriculum vitae, a transcript of academic grades and courses, a proof of writing (e.g. a research publication) and the names and contact information of two persons willing to provide reference information to Lucia Talamini (L.M.Talamini at uva.nl). Or submit them through this link: http://www.uva.nl/en/content/vacancies/2018/09/18-552-postdoctoral-position-sleep-and-memory.html?a Application closing date: October15th 2018. -------------- next part -------------- An HTML attachment was scrubbed... URL: From L.M.Talamini at uva.nl Mon Oct 1 14:30:07 2018 From: L.M.Talamini at uva.nl (Talamini, Lucia) Date: Mon, 1 Oct 2018 12:30:07 +0000 Subject: [FieldTrip] Postdoctoral position on Sleep and Memory Message-ID: Postdoctoral position on Sleep and Memory University of Amsterdam Job description We are looking for a postdoctoral scientist to join our research on manipulation of sleep and memories using EEG-guided neurostimulation. You will use a well-validated and highly flexible closed-loop neurostimulation (CLNS) method, developed at the UvA-Sleep and Memory lab. We have recently used this method to deepen sleep and to boost or depress individual memories. Responsibilities will also involve co-supervision of interns and junior investigators working on other CLNS projects. The lab The research will be executed at the UvA’s state of the art Sleep and Memory Lab, (Brain and Cognition Group, Dept. of Psychology, University of Amsterdam). The lab takes part in the interdisciplinary ABC (Amsterdam Brain and Cognitive Science Center). Requirements Applicants should have a PhD in (cognitive) neuroscience, bio-electrical signal analysis, or a related field. The candidate should have extensive experience in with EEG signal analysis (preferably involving methods development and application) and strong programming skills, e.g. in MATLAB and/or Python. Excellent scientific writing skills in English are required and some level of IT and engineering know-how will be considered a plus. Finally, flexibility with regard to working hours is expected in view of the type of research. Appointment Initial appointment will be for approximately 10 months, in case of full time employment. Part-time employment can be discussed. The appointment may be extended using expected ulterior funding. The salary will be in accordance with the university regulations for academic personnel (Collective Labor Agreement Dutch Universities). Job application For further information about this position please contact Dr. L.M. Talamini (phone: +31 6 4764 5932; e-mail: L.M.Talamini at uva.nl). Please send your application letter, curriculum vitae, a transcript of academic courses and grades, a proof of writing (e.g. a research publication) and the names and contact information of two persons willing to provide reference information to Lucia Talamini (L.M.Talamini at uva.nl). Or apply through this link: http://www.uva.nl/en/content/vacancies/2018/09/18-552-postdoctoral-position-sleep-and-memory.html?a Application closing date: October15th 2018. -------------- next part -------------- An HTML attachment was scrubbed... URL: From narun.pornpattananangkul at nih.gov Tue Oct 2 05:07:23 2018 From: narun.pornpattananangkul at nih.gov (Pornpattananangkul, Narun (NIH/NIMH) [F]) Date: Tue, 2 Oct 2018 03:07:23 +0000 Subject: [FieldTrip] Postdoctoral position at the National Institute of Mental Health, National Institute of Health Message-ID: The Mood Brain and Development Unit (MBDU) at the US National Institute of Health (NIH) led by Dr Argyris Stringaris is looking for a post-doc. The focus of the MBDU is on how reward processing aberrations impact on human mood and may lead to pathologies such as depression. There is a lot of promise that reward processing abnormalities underlie psychiatric disorders. Yet, only if causal links reward processing aberrations and mood still are elucidated, will they form the basis of targeted treatment design. To resolve these questions, we use a variety of tools, benefitting from the unique scientific environment and resources that the NIH offers. In particular, we use longitudinal imaging protocols at three different time scales, work on methodological development of resting-state and task-based imaging data fusion, leverage treatment designs (such as psychological or pharmacological therapies) and develop closed loop devices for mood manipulation. We are a team that works closely together, has many regular science and social meetings and collaborates extensively with others in and out of NIH. Candidates with a strong background in MRI and at least an interest in neurophysiological methods such as EEG/MEG, as well as a keen interest in methodology and computational modeling will be most suited for the job. Advanced coding in R, Python, Matlab or Shell scripts is a requirement. Cognitive neuroscientists, engineers and other candidates with strong numerical and computational skills are particularly encouraged to apply. The postdoctoral community at the NIH is large (approximately 4,000) strong and vibrant. Trainees come from across the U.S. and around the world. Salary for this position is defined by type of training and years of experience. https://www.training.nih.gov/postdoctoral_irta_stipend_ranges Benefits include health insurance for the trainee and his/her family, and support for coursework related to the trainee's research and travel to meetings is often available. https://www.training.nih.gov/programs/postdoc_irp The NIH is among the largest and best communities of neuroimaging researchers in the world, with opportunities to collaborate with leaders in the field of fMRI, MEG, machine learning, computational psychiatry and neuromodulation. Our group has access to high performance computing, has been allocated timeslots to use fMRI and has our own in-patient unit with full-time clinicians. See more information here https://www.nimh.nih.gov/labs-at-nimh/research-areas/clinics-and-labs/edb/mbdu/index.shtml For more information, please write with CV and expression of interest to Argyris Stringaris: argyris.stringaris at nih.gov and Narun Pornpattananangkul: narun.pornpattananangkul at nih.gov . The National Institutes of Health is an equal opportunity employer. -------------- next part -------------- An HTML attachment was scrubbed... URL: From michelic72 at gmail.com Tue Oct 2 10:31:30 2018 From: michelic72 at gmail.com (Cristiano Micheli) Date: Tue, 2 Oct 2018 10:31:30 +0200 Subject: [FieldTrip] Fwd: please circulate In-Reply-To: References: <8621bbfc-7ad6-af42-629c-604c5237452a@gmail.com> <73582e5b-25bd-8af9-7d70-a49d7e4233fc@gmail.com> <90669498-b492-d745-cdfa-d7cb0b85e1ff@gmail.com> <6ff95a55-5ac0-627b-6fc8-2ad8b26b5692@gmail.com> Message-ID: ---------- Forwarded message --------- From: Jean-Claude Dreher Date: Tue, Oct 2, 2018 at 10:29 AM Subject: please circulate To: Cristiano Micheli Dear Cristiano, Could you please post this add on the fieldtrip website? Thanks a lot, JC The Institute of Cognitive Science, Lyon, France is inviting applications for a postdoctoral position to study social decision making and reward processing using fMRI in healthy subjects and intracranial recordings in patients with epilepsy. The institute of Cognitive Science is located in Lyon, a thriving universitary city. The institute hosts an interdisciplinary community with access to several brain imaging facilities, such as one research-dedicated siemens scanner, one MEG, one TEP, EEG, TMS and other useful resources. Candidates, preferably with model-based fMRI experience, should send their CV, statement of research interests, and representative publications to Jean-Claude Dreher ( https://dreherteam.wixsite.com/neuroeconomics), Email: dreher at isc.cnrs.fr -- Dr Jean-Claude Dreher, Research director, CNRS UMR 5229 Neuroeconomics group, Reward and decision making Institut des Sciences Cognitives Marc Jeannerod, 67 Bd Pinel, 69675 Bron, France tel: 00 334 37 91 12 38 fax: 00 334 37 91 12 10 https://dreherteam.wixsite.com/neuroeconomicshttp://cnc.isc.cnrs.fr/en/research/neuroeco-en/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From michelic72 at gmail.com Tue Oct 2 11:19:58 2018 From: michelic72 at gmail.com (Cristiano Micheli) Date: Tue, 2 Oct 2018 11:19:58 +0200 Subject: [FieldTrip] please circulate - Postdoc Lyon Message-ID: Dear FieldTrip friends, please circulate this post from Lyon. Cris Micheli ==================== The Institute of Cognitive Science, Lyon, France is inviting applications for a postdoctoral position to study social decision making and reward processing using fMRI in healthy subjects and intracranial recordings in patients with epilepsy. The institute of Cognitive Science is located in Lyon, a thriving universitary city. The institute hosts an interdisciplinary community with access to several brain imaging facilities, such as one research-dedicated siemens scanner, one MEG, one TEP, EEG, TMS and other useful resources. Candidates, preferably with model-based fMRI experience, should send their CV, statement of research interests, and representative publications to Jean-Claude Dreher ( https://dreherteam.wixsite.com/neuroeconomics), Email: dreher at isc.cnrs.fr -- Dr Jean-Claude Dreher, Research director, CNRS UMR 5229 Neuroeconomics group, Reward and decision making Institut des Sciences Cognitives Marc Jeannerod, 67 Bd Pinel, 69675 Bron, France tel: 00 334 37 91 12 38 fax: 00 334 37 91 12 10 https://dreherteam.wixsite.com/neuroeconomicshttp://cnc.isc.cnrs.fr/en/research/neuroeco-en/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From junseok.kim at mail.utoronto.ca Tue Oct 2 19:31:40 2018 From: junseok.kim at mail.utoronto.ca (Junseok Kim) Date: Tue, 2 Oct 2018 17:31:40 +0000 Subject: [FieldTrip] Issue using ft_sourceanalysis Message-ID: Hello, I have just recently updated to the latest version of FieldTrip and I ran into a probelm I have never ran into before while using ft_sourceanalysis Reference to non-existent field 'dimord'. Error in ft_sourceanalysis (line 788) if strcmp(data.dimord, 'chan_time') in which the input was cfg = []; cfg.method = 'lcmv'; cfg.grid = source_model; cfg.headmodel = vol; cfg.lcmv.keepfilter = 'yes'; cfg.grad = dataica.grad; lcmvsource = ft_sourceanalysis(cfg, timelock); and the timelock analysis was performed by cfg = []; cfg.channel = 'meggrad'; cfg.covariance = 'yes'; cfg.covariancewindow = 'all'; cfg.vartrllength = 2; cfg.keeptrials = 'yes'; timelock = ft_timelockanalysis(cfg, dataica); This was performed on MATLAB 2015b on Neuromag resting state data divided into 10s epochs If anyone has run into this issue or has a solution for it, let me know. Cheers Andrew -------------- next part -------------- An HTML attachment was scrubbed... URL: From ajesse at psych.umass.edu Wed Oct 3 01:05:31 2018 From: ajesse at psych.umass.edu (Alexandra Jesse) Date: Tue, 2 Oct 2018 19:05:31 -0400 Subject: [FieldTrip] Captrak Message-ID: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> hi! I’m thinking about buying the Brain Vision’s Captrak system. Brain Vision recommended to buy BESA to analyze the data but I’d rather stick with Fieldtrip. Can that data easily be read into Fieldtrip? Thank you, Alexandra Sent from my iPhone From alik.widge at gmail.com Wed Oct 3 14:17:40 2018 From: alik.widge at gmail.com (Alik Widge) Date: Wed, 3 Oct 2018 07:17:40 -0500 Subject: [FieldTrip] Captrak In-Reply-To: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> References: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> Message-ID: We analyze data from BrainVision in MNE-Python, and if it fits into that cross-platform format, I am confident it can be used in FieldTrip also. The CapTrak is a pretty useful system. Alik Widge alik.widge at gmail.com (206) 866-5435 On Tue, Oct 2, 2018 at 6:24 PM Alexandra Jesse wrote: > hi! > I’m thinking about buying the Brain Vision’s Captrak system. Brain Vision > recommended to buy BESA to analyze the data but I’d rather stick with > Fieldtrip. Can that data easily be read into Fieldtrip? > Thank you, > Alexandra > > Sent from my iPhone > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From zangyl1983 at gmail.com Thu Oct 4 05:41:19 2018 From: zangyl1983 at gmail.com (Yunliang Zang) Date: Thu, 04 Oct 2018 12:41:19 +0900 Subject: [FieldTrip] Problem in running tutorial of preprocessing and analysis of spike data Message-ID: Hello, I am Yunliang Zang, a postdoc at OIST. I just began using fieldtrip. I met a problem when running the tutorial from the link: http://www.fieldtriptoolbox.org/tutorial/spike When reading event information by event = ft_read_event('p029_sort_final_01.nex'), there are 361626 events instead of 37689 events as shown in the tutorial. I checked further and found in the outputted structure called event, there are a lot of empty elements for the field value, and their type are Event002 instead of Strobed. I think ft_read_event does not function correctly in my case. This function is too long for me to check further. My Matlab version is MATLAB_R2018a. Does anybody have a similar problem and know how to fix it? Best, Yunliang -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Thu Oct 4 21:10:14 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Thu, 4 Oct 2018 12:10:14 -0700 Subject: [FieldTrip] ft_prepare_mesh ERROR In-Reply-To: References: <637223E2-7994-40FF-9D5A-A5C24E5B9374@neuro.med.kyushu-u.ac.jp> Message-ID: Just in case anyone experiences the same or a related issue in the future, Toshiki and I solved the problem for him, adjusting ft_prepare_mesh_cortexhull in the main repository . Simply re-download/pull if you had the same issue. Arjen On Fri, Sep 28, 2018 at 10:50 PM Arjen Stolk wrote: > Dear Toshiki, > > From that error, it seems your version of Freesurfer is referencing > libraries that do not exist while executing the *mris_fill *command. To > rule out that the issue is due to the call to *mris_fill* being made from > within the matlab environment as *prepare_mesh_cortexhull* does, you > could try to call *mris_fill* directly from within a terminal and see if > it replicates, as follows: > > export FREESURFER_HOME=/Applications/freesurfer > > source $FREESURFER_HOME/SetUpFreeSurfer.sh > > mris_fill -c -r 1 path_to_freesurfer/surf/lh.pial tmp.filled.mgz > > > If that reproduces the error, it might be worth consulting the > Freesurfer development team concerning this. A similar issue was reported > here > , > but it doesn't look like a fix has been suggested. Alternatively, you > could try a different version of Freesurfer for creating the cortical hull > (freesurfer-Darwin-lion-stable-pub-v5.3.0 runs fine on my mac). Hope > this helps, > Arjen > > > On Sep 25, 2018, at 12:13 AM, 岡留敏樹 > wrote: > > Hello fieldtrip experts, > > when trying to run ‘ft_prepare_mesh’ command from fieldtrip with MATLAB R2018a, I run into the error below. > > I was preprocessing MRI date according with the paper ’Nature Protocol 2018; 13: 1699-1723. http://doi.org/10.1038/s41596-018-0009-6 ‘. > > I’m running this on a iMac, macOS High Sierra 10.13.6, with fieldtrip-20180909, freesurfer-Darwin-OSX-stable-pub-v6.0.0-2beb96c. > > From past archives, I think this is because of SIP. So I disabled SIP of OSX, but I run into same errors. > I tried different subjects, which caused similar errors. > > Any ideas would be greatly appreciated, thank you! > > > [CODE] > > cfg = []; > > cfg.method = ‘cortexhull’; > > cfg.headshape = ; > > cfg.fshome = ; > > hull_lh = ft_prepare_mesh (cfg); > > > [ERROR] > > dyld: lazy symbol binding failed: Symbol not found: ___emutls_get_address > > Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib > Expected in: /usr/lib/libSystem.B.dylib > > dyld: Symbol not found: ___emutls_get_address > Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib > Expected in: /usr/lib/libSystem.B.dylib > > mris_fill -c -r 1 /Users/okadometoshiki/Desktop/SubjectUCI29/freesurfer/surf/lh.pial /private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz: Aborted > reading filled volume... > gunzip: can't stat: /private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz (/private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz.gz): No such file or directory > ERROR: problem reading fname > > > > ======== > > Toshiki Okadome > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From seunggoo.kim at duke.edu Thu Oct 4 22:44:03 2018 From: seunggoo.kim at duke.edu (Seung Goo Kim, Ph.D.) Date: Thu, 4 Oct 2018 20:44:03 +0000 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test Message-ID: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Dear FT-ers, I (also) want to know how we could perform one-sample T-test using the cluster-based permutation test to compute and correct p-values. I noticed that many people asked about this for a long time (https://mailman.science.ru.nl/pipermail/fieldtrip/2018-August/012314.html). I think it's because the one-sample T-test after baseline correction has been really a common way to analyze ERP/F data. Furthermore, in many functional studies (including fMRI studies), the first question at the second-level is if the effect of a certain contrast is non-zero over multiple subjects, thus one-sample T-test is a really natural thing to do. It is possible to compare baseline-vs-activation trials at the first level, although it could be a problem if the baseline period is too short compared to the activation period, which is not really uncommon (for example, it would be rather common to have short inter-stimulus-interval than the stimulus length itself). But at the second level, I have no idea how we could do that. I know that a permutation distribution can be generated by randomly flipping signs for one-sample T-test in permutation test, so I added another resampling method to flip signs of the half of trials in resampledesign() but the result was quite different from the two-sample t-test comparing baseline vs activation trials. Could be there any specific reason for flipping signs would not work for cluster-based correction? One possible reason I could think of would be that the noise distribution is not actually symmetric, thus permuting labels and flipping signs create different permutation distributions. Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? Best, -- Seung-Goo ("SG") Kim, PhD Postdoctoral Research Associate O-Lab, Department of Psychology & Neuroscience, Duke University Postal: 308 Research Drive, Durham, NC 27708, USA Email: solleo at gmail.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Fri Oct 5 10:07:18 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Fri, 5 Oct 2018 10:07:18 +0200 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test In-Reply-To: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> References: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Message-ID: Dear SG, Rather than go into the whole one-sample T test business again, I will respond to one aspect of your question which I think might be useful. > but the result was quite different from the two-sample t-test comparing baseline vs activation trials. I don't know exactly how you were doing the baseline vs activation test, but I'll note two things here. First, you will typically not want to do a *two-sample* test, but a *paired-sample* test; i.e. each unit of observation (trial or subject) has both a baseline and an activation period, and it's the paired difference that matters. Second, you will want to test the activation period against the *mean* across the entire baseline period (since we typically assume a stationary baseline). If you were to simply do (1) select baseline window as condition A; (2) select activation window as condition B; (3) compare the two using cluster stats; then the statistic would be comparing each time point for the activation period against the matching time point in the baseline. So basically, one way of doing activation versus baseline cluster stats is to average the baseline window across time (probably repmat() the mean over time again) and then use paired statistics against the activation window. This should work at first or second level. Hope that helps! Best, Eelke > > Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? > > Best, > -- > Seung-Goo ("SG") Kim, PhD > > Postdoctoral Research Associate > O-Lab, Department of Psychology & Neuroscience, Duke University > Postal: 308 Research Drive, Durham, NC 27708, USA > Email: solleo at gmail.com > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From seunggoo.kim at duke.edu Fri Oct 5 19:58:39 2018 From: seunggoo.kim at duke.edu (Seung Goo Kim, Ph.D.) Date: Fri, 5 Oct 2018 17:58:39 +0000 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test In-Reply-To: References: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Message-ID: <562AE551-038E-4BA8-9ED0-6BA7124F9115@duke.edu> Dear Eelke, Thank you for an informative response. I just wonder how the discussion around the one-sample T-test finally has ended and why it was decided not to be included. I think you're right that it should be tested with the paired T-test. I thought the autocorrelation in MEG data should be minimal, I think it would make more sense to pair them, especially the signal was low-pass filtered. I think if you create a flat timeseries with the mean values of each baseline period as a "baseline trial" and do a paired t-test, the result should be identical to flipping signs of baseline-corrected activation trials. During the permutation, if the labels are not swapped, then the paired difference at one trial-pair would be y - x0 (y is a timeseries in an activation period; x0 is the mean of the baseline period). If the labels are swapped, then the difference would be x0 - y. Since baseline-corrected activation trial is also y - x0, this is equivalent to flipping signs of the corrected activation trials. But I'm not sure if I really want to use only the mean of the baseline period because then it would discard underlying noise structure in the baseline period. But also because the baseline period is not evoked by any stimuli of interest, we don't assume any sample-by-sample correspondence between the baseline and activation periods. So it feels a bit strange to do so too. Best, -SG On 2018-10-05, at 04:07, Eelke Spaak > wrote: Dear SG, Rather than go into the whole one-sample T test business again, I will respond to one aspect of your question which I think might be useful. but the result was quite different from the two-sample t-test comparing baseline vs activation trials. I don't know exactly how you were doing the baseline vs activation test, but I'll note two things here. First, you will typically not want to do a *two-sample* test, but a *paired-sample* test; i.e. each unit of observation (trial or subject) has both a baseline and an activation period, and it's the paired difference that matters. Second, you will want to test the activation period against the *mean* across the entire baseline period (since we typically assume a stationary baseline). If you were to simply do (1) select baseline window as condition A; (2) select activation window as condition B; (3) compare the two using cluster stats; then the statistic would be comparing each time point for the activation period against the matching time point in the baseline. So basically, one way of doing activation versus baseline cluster stats is to average the baseline window across time (probably repmat() the mean over time again) and then use paired statistics against the activation window. This should work at first or second level. Hope that helps! Best, Eelke Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? Best, -- Seung-Goo ("SG") Kim, PhD Postdoctoral Research Associate O-Lab, Department of Psychology & Neuroscience, Duke University Postal: 308 Research Drive, Durham, NC 27708, USA Email: solleo at gmail.com _______________________________________________ fieldtrip mailing list https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e= https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e= _______________________________________________ fieldtrip mailing list https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e= https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e= -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Sat Oct 6 01:25:37 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Fri, 5 Oct 2018 16:25:37 -0700 Subject: [FieldTrip] Hiring for Postdoc (and RA) - Intracranial research Message-ID: The newly opened Cognitive Neurophysiology Lab at UC Davis headed by Dr. Saez is hiring a postdoctoral researcher. Lab research projects will focus on the neurobiological basis of human decision-making behavior, using a combination of intracranial recordings in human patients, neuroeconomic tasks and computational modeling of behavior. Candidates must have a strong background in *in vivo* electrophysiological recordings and/or decision neuroscience. For more information, including details on how to apply, please visit their lab webpage . -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Sun Oct 7 13:52:18 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 7 Oct 2018 07:52:18 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster Message-ID: Dear FieldTrip community, sorry for sending another email out, but I am still having trouble with this issue. I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Mon Oct 8 09:04:37 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 8 Oct 2018 07:04:37 +0000 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster In-Reply-To: References: Message-ID: <7E583DEC-CBEE-4554-9FBC-E0847816C100@donders.ru.nl> Hi Paul, Currently, it’s not possible to use ft_freqstatistics for statistical inference of coherence in an all-by-all channel pair fashion. The reason is that clustering in 4D space is not implemented. What is possible, though, is to compare coherence across conditions between all channels, and a separate reference channel (e.g. EMG). This will probably require some tweaks to the input data (e.g. replace ‘labelcmb’, with ‘label’, and replace ‘dimord’ with ‘chan_freq’). Using one of the EEG channels as a ‘reference’ should be in principle possible as well (at least in terms of algorithm, whether interpretations based on inferential decisions make sense in this case, that’s up to the user). Best wishes, Jan-Mathijs On 7 Oct 2018, at 13:52, Paul Dhami > wrote: Dear FieldTrip community, sorry for sending another email out, but I am still having trouble with this issue. I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 8 20:49:46 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Mon, 8 Oct 2018 14:49:46 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster (Schoffelen, J.M. (Jan Mathijs)) Message-ID: Hi Jan-Mathijs, thank you very much for your response, it's greatly appreciated. If I approached it from an a priori perspective, and wanted test the coherency difference between two groups for just a pair of electrodes, where one is the reference electrode (e.g. comparing the coherency values of F3 - F4 between two groups), would this be possible? My understanding is that it would become just as any other 3D matrix, with freq-time-subjects becoming the dimensions after squeezing the pair dimension. Would this then be something possible to run freqstatistics on with cluster analysis? Thank you again, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From sauppe.s at gmail.com Tue Oct 9 11:49:56 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Tue, 9 Oct 2018 11:49:56 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? Message-ID: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Dear list members, there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? Thanks a lot! — Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From S.Arana at donders.ru.nl Tue Oct 9 12:55:01 2018 From: S.Arana at donders.ru.nl (Arana, S.L. (Sophie)) Date: Tue, 9 Oct 2018 10:55:01 +0000 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: <1539082501488.92451@donders.ru.nl> Hi Sebastian, option 'normchange' will give you the normalized change to baseline (data-meanbsl)/(data+meanbsl) You can see the formulas for the different options at the end of the ft_freqbaseline code. I am not sure in which situation you would prefer this option over 'relchange', maybe someone else knows? Best, Sophie ________________________________ From: fieldtrip on behalf of Sebastian Sauppe Sent: Tuesday, October 9, 2018 11:49 AM To: fieldtrip at science.ru.nl Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? Dear list members, there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like "absolute" or "db". However, I wasn't able to find out what "normchange" does. Does anyone of you know (where to find information about this)? Thanks a lot! - Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Tue Oct 9 13:12:35 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Tue, 9 Oct 2018 13:12:35 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: Dear Sebastian, 'normchange' will compute (a-b) / (a+b). The best reference on what the options do is of course the source code itself; see here: https://github.com/fieldtrip/fieldtrip/blob/ce5bdd3d6433dbc4ca600eb127f369f3917c02cd/ft_freqbaseline.m#L205 Cheers, Eelke On Tue, 9 Oct 2018 at 11:49, Sebastian Sauppe wrote: > > Dear list members, > > there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? > > Thanks a lot! > — Sebastian > > ----------- > Dr. Sebastian Sauppe > Department of Comparative Linguistics, University of Zurich > Homepage: https://sites.google.com/site/sauppes/ > Twitter: @SebastianSauppe > Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ > ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe > ORCID ID: http://orcid.org/0000-0001-8670-8197 > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From david.schubring at uni-konstanz.de Tue Oct 9 13:27:15 2018 From: david.schubring at uni-konstanz.de (David Schubring) Date: Tue, 9 Oct 2018 13:27:15 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> Dear Sebastian, more information can be found in the code of ft_freqbaseline, where "data" is your poststimulus and "meanVals" is your baseline and normchange does the following: data = (data - meanVals) ./ (data + meanVals) Hope that helps. Best, David PS: The other baseline types do the following: if (strcmp(baselinetype, 'absolute')) data = data - meanVals; elseif (strcmp(baselinetype, 'relative')) data = data ./ meanVals; elseif (strcmp(baselinetype, 'relchange')) data = (data - meanVals) ./ meanVals; elseif (strcmp(baselinetype, 'normchange')) || (strcmp(baselinetype, 'vssum')) data = (data - meanVals) ./ (data + meanVals); elseif (strcmp(baselinetype, 'db')) data = 10*log10(data ./ meanVals); Am 09.10.2018 um 11:49 schrieb Sebastian Sauppe: > Dear list members, > > there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? > > Thanks a lot! > — Sebastian > > ----------- > Dr. Sebastian Sauppe > Department of Comparative Linguistics, University of Zurich > Homepage: https://sites.google.com/site/sauppes/ > Twitter: @SebastianSauppe > Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ > ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe > ORCID ID: http://orcid.org/0000-0001-8670-8197 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From mjones at sanantoniocounseling.net Wed Oct 10 04:54:46 2018 From: mjones at sanantoniocounseling.net (Mark Jones) Date: Tue, 9 Oct 2018 21:54:46 -0500 Subject: [FieldTrip] Moving means Message-ID: <5152EC6D-5544-4406-BC6B-336B3729B983@sanantoniocounseling.net> I have a time frequency series of continuous EEG, segmented in 10 second trials (which gives me 0.1hz resolution). How can I create moving means of the data based on a rolling average of 10 seconds that will update once a second? Thanks, Mark Jones San Antonio, Texas, USA From sauppe.s at gmail.com Wed Oct 10 09:47:38 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Wed, 10 Oct 2018 09:47:38 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> Message-ID: <2BAC407B-5208-49E3-A4F5-A0DA0D7E326B@gmail.com> Dear David, thanks a lot, that clarifies what the different methods do. — Sebastian > Am 09.10.2018 um 13:27 schrieb David Schubring : > > Dear Sebastian, > > more information can be found in the code of ft_freqbaseline, where "data" is your poststimulus and "meanVals" is your baseline and normchange does the following: > > data = (data - meanVals) ./ (data + meanVals) > > Hope that helps. > > Best, > David > > PS: The other baseline types do the following: > > if (strcmp(baselinetype, 'absolute')) > data = data - meanVals; > elseif (strcmp(baselinetype, 'relative')) > data = data ./ meanVals; > elseif (strcmp(baselinetype, 'relchange')) > data = (data - meanVals) ./ meanVals; > elseif (strcmp(baselinetype, 'normchange')) || (strcmp(baselinetype, 'vssum')) > data = (data - meanVals) ./ (data + meanVals); > elseif (strcmp(baselinetype, 'db')) > data = 10*log10(data ./ meanVals); > > > Am 09.10.2018 um 11:49 schrieb Sebastian Sauppe: >> Dear list members, >> there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? >> Thanks a lot! >> — Sebastian >> ----------- >> Dr. Sebastian Sauppe >> Department of Comparative Linguistics, University of Zurich >> Homepage: https://sites.google.com/site/sauppes/ >> Twitter: @SebastianSauppe >> Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ >> ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe >> ORCID ID: http://orcid.org/0000-0001-8670-8197 >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > -- > Dr. David Schubring > > General & Biological Psychology > University of Konstanz > Room C524 > P.O. Box 36 > 78457 Konstanz > > Phone: +49-(0)7531-88-5350 > Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From sauppe.s at gmail.com Wed Oct 10 10:21:50 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Wed, 10 Oct 2018 10:21:50 +0200 Subject: [FieldTrip] Question on baselining methods (absolute vs db) Message-ID: Dear FieldTrip list members, I’ve got a question on the different methods available in ft_freqbaseline. I have TFA data from an experiment where participants saw pictures and reacted to them. There is a pre-trial period (-1000 to 0 ms) during which the fixate a fixation cross and a trial period (0 to 1200 ms) during which they see the picture. I used mtmconvol in ft_freqstatistics to get the power. The frequencies are 4 to 20 Hz and I used 3 cycles (cfg.t_ftimwin = 3./cfg.foi). Then I want to baseline the data for plotting and analysis. As baseline period I chose -500 to -200 ms. (But using other baseline periods don’t change the picture.) When I use an absolute baseline, the activity during the baseline period is around 0 and during the trial there are increases and decreases (i.e. positive and negative values). So this looks like one would expect. When I use a dB baseline, however, the activity during the baseline period is not around 0, but rather around -1 to -2 dB. During the trial, I also get values around -1 to -3 dB. This is the command: cfg = []; cfg.baseline = [-0.5 -0.2]; % baseline period = -500 to -200 ms, relative to stimulus onset cfg.baselinetype = 'db'; cfg.parameter = 'powspctrm'; freq_data_baselined = ft_freqbaseline(cfg, freq_data); What could be the reason for this discrepancy between absolute and dB baselines? Thanks a lot for your help already in advance! Regards, Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From wwumedstanford at gmail.com Wed Oct 10 20:23:38 2018 From: wwumedstanford at gmail.com (Wei Wu) Date: Wed, 10 Oct 2018 11:23:38 -0700 Subject: [FieldTrip] Assistant, associate or full professor positions @ Department of Psychiatry and Behavioral Sciences, Stanford University Message-ID: The Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine is seeking new full-time faculty members at the rank of Assistant, Associate, or full Professor in the Medical Center Line. These are positions for mental health clinician scientists who will be based in the Department of Psychiatry and Behavioral Sciences, or at the Veterans Affairs Palo Alto Health Care System with presence in the Departmental programs on the Stanford campus. The chosen candidates will be expected to conduct scholarly research and teaching in the areas of psychiatry or psychology in adult and/or child and adolescent populations across several areas of programmatic need, including mood disorders, anxiety disorders, eating disorders, addiction medicine, public mental health and/or special patient populations, among others. Candidates are required to have a proven track record of success in producing high-quality scholarly work, in addition to excellence in teaching and established clinical expertise in their respective field. Possible candidates could include, but are not limited to, psychiatrists, psychologists, neuropsychiatrists, and neuropsychologists. Physician applicants must have a medical degree or equivalent degree, completed training in General Psychiatry, be board-certified in General Psychiatry, and possess or be fully eligible for a California medical license. Physicians interested in working with children must hold board certification in child psychiatry. Clinical psychologist applicants must have a doctoral degree in psychology or equivalent degree, have completed an APA-approved internship, and possess or be fully eligible for licensure as a psychologist in California. The major criteria for appointment for faculty in the Medical Center Line shall be excellence in the overall mix of clinical care, clinical teaching, scholarly activity that advances clinical medicine, and institutional service appropriate to the programmatic need the individual is expected to fulfill. Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford also welcomes applications from others who would bring additional dimensions to the University’s research, teaching and clinical missions. Interested candidates should send a copy of their curriculum vitae, a brief letter outlining their experiences and interests and the names of three references via e-mail only to: Search Co-Chairs: Natalie Rasgon, MD, Ph.D., and Alan Louie, M.D. c/o Heather Kenna Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine 401 Quarry Road Stanford, CA 94305 Phone: (650) 724-0521 Email: hkenna at stanford.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From fda.bolanos at gmail.com Thu Oct 11 20:19:47 2018 From: fda.bolanos at gmail.com (=?UTF-8?Q?Fernanda_Bola=C3=B1os?=) Date: Thu, 11 Oct 2018 13:19:47 -0500 Subject: [FieldTrip] Problems running FieldTrip Message-ID: Hi! My name is Fernanda and I am using FieldTrip for a school project. I download the most recent version, but I’m having problems to run the code. It happens with each and every code I make. I followed the instructions on how to install the toolbox. I placed the fieldtrip carpet at my (C:) folder and I added the startup.m at the C:\Program Files\MATLAB\R2017b\toolbox\local. May have some help on how to solve the problem, please? Best regards, Fernanda Bolaños -- Ma. Fernanda Bolaños De Regil -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Fri Oct 12 09:29:00 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Fri, 12 Oct 2018 09:29:00 +0200 Subject: [FieldTrip] Problems running FieldTrip In-Reply-To: References: Message-ID: Hi Fernanda, What sort of errors are you getting? In general, make sure you add the FieldTrip folder to your Matlab path and run ft_defaults() in order to check whether everything is OK. Cheers, Eelke On Thu, 11 Oct 2018 at 20:19, Fernanda Bolaños wrote: > Hi! My name is Fernanda and I am using FieldTrip for a school project. > > > > I download the most recent version, but I’m having problems to run the > code. It happens with each and every code I make. I followed the > instructions on how to install the toolbox. > > > > I placed the fieldtrip carpet at my (C:) folder and I added the startup.m > at the C:\Program Files\MATLAB\R2017b\toolbox\local. > > > > May have some help on how to solve the problem, please? > > > > Best regards, > > > > Fernanda Bolaños > > -- > Ma. Fernanda Bolaños De Regil > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Alexander_Nakhnikian at hms.harvard.edu Fri Oct 12 18:34:19 2018 From: Alexander_Nakhnikian at hms.harvard.edu (Nakhnikian, Alexander) Date: Fri, 12 Oct 2018 16:34:19 +0000 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling Message-ID: Dear All, Apologies for the long post, I've tried to be as succinct as possible while describing my problem is sufficient detail. I've been trying to solve a problem with source modeling for sometime. I've found that distributed source estimates (MNE, sLORETA, eLORETA) are heavily biased towards the ventral temporal lobe. This is the case with multiple data sets analyzed using both built-in field trip functions and imaging kernels generated by my own code. It occurs in within group grand averages and statistical contrasts between controls and patients. I've confirmed that sLORETA and eLORETA are unbiased for noiseless data by filtering point sources through the resolution kernel. I'm working on a Mac running OS 10.11.6 and the latest version of Field Trip. I'm using a standard 10/10 electrode layout (no individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) and Field Trip's template BEM. The forward model is restricted to the cortical mantle (I've had similar problems with whole brain forward models as well). I recently ran PCA at the source level to explore the issue. The data were collecting during quiet rest and bandpassed to isolate a peak that accounts for a significant difference between controls and patients at the sensor level. The imaging kernel was applied to the sensor spectral matrix. To obtain the PCs, I analyzed the covariance of power among voxels. The rank of the voxel covariance matrix was 31 with the first 3 eigenvalues account for approximately 95% of the variance. Interestingly, the spatial distribution of the first three principal components exhibited the same bias as the sensor data. When I reconstructed the source data omitting the first 3 components, the control-patient contrast localized to a collection of canonical DMN nodes. It seems extremely odd to me that removing so much of the information present in the original data returns a reasonable source-level contrast while the majority of variance is accounted for by what is clearly bias. I cannot complete this analysis and submit the results unless I can isolate the problem and correct it so I can run the analysis without reducing the rank of the source data. If anyone can speculate on possible reasons for this problem and/or potential solutions I would be grateful. Thank you, Alexander Alexander Nakhnikian, Ph.D. Research Investigator VA Boston Healthcare System Instructor in Psychiatry, Harvard Medical School -------------- next part -------------- An HTML attachment was scrubbed... URL: From kai.hwang at gmail.com Sat Oct 13 05:29:51 2018 From: kai.hwang at gmail.com (Kai Hwang) Date: Fri, 12 Oct 2018 22:29:51 -0500 Subject: [FieldTrip] postdoc position | cognitive neuroscience | University of Iowa Message-ID: The Hwang lab in the Psychological and Brain Sciences Department at the University of Iowa has a fully funded postdoc position available. The Hwang lab focuses on brain network mechanisms, cognitive control, and their developmental processes with a strong emphasis on the human thalamocortical system and neural oscillations. Our research utilizes multimodal neuroimaging (EEG and fMRI), TMS, and lesion studies in combination with network neuroscience approaches. For more info please see: https://kaihwang.github.io/ The lab is affiliated with the DeLTA Center and the Iowa Neuroscience Institute, which offers a collaborative research environment with access to research dedicated 3T and 7T MRI systems, TMS, EEG, neurosurgery patients, and a large lesion patient registry. Postdoc Candidate Qualifications - PhD in Psychology, Neuroscience, or other related disciplines. - Experience with neuroimaging (fMRI, EEG/MEG). - Strong Programming skills (Matlab or Python) The postdoc position is opened immediately until filled. To apply, please send a cover letter and CV to: kai-hwang at uiowa.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 15 04:37:52 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 14 Oct 2018 22:37:52 -0400 Subject: [FieldTrip] Attempt at cluster analysis on seed to whole brain coherency values Message-ID: Dear Fieldtrip, I have a dataset in which I'd like to compare, between controls and patients, their imaginary coherency values in a seed to whole brain manner. In other words, I would like to calculate the imaginary coherency between a single seed electrode and the rest of the remaining electrodes, and *ultimately use ft_freqstatistics' cluster analysis* to test for a significance difference between the groups' seed-to-whole-scalp coherency maps. >From my understanding, this would require a bit of hacking to implement this. I attempted to do so and describe what I did below, with the goal to eventually create something that would work with freqstatistics : - ran freq analysis with output as 'powandcsd' and method as 'mtmconvolv' - on the results of freqanalysis, ran ft_connectivityanalysis with cfg.method = 'coh' and cfg.complex = 'absimag' - with each subject's resulting structure from ft_connectivityanalysis, I first chose a seed of interest - I then found the indices of the 59 channel combinations in relation to the seed of interest in labelcmb - Using the indices, I then pruned/removed the remaining channel combinations of no interest from both the labelcmb and cohspctrm, reducing cohspctrm to a channel of interest (59) x frequency x time matrix (as in it only included values for the channel combinations of interest) - I rename the dimord as 'chan_freq_time' - create 'label' field with standard electrode labels - I then create powspctrm in my structure, which holds the exact same data as the cohspctrm (created powspctrm strictly for freqstatistics) - removed the fields of 'labelcmb' and 'cohspctrm' - because my powspctrm is missing the seed channel, I then inserted a matrix of ones with the appropriate dimensions into where it should be (e.g. if my seed of interest was channel FCZ, I would then insert into my matrix of ones into the 19th position of the powspctrm, thus shifting the latter matrices so now it becomes a matrix with 60 channels in the appropriate order) >From my understanding, the resulting structure of each subject should contain now the coherency values between the seed of interest and the rest of the electrodes in 'powspctrm'. I then used freq_statistics (in the standard way) with cluster correction to compare the coherency between groups, and from what I can tell with no errors popping up, it worked. I then interpreted the resulting clusters in a similar fashion as you would do for a typical frequency chan-freq-time analysis (instead of power, looking at coherency clusters now). My questions are: 1) In regards to implementation (assuming something like this can even be appropriately implemented), do things look okay? 2) Am I wrong in thinking that the cluster results of freq_statistics can be interpreted in a similar fashion as to a typical frequency chan-freq-time analsyis (just replacing power with coherency)? Sorry for the long-winded email, but any help would be greatly appreciated. Thank you, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From virginie.van.wassenhove at gmail.com Mon Oct 15 10:24:01 2018 From: virginie.van.wassenhove at gmail.com (Virginie van Wassenhove) Date: Mon, 15 Oct 2018 10:24:01 +0200 Subject: [FieldTrip] Fwd: [masters + postdoc fellowships] In-Reply-To: References: Message-ID: Dear colleagues, please, feel free to advertise for those interested in temporal cognition inside and outside the lab! Internships for masters: https://brainthemind.files.wordpress.com/2018/10/wildtimes_masters_add.pdf Postdoctoral fellowship: https://brainthemind.files.wordpress.com/2018/10/wildtimes_postdoc_add.pdf Best wishes, Virginie van Wassenhove *https://brainthemind.com/ * -------------- next part -------------- An HTML attachment was scrubbed... URL: From alexandra.korzeczek at med.uni-goettingen.de Mon Oct 15 12:55:43 2018 From: alexandra.korzeczek at med.uni-goettingen.de (Korzeczek, Alexandra) Date: Mon, 15 Oct 2018 10:55:43 +0000 Subject: [FieldTrip] ft_artifact_threshold not operating properly after resampling data Message-ID: Dear all, does anyone know if ft_resampledata is affecting EEG-data in an adverse manner? I’m asking because I’m having a similar problem wich was already described by Alexandrina Guran on Mon Mar 27 11:34:19 CEST 2017 (subject: Problem with downsampling / automatic artifact rejection) and wanted to ask if a solution has been found why this problem appears? The problem (black and bold) occurs while using the function ft_artifact_threshold: Example: Warning: the trial definition in the configuration is inconsistent with the actual data Warning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording threshold artifact scanning: trial 1 from 74 exceeds min-threshold threshold artifact scanning: trial 2 from 74 is ok threshold artifact scanning: trial 3 from 74 is ok threshold artifact scanning: trial 4 from 74 is ok threshold artifact scanning: trial 5 from 74 exceeds min-threshold threshold artifact scanning: trial 6 from 74 exceeds max-threshold Warning: data contains NaNs, no filtering or preprocessing applied I have backtracked why theses NANs occour, and it happens when ft_artifact_threshold (line 160) calls the function ft_fetch data. Within this function (line 167) the Matlab function unique is called. The Warning is posted when the variable utrl stays empty (line 169). Then in line 178 the data (variable dat), which until then are NANs, in the matrix data.trial are not converted into proper values. 167 - utrl = unique(trialnum); 168 - utrl(~isfinite(utrl)) = 0; 169 - utrl(utrl==0) = []; 170 - if length(utrl)==1 171 - ok = trialnum==utrl; 172 - smps = samplenum(ok); 173 - dat(:,ok) = data.trial{utrl}(chanindx,smps); 174 - else 175 - for xlop=1:length(utrl) 176 - ok = trialnum==utrl(xlop); 177 - smps = samplenum(ok); 178 - dat(:,ok) = data.trial{utrl(xlop)}(chanindx,smps); 179 - end 180 - end This problem is independent of my cfg configurations for ft_artifact threshold. However, as Alexandrina found out, it does not appear when I leave out my previous ft_resampledata step. So my question is: Can anybody explain, why resampling my data is giving this effect? And additionally if it is save to use resampling anyway (other ft functions as databrowser or rejectvisual do not seem to be affected by this resampling step – but maybe I just don’t notice that?). I’m using the EGI system with a 256 EEG cap. Here are my current steps before I call ft_artifact_threshold: Definetrial: cfg = []; cfg.dataformat = 'egi_mff_v2'; %uses the new format for egi files cfg.headerformat = 'egi_mff_v2'; cfg.eventformat = 'egi_mff_v2'; cfg.continuous = 'yes'; cfg.trialfun = 'ft_trialfun_general'; cfg.trialdef.eventtype = DIN4; cfg.trialdef.eventvalue = ''; % necessary for mff files. cfg.trialdef.prestim = 1; cfg.trialdef.poststim = 2,5; EEG_trldef_S1= ft_definetrial(cfg) 2x times Selectdata: cfg.trials = keep_trlinf;% specific rules for my experiment EEG_trls= ft_selectdata(cfg, EEG_trldef_S1); Preprocessing: EEG_trls.padding = Pad; EEG_trls.chantype = {'eeg'}; EEG_trls.channel=(1:257); EEG_trls.detrend = 'yes'; EEG_trls.trials = 'all'; EEG_trls.lpfilter ='yes'; EEG_trls.lpfreq = 60; EEG_preproc_1000Hz = ft_preprocessing (EEG_trls); Resample data cfg.resamplefs = 500; EEG_preproc = ft_resampledata(cfg, EEG_preproc_1000Hz); The difference between the data output of ft_preprocessing and ft_resampledata is that the variable sampleinfo is missing after ft_resampledata (see attached picture). Maybe this is the reason why ft_artifact_threshold is not working properly? I hope someone has an explanation? Thank you in advance, Alexandra _________________________________________________________________ Alexandra Korzeczek Wissenschaftliche Mitarbeiterin Klinik für Klinische Neurophysiologie Georg-August-Universität Göttingen Robert-Koch.Str. 40, 37075 Göttingen Tel. 0551- 39-65106 -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Matrix_ft_resampledata.png Type: image/png Size: 22979 bytes Desc: Matrix_ft_resampledata.png URL: From e.spaak at donders.ru.nl Mon Oct 15 13:22:13 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Mon, 15 Oct 2018 13:22:13 +0200 Subject: [FieldTrip] Attempt at cluster analysis on seed to whole brain coherency values In-Reply-To: References: Message-ID: Dear Paul, That all sounds good to me. Note that you could also have specified cfg.parameter = 'cohspctrm' in the call to ft_freqanalysis, so the renaming step to powspctrm was not strictly necessary. One thing you'll want to keep in mind when doing this is that imaginary part of coherency (ImC) is a biased quantity and depends on the number of trials. So comparing two groups statistically will not give you meaningful results if the number of data points for the two groups is unequal. It's worth having a look at this paper which goes into more details on this and related issues: Vinck, Oostenveld, Van Wingerden, Battaglia, & Pennartz. An Improved Index of Phase-Synchronization for Electrophysiological Data in the Presence of Volume-Conduction, Noise and Sample-Size Bias. NeuroImage 55, no. 4 (April 15, 2011): 1548–65. https://doi.org/10.1016/j.neuroimage.2011.01.055. Cheers, Eelke On Mon, 15 Oct 2018 at 04:37, Paul Dhami wrote: > > Dear Fieldtrip, > > I have a dataset in which I'd like to compare, between controls and patients, their imaginary coherency values in a seed to whole brain manner. In other words, I would like to calculate the imaginary coherency between a single seed electrode and the rest of the remaining electrodes, and ultimately use ft_freqstatistics' cluster analysis to test for a significance difference between the groups' seed-to-whole-scalp coherency maps. > > From my understanding, this would require a bit of hacking to implement this. I attempted to do so and describe what I did below, with the goal to eventually create something that would work with freqstatistics : > > ran freq analysis with output as 'powandcsd' and method as 'mtmconvolv' > on the results of freqanalysis, ran ft_connectivityanalysis with cfg.method = 'coh' and cfg.complex = 'absimag' > with each subject's resulting structure from ft_connectivityanalysis, I first chose a seed of interest > I then found the indices of the 59 channel combinations in relation to the seed of interest in labelcmb > Using the indices, I then pruned/removed the remaining channel combinations of no interest from both the labelcmb and cohspctrm, reducing cohspctrm to a channel of interest (59) x frequency x time matrix (as in it only included values for the channel combinations of interest) > I rename the dimord as 'chan_freq_time' > create 'label' field with standard electrode labels > I then create powspctrm in my structure, which holds the exact same data as the cohspctrm (created powspctrm strictly for freqstatistics) > removed the fields of 'labelcmb' and 'cohspctrm' > because my powspctrm is missing the seed channel, I then inserted a matrix of ones with the appropriate dimensions into where it should be (e.g. if my seed of interest was channel FCZ, I would then insert into my matrix of ones into the 19th position of the powspctrm, thus shifting the latter matrices so now it becomes a matrix with 60 channels in the appropriate order) > > From my understanding, the resulting structure of each subject should contain now the coherency values between the seed of interest and the rest of the electrodes in 'powspctrm'. > > I then used freq_statistics (in the standard way) with cluster correction to compare the coherency between groups, and from what I can tell with no errors popping up, it worked. I then interpreted the resulting clusters in a similar fashion as you would do for a typical frequency chan-freq-time analysis (instead of power, looking at coherency clusters now). > > My questions are: > 1) In regards to implementation (assuming something like this can even be appropriately implemented), do things look okay? > 2) Am I wrong in thinking that the cluster results of freq_statistics can be interpreted in a similar fashion as to a typical frequency chan-freq-time analsyis (just replacing power with coherency)? > > Sorry for the long-winded email, but any help would be greatly appreciated. > > Thank you, > Paul > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From jan.schoffelen at donders.ru.nl Tue Oct 16 09:51:22 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Tue, 16 Oct 2018 07:51:22 +0000 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling In-Reply-To: References: Message-ID: Dear Alexander, I am not sure whether I completely follow your diagnostic steps and your conclusions, but I would also check: - the alignment between the electrodes, volume conduction model, and source model. - are the electrode positions expressed in the same coordinate system as the volume conductor/source model? - are the leadfields well-behaved? For instance: if the dipole locations are too close to the innermost mesh, numerical problems may arise. Best wishes, Jan-Mathijs On 12 Oct 2018, at 18:34, Nakhnikian, Alexander > wrote: Dear All, Apologies for the long post, I've tried to be as succinct as possible while describing my problem is sufficient detail. I've been trying to solve a problem with source modeling for sometime. I've found that distributed source estimates (MNE, sLORETA, eLORETA) are heavily biased towards the ventral temporal lobe. This is the case with multiple data sets analyzed using both built-in field trip functions and imaging kernels generated by my own code. It occurs in within group grand averages and statistical contrasts between controls and patients. I've confirmed that sLORETA and eLORETA are unbiased for noiseless data by filtering point sources through the resolution kernel. I'm working on a Mac running OS 10.11.6 and the latest version of Field Trip. I'm using a standard 10/10 electrode layout (no individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) and Field Trip's template BEM. The forward model is restricted to the cortical mantle (I've had similar problems with whole brain forward models as well). I recently ran PCA at the source level to explore the issue. The data were collecting during quiet rest and bandpassed to isolate a peak that accounts for a significant difference between controls and patients at the sensor level. The imaging kernel was applied to the sensor spectral matrix. To obtain the PCs, I analyzed the covariance of power among voxels. The rank of the voxel covariance matrix was 31 with the first 3 eigenvalues account for approximately 95% of the variance. Interestingly, the spatial distribution of the first three principal components exhibited the same bias as the sensor data. When I reconstructed the source data omitting the first 3 components, the control-patient contrast localized to a collection of canonical DMN nodes. It seems extremely odd to me that removing so much of the information present in the original data returns a reasonable source-level contrast while the majority of variance is accounted for by what is clearly bias. I cannot complete this analysis and submit the results unless I can isolate the problem and correct it so I can run the analysis without reducing the rank of the source data. If anyone can speculate on possible reasons for this problem and/or potential solutions I would be grateful. Thank you, Alexander Alexander Nakhnikian, Ph.D. Research Investigator VA Boston Healthcare System Instructor in Psychiatry, Harvard Medical School _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Tue Oct 16 11:47:46 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 16 Oct 2018 11:47:46 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' Message-ID: Dear all, I have pre-processed some ERP data in BrainVision and marked bad segments instead of removing them. I now want to import this data into Fieldtrip, but how do I remove the marked segments? The following tells me that there are 10 bad segments out of 44, but when I set event type to 'Stimulus' and add the triggers at event values, it imports all 44 segments. cfg = []; cfg.dataset = '../data/export/28_preproc.dat'; cfg.trialdef.eventtype = 'Bad Interval'; ft_definetrial(cfg); Do I need to write my own conditional trialfun? If so, how? I've tried editing the tutorial example but I'm new to Matlab and am struggling. Thanks in advance, Kate -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From matti.stenroos at aalto.fi Tue Oct 16 12:16:11 2018 From: matti.stenroos at aalto.fi (Matti Stenroos) Date: Tue, 16 Oct 2018 13:16:11 +0300 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling In-Reply-To: References: Message-ID: Dear Alexander, Also I could not fully follow the pipeline. The terms "sensor spectral matrix" and "contrast" however, caught my eye. Is your "data vector" that you try to map on the brain a result of only linear processing of the measurement? Cheers, Matti On 2018-10-16 10:51, Schoffelen, J.M. (Jan Mathijs) wrote: > Dear Alexander, > > I am not sure whether I completely follow your diagnostic steps and your > conclusions, but I would also check: > > - the alignment between the electrodes, volume conduction model, and > source model. > - are the electrode positions expressed in the same coordinate system as > the volume conductor/source model? > - are the leadfields well-behaved? For instance: if the dipole locations > are too close to the innermost mesh, numerical problems may arise. > > Best wishes, > Jan-Mathijs > > >> On 12 Oct 2018, at 18:34, Nakhnikian, Alexander >> > > wrote: >> >> Dear All, >> >> Apologies for the long post, I've tried to be as succinct as possible >> while describing my problem is sufficient detail. >> >> I've been trying to solve a problem with source modeling for sometime. >> I've found that distributed source estimates (MNE, sLORETA, eLORETA) >> are heavily biased towards the ventral temporal lobe. This is the case >> with multiple data sets analyzed using both built-in field trip >> functions and imaging kernels generated by my own code. It occurs in >> within group grand averages and statistical contrasts between controls >> and patients. I've confirmed that sLORETA and eLORETA are unbiased for >> noiseless data by filtering point sources through the resolution >> kernel. I'm working on a Mac running OS 10.11.6 and the latest version >> of Field Trip. I'm using a standard 10/10 electrode layout (no >> individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) >> and Field Trip's template BEM. The forward model is restricted to the >> cortical mantle (I've had similar problems with whole brain forward >> models as well). >> >> I recently ran PCA at the source level to explore the issue. The data >> were collecting during quiet rest and bandpassed to isolate a peak >> that accounts for a significant difference between controls and >> patients at the sensor level. The imaging kernel was applied to the >> sensor spectral matrix. To obtain the PCs, I analyzed the covariance >> of power among voxels. The rank of the voxel covariance matrix was 31 >> with the first 3 eigenvalues account for approximately 95% of the >> variance. Interestingly, the spatial distribution of the first three >> principal components exhibited the same bias as the sensor data. When >> I reconstructed the source data/omitting/the first 3 components, the >> control-patient contrast localized to a collection of canonical DMN >> nodes. >> >> It seems extremely odd to me that removing so much of the information >> present in the original data returns a reasonable source-level >> contrast while the majority of variance is accounted for by what is >> clearly bias. I cannot complete this analysis and submit the results >> unless I can isolate the problem and correct it so I can run the >> analysis without reducing the rank of the source data. If anyone can >> speculate on possible reasons for this problem and/or potential >> solutions I would be grateful. >> >> Thank you, >> >> Alexander >> >> Alexander Nakhnikian, Ph.D. >> Research Investigator >> VA Boston Healthcare System >> Instructor in Psychiatry, Harvard Medical School >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > From bioeng.yoosofzadeh at gmail.com Tue Oct 16 22:12:06 2018 From: bioeng.yoosofzadeh at gmail.com (Vahab Yousofzadeh) Date: Tue, 16 Oct 2018 15:12:06 -0500 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: Hi Kate, if it is a continuous data (before epoching), you can treat bad segments as an artifact (e.g. eog or muscle, etc) and do something like this, % artifact_EOG = [100 500]; % in sample cfg = []; cfg.artfctdef.eog.artifact = artifact_EOG; cfg.artfctdef.reject = 'value'; % cfg.artfctdef.value = 0; % replacing values with nan or 0 data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is the output from ft_preprocessing. % inspecting cleaned data cfg = []; cfg.continuous = 'yes'; cfg.viewmode = 'vertical'; % all channels seperate cfg.blocksize = 5; % view the continous data in 30-s blocks ft_databrowser(cfg, data_continuous_eog_clean); if the data is epoched, simply use ft_redefinetrial. Best, Vahab From alessandro.orticoni at gmail.com Tue Oct 16 22:56:16 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Tue, 16 Oct 2018 22:56:16 +0200 Subject: [FieldTrip] ft_preprocessing filter order Message-ID: Dear all, I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. Thanks a lot, Alessandro Orticoni -------------- next part -------------- An HTML attachment was scrubbed... URL: From abela.eugenio at gmail.com Tue Oct 16 23:57:12 2018 From: abela.eugenio at gmail.com (Eugenio Abela) Date: Tue, 16 Oct 2018 22:57:12 +0100 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi Alessandro, any defaults are set in the low-level functions that ft_preprocessing calls. So, for the lowpass filter, go have a look at the help of ft_preproc_lowpassfilter.m, it says: % FT_PREPROC_LOWPASSFILTER applies a low-pass filter to the data and thereby % removes all high frequency components in the data % % Use as % [filt] = ft_preproc_lowpassfilter(dat, Fsample, Flp, N, type, dir, instabilityfix) % where % dat data matrix (Nchans X Ntime) % Fsample sampling frequency in Hz % Flp filter frequency % N optional filter order, default is 6 (but) or dependent upon % frequency band and data length (fir/firls) etc... The default for the Butterworth low-pass filter is thus 6 (for the bandpass it’s 4). You can set your preferred order when calling ft_preprocessing.m e.g. via cfg.lpfiltord = 4 or whatever makes sense. Hope that helps Eugenio On 16 Oct 2018, at 21:56, Alessandro Orticoni wrote: Dear all, I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. Thanks a lot, Alessandro Orticoni _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From dlozanosoldevilla at gmail.com Wed Oct 17 00:15:43 2018 From: dlozanosoldevilla at gmail.com (Diego Lozano-Soldevilla) Date: Wed, 17 Oct 2018 00:15:43 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi Alessandro, As explained in the help of ft_preprocessing, the default filter order can be found in the low level functions: fieldtrip/preproc/ft_preproc_bandpassfilter.m fieldtrip/preproc/ft_preproc_bandstopfilter.m fieldtrip/preproc/ft_preproc_highpassfilter.m fieldtrip/preproc/ft_preproc_lowpassfilter.m The filter order means something very different for the Butterworth (i.e. amount of samples used for the input-output recursion) https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 or for the Finite Impulse Response (FIR) filter (i.e. length of the filter kernel). https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 For the ones interested to know more about what that number does for different filters, please check this example script: http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics I hope that helps, Diego On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < alessandro.orticoni at gmail.com> wrote: > Dear all, > > I would like to ask you just a question: which is the default order of the > filters implemented by ft_preprocessing? I cannot find it anywhere. > > Thanks a lot, > Alessandro Orticoni > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.orticoni at gmail.com Wed Oct 17 00:54:24 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Wed, 17 Oct 2018 00:54:24 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi, Yes, thanks both! You have been very helpful. Best, Alessandro Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla < dlozanosoldevilla at gmail.com> ha scritto: > Hi Alessandro, > > As explained in the help > of > ft_preprocessing, the default filter order can be found in the low level > functions: > fieldtrip/preproc/ft_preproc_bandpassfilter.m > fieldtrip/preproc/ft_preproc_bandstopfilter.m > fieldtrip/preproc/ft_preproc_highpassfilter.m > fieldtrip/preproc/ft_preproc_lowpassfilter.m > > The filter order means something very different for the Butterworth (i.e. > amount of samples used for the input-output recursion) > > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 > > or for the Finite Impulse Response (FIR) filter (i.e. length of the filter > kernel). > > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 > > For the ones interested to know more about what that number does for > different filters, please check this example script: > > http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics > > I hope that helps, > > Diego > > > On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < > alessandro.orticoni at gmail.com> wrote: > >> Dear all, >> >> I would like to ask you just a question: which is the default order of >> the filters implemented by ft_preprocessing? I cannot find it anywhere. >> >> Thanks a lot, >> Alessandro Orticoni >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From k.kessler at aston.ac.uk Wed Oct 17 11:25:06 2018 From: k.kessler at aston.ac.uk (Kessler, Klaus) Date: Wed, 17 Oct 2018 09:25:06 +0000 Subject: [FieldTrip] Lectureship (faculty post) at Aston University Message-ID: Dear Fieldtrippers We would be grateful if you would disseminate or consider yourself applying for our faculty post at Aston University. We are particularly keen to strengthen our MEG group, but are open to other method backgrounds. For further details please follow the link. Many thanks Klaus https://jobs.aston.ac.uk/Vacancy.aspx?ref=R180444 Klaus Kessler (Professor of Cognitive Neuroscience) http://www.aston.ac.uk/lhs/staff/az-index/prof-klaus-kessler/ Aston Brain Centre (ABC), Aston Laboratory for Immersive Virtual Environments (ALIVE) School of Life and Health Sciences Aston University Aston Triangle Birmingham, B4 7ET Phone: +44 (0)121 204 3187 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Wed Oct 17 12:01:46 2018 From: katemsto at gmail.com (K S) Date: Wed, 17 Oct 2018 12:01:46 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: Hi Vahab, Thanks for the response. The data is already epoched so I tried ft_redefine trial as you suggested. I think the problem is that the segments marked as artefact are also marked with triggers. I therefore need some way of saying: "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried it also with ft_rejectartifact but I'm not sure how to get it to recognise the 'Bad Interval' marking. Any ideas? Many thanks, Kate On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < bioeng.yoosofzadeh at gmail.com> wrote: > Hi Kate, > > if it is a continuous data (before epoching), you can treat bad > segments as an artifact (e.g. eog or muscle, etc) and do something > like this, > > % artifact_EOG = [100 500]; % in sample > > cfg = []; > cfg.artfctdef.eog.artifact = artifact_EOG; > cfg.artfctdef.reject = 'value'; > % cfg.artfctdef.value = 0; % replacing values with nan or 0 > data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is > the output from ft_preprocessing. > > % inspecting cleaned data > cfg = []; > cfg.continuous = 'yes'; > cfg.viewmode = 'vertical'; % all channels seperate > cfg.blocksize = 5; % view the continous data in 30-s blocks > ft_databrowser(cfg, data_continuous_eog_clean); > > if the data is epoched, simply use ft_redefinetrial. > > Best, > Vahab > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Wed Oct 17 12:48:11 2018 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Wed, 17 Oct 2018 12:48:11 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> Hi Alessandro If you would want to apply them in a different order, just call ft_preprocessing multiple times, once for every filter you want to apply. best Robert > On 17 Oct 2018, at 00:54, Alessandro Orticoni wrote: > > Hi, > > Yes, thanks both! You have been very helpful. > > Best, > Alessandro > > Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla > ha scritto: > Hi Alessandro, > > As explained in the help of ft_preprocessing, the default filter order can be found in the low level functions: > fieldtrip/preproc/ft_preproc_bandpassfilter.m > fieldtrip/preproc/ft_preproc_bandstopfilter.m > fieldtrip/preproc/ft_preproc_highpassfilter.m > fieldtrip/preproc/ft_preproc_lowpassfilter.m > > The filter order means something very different for the Butterworth (i.e. amount of samples used for the input-output recursion) > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 > > or for the Finite Impulse Response (FIR) filter (i.e. length of the filter kernel). > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 > > For the ones interested to know more about what that number does for different filters, please check this example script: > http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics > > I hope that helps, > > Diego > > > On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni > wrote: > Dear all, > > I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. > > Thanks a lot, > Alessandro Orticoni > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From dmatthes at cbs.mpg.de Wed Oct 17 14:48:29 2018 From: dmatthes at cbs.mpg.de (Daniel Matthes) Date: Wed, 17 Oct 2018 14:48:29 +0200 (CEST) Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> Hi Kate, I did it in the following way: First, I did a regular import by ignoring the 'Bad Interval' markers. % ------------------------------------------------------------------------- % Data import % ------------------------------------------------------------------------- cfg = []; cfg.dataset = headerfile; cfg.trialfun = 'ft_trialfun_brainvision_segmented'; cfg.stimformat = 'S %d'; cfg.showcallinfo = 'no'; cfg = ft_definetrial(cfg); data = ft_preprocessing(cfg); After that step I have all trials in my data structure, also the trials which have bad intervals. In the second step I did this with the data: % ------------------------------------------------------------------------- % Estimate artifacts % ------------------------------------------------------------------------- events = data.cfg.event; % extract all events from the data structure artifact = zeros(length(events), 2); % allocate memory for the artifact array j = 1; for i=1:1:length(events) if(strcmp(events(i).type, 'Bad Interval')) % search for bad interval events artifact(j,1)=events(i).sample; % create artifact matrix artifact(j,2)=events(i).sample + events(i).duration - 1; j = j +1; end end artifact = artifact(1:j-1, :); % prune the artifact array to its actual size % ------------------------------------------------------------------------- % Revise data % ------------------------------------------------------------------------- cfg = []; cfg.event = events; cfg.artfctdef.reject = 'complete'; cfg.artfctdef.feedback = 'no'; cfg.artfctdef.xxx.artifact = artifact; cfg.showcallinfo = 'no'; data = ft_rejectartifact(cfg, data); These line are removing all trials with bad intervals completely from the data structure. But if you set the option cfg.artfctdef.reject to another value i.e. 'partial', you can also remove only the bad parts of certain trials I wrote this code some time ago, today I would replace the for-cycle with some more effective code. But in general it should work. All the best, Daniel ----- Original Message ----- From: "K S" To: "bioeng yoosofzadeh" Cc: fieldtrip at science.ru.nl Sent: Wednesday, October 17, 2018 12:01:46 PM Subject: Re: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' Hi Vahab, Thanks for the response. The data is already epoched so I tried ft_redefine trial as you suggested. I think the problem is that the segments marked as artefact are also marked with triggers. I therefore need some way of saying: "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried it also with ft_rejectartifact but I'm not sure how to get it to recognise the 'Bad Interval' marking. Any ideas? Many thanks, Kate On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < bioeng.yoosofzadeh at gmail.com > wrote: Hi Kate, if it is a continuous data (before epoching), you can treat bad segments as an artifact (e.g. eog or muscle, etc) and do something like this, % artifact_EOG = [100 500]; % in sample cfg = []; cfg.artfctdef.eog.artifact = artifact_EOG; cfg.artfctdef.reject = 'value'; % cfg.artfctdef.value = 0; % replacing values with nan or 0 data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is the output from ft_preprocessing. % inspecting cleaned data cfg = []; cfg.continuous = 'yes'; cfg.viewmode = 'vertical'; % all channels seperate cfg.blocksize = 5; % view the continous data in 30-s blocks ft_databrowser(cfg, data_continuous_eog_clean); if the data is epoched, simply use ft_redefinetrial. Best, Vahab -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From katemsto at gmail.com Wed Oct 17 17:59:52 2018 From: katemsto at gmail.com (Kate Stone) Date: Wed, 17 Oct 2018 17:59:52 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> References: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> Message-ID: Brilliant! Thanks Daniel, this worked perfectly. On Wed., 17 Oct. 2018, 15:35 Daniel Matthes, wrote: > Hi Kate, > > I did it in the following way: > > First, I did a regular import by ignoring the 'Bad Interval' markers. > > % ------------------------------------------------------------------------- > % Data import > % ------------------------------------------------------------------------- > > cfg = []; > cfg.dataset = headerfile; > cfg.trialfun = 'ft_trialfun_brainvision_segmented'; > cfg.stimformat = 'S %d'; > cfg.showcallinfo = 'no'; > > cfg = ft_definetrial(cfg); > data = ft_preprocessing(cfg); > > After that step I have all trials in my data structure, also the trials > which have bad intervals. > > In the second step I did this with the data: > > % ------------------------------------------------------------------------- > % Estimate artifacts > % ------------------------------------------------------------------------- > events = data.cfg.event; > % extract all events from the data structure > artifact = zeros(length(events), 2); > % allocate memory for the artifact array > j = 1; > > for i=1:1:length(events) > if(strcmp(events(i).type, 'Bad Interval')) > % search for bad interval events > artifact(j,1)=events(i).sample; > % create artifact matrix > artifact(j,2)=events(i).sample + events(i).duration - 1; > j = j +1; > end > end > > artifact = artifact(1:j-1, :); > % prune the artifact array to its actual size > > % ------------------------------------------------------------------------- > % Revise data > % ------------------------------------------------------------------------- > cfg = []; > cfg.event = events; > cfg.artfctdef.reject = 'complete'; > cfg.artfctdef.feedback = 'no'; > cfg.artfctdef.xxx.artifact = artifact; > cfg.showcallinfo = 'no'; > > data = ft_rejectartifact(cfg, data); > > These line are removing all trials with bad intervals completely from the > data structure. But if you set the option cfg.artfctdef.reject to another > value i.e. 'partial', you can also remove only the bad parts of certain > trials > > I wrote this code some time ago, today I would replace the for-cycle with > some more effective code. But in general it should work. > > All the best, > Daniel > > ----- Original Message ----- > From: "K S" > To: "bioeng yoosofzadeh" > Cc: fieldtrip at science.ru.nl > Sent: Wednesday, October 17, 2018 12:01:46 PM > Subject: Re: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' > > Hi Vahab, > > Thanks for the response. > > The data is already epoched so I tried ft_redefine trial as you suggested. > I think the problem is that the segments marked as artefact are also marked > with triggers. I therefore need some way of saying: > > "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" > > I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried > it also with ft_rejectartifact but I'm not sure how to get it to recognise > the 'Bad Interval' marking. > > Any ideas? > > Many thanks, > Kate > > > On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < > bioeng.yoosofzadeh at gmail.com > wrote: > > > Hi Kate, > > if it is a continuous data (before epoching), you can treat bad > segments as an artifact (e.g. eog or muscle, etc) and do something > like this, > > % artifact_EOG = [100 500]; % in sample > > cfg = []; > cfg.artfctdef.eog.artifact = artifact_EOG; > cfg.artfctdef.reject = 'value'; > % cfg.artfctdef.value = 0; % replacing values with nan or 0 > data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is > the output from ft_preprocessing. > > % inspecting cleaned data > cfg = []; > cfg.continuous = 'yes'; > cfg.viewmode = 'vertical'; % all channels seperate > cfg.blocksize = 5; % view the continous data in 30-s blocks > ft_databrowser(cfg, data_continuous_eog_clean); > > if the data is epoched, simply use ft_redefinetrial. > > Best, > Vahab > > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.orticoni at gmail.com Wed Oct 17 18:43:50 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Wed, 17 Oct 2018 18:43:50 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> References: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> Message-ID: Hi Robert, Thanks a lot. Best, Alessandro Il giorno mer 17 ott 2018 alle ore 13:09 Robert Oostenveld < r.oostenveld at donders.ru.nl> ha scritto: > Hi Alessandro > > If you would want to apply them in a different order, just call > ft_preprocessing multiple times, once for every filter you want to apply. > > best > Robert > > > On 17 Oct 2018, at 00:54, Alessandro Orticoni < > alessandro.orticoni at gmail.com> wrote: > > Hi, > > Yes, thanks both! You have been very helpful. > > Best, > Alessandro > > Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla < > dlozanosoldevilla at gmail.com> ha scritto: > >> Hi Alessandro, >> >> As explained in the help >> of >> ft_preprocessing, the default filter order can be found in the low level >> functions: >> fieldtrip/preproc/ft_preproc_bandpassfilter.m >> fieldtrip/preproc/ft_preproc_bandstopfilter.m >> fieldtrip/preproc/ft_preproc_highpassfilter.m >> fieldtrip/preproc/ft_preproc_lowpassfilter.m >> >> The filter order means something very different for the Butterworth (i.e. >> amount of samples used for the input-output recursion) >> >> https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 >> >> or for the Finite Impulse Response (FIR) filter (i.e. length of the >> filter kernel). >> >> https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 >> >> For the ones interested to know more about what that number does for >> different filters, please check this example script: >> >> http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics >> >> I hope that helps, >> >> Diego >> >> >> On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < >> alessandro.orticoni at gmail.com> wrote: >> >>> Dear all, >>> >>> I would like to ask you just a question: which is the default order of >>> the filters implemented by ft_preprocessing? I cannot find it anywhere. >>> >>> Thanks a lot, >>> Alessandro Orticoni >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >>> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From sauppe.s at gmail.com Thu Oct 18 12:49:00 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Thu, 18 Oct 2018 12:49:00 +0200 Subject: [FieldTrip] Interpolate outlier power values within trials Message-ID: <52E7BDAC-086E-4606-BB0E-060C21807A96@gmail.com> Dear FieldTrip list members, I have EEG data that I transform to power values with ft_freqanalysis. Is there a way to identify for each trial (= each frequency time series within each trial) outlier values that are much higher and lower than the other values in that frequency for that trial and then interpolate them? Regards, Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From danielkrauel96 at gmail.com Fri Oct 19 11:03:06 2018 From: danielkrauel96 at gmail.com (Daniel Krauel) Date: Fri, 19 Oct 2018 11:03:06 +0200 Subject: [FieldTrip] Fieldtrip Problem ft_default Message-ID: Subject: MCG Dear community, My name is Daniel Krauel and I am working for the UKSH in Kiel Germany. Currently I am analysing data of a MCG measurement. I am currently at the fieldtrip version from 27.05.2017 I used fieldtrip to read and extract the datas and also for plotting of sensor maps.It worked perfectly fine till one start. I couldn`t use ft_defaults, only executing 2 lines add path and ft default to get the error message below. addpath('C:\Users\Daniel\Documents\MATLAB\Till_2\fieldtrip-20170527'); ft_defaults; Undefined function or variable 'ft_platform_supports'. Error in ft_defaults>checkMultipleToolbox (line 280) if ~ft_platform_supports('which-all') Error in ft_defaults (line 109) checkMultipleToolbox('FieldTrip', 'ft_defaults.m'); Can someone explain me how I fix this problem? It looks so easy because I only execute two lines but I can`t fix it. I tried 4 different versions of fieldtrip and I also reinstalled matlab and resets all settings. In Addition the error message changes a little if I am using an other version of fieldtrip. Yours sincerly, Daniel Krauel -------------- next part -------------- An HTML attachment was scrubbed... URL: From gopalar.ccf at gmail.com Fri Oct 19 19:34:35 2018 From: gopalar.ccf at gmail.com (Raghavan Gopalakrishnan) Date: Fri, 19 Oct 2018 13:34:35 -0400 Subject: [FieldTrip] single subject coherence statistics In-Reply-To: <01d601d467d0$a397a570$eac6f050$@gmail.com> References: <01d601d467d0$a397a570$eac6f050$@gmail.com> Message-ID: <01e501d467d2$013c2920$03b47b60$@gmail.com> Dear all, There has been a lot of discussion on the topic of finding statistical significance between two conditions within the same subject, but there still seems to be some lack of clarity and issues. As I understand, The first step is to compute frequencies using ft_freqanalysis, with keep trials as ‘yes’ and output=’fourier’ cfg = []; cfg.output = 'fourier'; cfg.method = 'mtmfft'; cfg.foilim = [5 100]; cfg.tapsmofrq = 5; cfg.keeptrials = 'yes'; cfg.trials = find(data_redef.trialinfo(:,1)==trialopt); freq = ft_freqanalysis(cfg, data_redef); freq = struct with fields: label: {36×1 cell} dimord: 'rpttap_chan_freq' freq: [1×191 double] fourierspctrm: [1444×36×191 double] cumsumcnt: [76×1 double] cumtapcnt: [76×1 double] trialinfo: [76×5 double] cfg: [1×1 struct] The second step is to compute statistics using cfg.statistic = ‘indepsamplesZcoh’ Below is the example code cfg1 = []; cfg1.method = 'montecarlo'; cfg1.statistic = 'indepsamplesZcoh'; cfg1.correctm = 'cluster'; cfg1.clusteralpha = 0.05; cfg1.minnbchan = 2; cfg1.neighbours = neighbours; % neighbours computed separately before cfg1.tail = 0; % -1, 1 or 0 (default = 0); one-sided or two-sided test cfg1.clustertail = 0; cfg1.alpha = 0.025; % alpha level of the permutation test cfg1.numrandomization = 500; % number of draws from the permutation distribution cfg1.parameter = 'fourierspctrm'; design = zeros(1,size(freq1.fourierspctrm,1) + size(freq2.fourierspctrm,1)); design(1,1:size(freq1.fourierspctrm,1)) = 1; design(1,(size(freq1.fourierspctrm,1)+1):(size(freq1.fourierspctrm,1) + size(freq2.fourierspctrm,1)))= 2; cfg1.design = design'; cfg1.ivar = 1; cfg1.channelcmb = {'FC1' 'REMG'}; % coherence between two channels cfg1.computecritval = 'yes'; stat=ft_freqstatistics(cfg1, freq1, freq2); % freq1 and freq2 are two conditions from same subject But the second step gives an error because of dimension issues with the data Assignment has more non-singleton rhs dimensions than non-singleton subscripts Error in ft_statistics_montecarlo (line 319) statrand(:,i) = dum.stat; Error in ft_freqstatistics (line 193) [stat, cfg] = statmethod(cfg, dat, design); Error in Griptask_RT_coherence_analysis (line 140) stat=ft_freqstatistics(cfg1, freq1, freq2); There are couple of previous posts (below) that reported the same kind of error, but there is no solution yet https://mailman.science.ru.nl/pipermail/fieldtrip/2013-July/006821.html https://mailman.science.ru.nl/pipermail/fieldtrip/2010-June/002900.html In the below post, Jan-Mathijs says not to use ‘cluster’ for correctm option. However, if we don’t use then how to reproduce the effects reported in “Nonparametric statistical testing of coherence differences” paper? https://mailman.science.ru.nl/pipermail/fieldtrip/2010-April/002830.html Is this error due to a bug? Or am I doing any mistake? I appreciate developers addressing this issue. Thanks, Raghavan -------------- next part -------------- An HTML attachment was scrubbed... URL: From shaegens at gmail.com Mon Oct 22 23:19:34 2018 From: shaegens at gmail.com (Saskia Haegens) Date: Mon, 22 Oct 2018 17:19:34 -0400 Subject: [FieldTrip] postdoc opportunity at donders Message-ID: Applications are invited for a postdoc position in my lab, studying oscillations using MEG. For details please see: https://www.ru.nl/english/working-at/jobopportunities/details/details-vacature/?recid=601751 -- Saskia Haegens, PhD haegenslab.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From conny.quaedflieg at maastrichtuniversity.nl Tue Oct 23 00:18:57 2018 From: conny.quaedflieg at maastrichtuniversity.nl (Quaedflieg, Conny (PSYCHOLOGY)) Date: Mon, 22 Oct 2018 22:18:57 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis between Groups - GA, plotting and statistics Message-ID: <4ac933a569fb432fae71404f91a37f0e@UM-MAIL3218.unimaas.nl> Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Tue Oct 23 14:04:15 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 23 Oct 2018 14:04:15 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs Message-ID: Hi all, I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. Thanks in advance, Kate -------------- next part -------------- An HTML attachment was scrubbed... URL: From m.manahova at gmail.com Tue Oct 23 14:55:30 2018 From: m.manahova at gmail.com (Mariya Manahova) Date: Tue, 23 Oct 2018 14:55:30 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, You mention that you're using indepsamplesT, so are you doing a test between groups (comparing two different groups) or within participants (comparing the same participants in two conditions)? If it's the latter, then I'd suggest using depsamplesT. I am pasting below a good way to define your design matrix. This works well for a within-participant comparison. You'll need to adapt it if yours is indeed between groups. See the comments for the explanation. Nsub = 29; cfg.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)]; cfg.design(2,1:2*Nsub) = [1:Nsub 1:Nsub]; cfg.ivar = 1; % the 1st row in cfg.design contains the independent variable cfg.uvar = 2; % the 2nd row in cfg.design contains the subject number A possible problem is if you've computed grand averages and haven't used cfg.keepindividual = 'yes'. If that's the case, I'd suggest keeping the individual participants' data when calling ft_timelockgrandaverage. And then using the correct design matrix. I hope this helps! Let us know if it still doesn't work. All the best, Marisha On Tue, Oct 23, 2018 at 2:35 PM Kate Stone wrote: > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP > data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using > indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't > apply as I am using grand averages and the reference for > ft_timelockstatistics doesn't mention the design matrix, although it is > definitely required. I have tried design = [1,2], but this doesn't seem to > give sensible results. > > Thanks in advance, > Kate > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Tue Oct 23 14:59:26 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Tue, 23 Oct 2018 14:59:26 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, You can't do permutation statistics if you've already consumed the degrees of freedom you want to do statistics across (i.e., subjects in this case). Did you use cfg.keepindividual = 'yes' in the call to ft_timelockgrandaverage? If so, then the design matrix should be specified exactly as in the tutorial; just as if you had been using the individual subjects' data structures. If not, then indeed you only have a grand-average left, and no permutation stats can be performed. Cheers, Eelke On Tue, 23 Oct 2018 at 14:04, Kate Stone wrote: > > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. > > Thanks in advance, > Kate > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From katemsto at gmail.com Tue Oct 23 17:21:22 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 23 Oct 2018 17:21:22 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi again, Further to the below, what I would actually prefer to do is use the depsamplesT test on my data structure containing averages over trials for each subject (i.e. the output of ft_timelockanalysis). But every time I try, I get the message "length of the design matrix (2) doesn't equal the number of observations (694)" - no matter what the dimensions of the design matrix are. I've set up the matrix exactly as specified in the tutorial (relevant to my data of course). Any idea what this error is about? Thanks again, Kate On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP > data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using > indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't > apply as I am using grand averages and the reference for > ft_timelockstatistics doesn't mention the design matrix, although it is > definitely required. I have tried design = [1,2], but this doesn't seem to > give sensible results. > > Thanks in advance, > Kate > > > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Wed Oct 24 09:39:39 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Wed, 24 Oct 2018 09:39:39 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, It sounds like what you're dealing with is a single group of participants, each of whom is measured under two conditions. You are correct in stating that depsamplesT is the way to go here (as Marisha also suggested). This is, I believe, exactly the case described in this section of the "gentle" stats tutorial: http://www.fieldtriptoolbox.org/tutorial/eventrelatedstatistics#permutation_test_based_on_cluster_statistics and this section of the more "in depth" tutorial: http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock#within-subjects_experiments . See those tutorials (and Marisha's email) for more info on how to set up your design matrix in this case. Cheers, Eelke On Tue, 23 Oct 2018 at 17:21, Kate Stone wrote: > > Hi again, > > Further to the below, what I would actually prefer to do is use the depsamplesT test on my data structure containing averages over trials for each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) doesn't equal the number of observations (694)" - no matter what the dimensions of the design matrix are. I've set up the matrix exactly as specified in the tutorial (relevant to my data of course). Any idea what this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: >> >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. >> >> Thanks in advance, >> Kate >> >> > > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From m.manahova at gmail.com Wed Oct 24 09:41:43 2018 From: m.manahova at gmail.com (Mariya Manahova) Date: Wed, 24 Oct 2018 09:41:43 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, Clearly, there's a problem with the way you're defining your design matrix. Can you tell me what cfg.design looks like when you define it? With the code I sent you, the design matrix is a 2x58 matrix (the number of participants is 29). There are two rows, one for the independent variable (condition 1 or 2) and one for the participant number. The first row consists of 29 1's and 29 2's because I have two conditions (1 and 2), and this denotes that each participant is on the one hand in condition 1 and on the other hand in condition 2. The second row is 1:29 and then again 1:29, referring to the participant number per condition. Does this make sense? In this case, the length of my design matrix is 58 because I have 2 observations (1 for each condition) per participant. Why do you end up having 694? Can you paste the output of your cfg.design? Here's mine: [image: image.png] All the best, Marisha On Tue, Oct 23, 2018 at 6:35 PM Kate Stone wrote: > Hi again, > > Further to the below, what I would actually prefer to do is use the > depsamplesT test on my data structure containing averages over trials for > each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) > doesn't equal the number of observations (694)" - no matter what the > dimensions of the design matrix are. I've set up the matrix exactly as > specified in the tutorial (relevant to my data of course). Any idea what > this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on >> ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using >> indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't >> apply as I am using grand averages and the reference for >> ft_timelockstatistics doesn't mention the design matrix, although it is >> definitely required. I have tried design = [1,2], but this doesn't seem to >> give sensible results. >> >> Thanks in advance, >> Kate >> >> >> > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 15551 bytes Desc: not available URL: From jan.schoffelen at donders.ru.nl Wed Oct 24 10:07:26 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 24 Oct 2018 08:07:26 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis between Groups - GA, plotting and statistics References: Message-ID: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) > wrote: Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Wed Oct 24 10:49:10 2018 From: katemsto at gmail.com (Kate Stone) Date: Wed, 24 Oct 2018 10:49:10 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hello, one last email just in case anyone is following my series of very silly questions or ever searches the following warnings when trying to run ft_timelockstatistics: Warning: timelock structure contains field with and without repetitions Error: the length of the design matrix does not match the number of observations in the data In my case, it was because, in the previous step where I averaged ERPs over trials using ft_timelockanalysis, I had set cfg.keeptrials = 'yes' . It should be left at the default 'no' (for a within-subjects design at least), otherwise ft_timelockstatistics will go looking for observations in the wrong spot (trial) and will discard the ones it really needs (avg, var, dof). Thanks and apologies for over-posting, Kate On Tue, Oct 23, 2018 at 5:21 PM Kate Stone wrote: > Hi again, > > Further to the below, what I would actually prefer to do is use the > depsamplesT test on my data structure containing averages over trials for > each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) > doesn't equal the number of observations (694)" - no matter what the > dimensions of the design matrix are. I've set up the matrix exactly as > specified in the tutorial (relevant to my data of course). Any idea what > this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on >> ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using >> indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't >> apply as I am using grand averages and the reference for >> ft_timelockstatistics doesn't mention the design matrix, although it is >> definitely required. I have tried design = [1,2], but this doesn't seem to >> give sensible results. >> >> Thanks in advance, >> Kate >> >> >> > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From Joachim.Gross at glasgow.ac.uk Wed Oct 24 11:03:17 2018 From: Joachim.Gross at glasgow.ac.uk (Joachim Gross) Date: Wed, 24 Oct 2018 09:03:17 +0000 Subject: [FieldTrip] PhD student position in Muenster, Germany Message-ID: <4C93D541-3D77-4398-8160-A0FE9C3F152F@glasgow.ac.uk> The University Hospital of Münster is one of the leading hospitals in Germany. Such a position cannot be achieved by size and medical successes alone. The individual commitment counts above all. We need your commitment so that even with little things we can achieve great things for our patients. There are many possibilities open for you so that you may develop with them. The Institute for Biomagnetism and Biosignalanalysis at University of Muenster invites applications for a position as a PhD Student (gn*) Ref.: 03170 Part-Time with 65% (Germany salary grade: E 13 TV-L, 65%) (*gn=gender neutral) The position starts on 1.1.2019 and is available for 3 years in the research group of Prof. Dr. Joachim Gross. The successful candidate will coordinate, perform and publish research using MEG to study brain oscillations. We offer a stimulating environment in a successful team with high-level experience in MEG research. Successful candidates will benefit from personal mentoring, weekly seminars and general training and knowledge dissemination within the Institute for Biomagnetism and Biosignalanalysis (IBB). The position offers opportunity for further academic qualifications (PhD). The successful applicants will join a dynamic team of both experienced and junior researchers with many national and international collaborations and a sustained record of high-quality research output. We are searching for trained and highly motivated scientists ideally having experience in electrophysiology, cognitive neuroimaging or similar disciplines. The research projects require acquisition of MEG data and analysis of neuronal oscillations and their relationship to behaviour. Therefore, experience with MEG/EEG, programming and spectral analysis is highly desirable. Requirements: * Degree in psychology, medicine, physics, or a related discipline relevant for neuroscience * Experience with cognitive neuroscience research ideally with MEG or EEG * Experience with running cognitive neuroscience studies * Programming skills (matlab, python or similar) * Knowledge of general statistics for data analysis * Very good English language skills * Strong commitment, flexibility, independence and team work For more information please contact Prof. Dr. Joachim Gross, University of Muenster, Institute for Biomagnetism and Biosignalanalysis, 48149 Münster, Germany, Email: Joachim.Gross(at)­wwu(dot)­de Please send your application (including the above reference number) with all relevant information (CV, cover letter) until 11.11.2018 to Personalgewinnung des Universitätsklinikums Münster, Bewerbermanagement, Domagkstraße 5, 48149 Münster or via e-mail (PDF-file, max. 5 MB) to Bewerbung(at)­ukmuenster(dot)­de. Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference. -------------- next part -------------- An HTML attachment was scrubbed... URL: From Joachim.Gross at glasgow.ac.uk Wed Oct 24 11:03:56 2018 From: Joachim.Gross at glasgow.ac.uk (Joachim Gross) Date: Wed, 24 Oct 2018 09:03:56 +0000 Subject: [FieldTrip] PostDoc Position in Muenster, Germany Message-ID: <0EC39993-381F-49E2-B52F-FE1CD36F4058@glasgow.ac.uk> The University Hospital of Münster is one of the leading hospitals in Germany. Such a position cannot be achieved by size and medical successes alone. The individual commitment counts above all. We need your commitment so that even with little things we can achieve great things for our patients. There are many possibilities open for you so that you may develop with them. The Institute for Biomagnetism and Biosignalanalysis at University of Muenster invites applications for a Research Associate/Postdoctoral Scientist (gn*) Ref.: 03171 Full-Time with 38,5 (hours/week) (Germany salary grade: E 13 TV-L, 100%) (*gn=gender neutral) The position starts on 1.1.2019 and is available for 3 years in the research group of Prof. Dr. Joachim Gross. The successful candidate will coordinate, perform and publish research using MEG to study brain oscillations. We offer a stimulating environment in a successful team with high-level experience in MEG research. Successful candidates will benefit from personal mentoring, weekly seminars and general training and knowledge dissemination within the Institute for Biomagnetism and Biosignalanalysis (IBB). The position offers opportunity for further academic qualifications (Habilitation). The successful applicants will join a dynamic team of both experienced and junior researchers with many national and international collaborations and a sustained record of high-quality research output. We are searching for trained and highly motivated scientists ideally having experience in electrophysiology, cognitive neuroimaging or similar disciplines. The research projects require acquisition of MEG data and analysis of neuronal oscillations and their relationship to behaviour. Therefore, experience with MEG/EEG, programming and spectral analysis is highly desirable. Requirements: * PhD in psychology, medicine, physics, or a related discipline relevant for neuroscience * Experience with cognitive neuroscience research ideally with MEG or EEG * Publications in peer-reviewed journals * Experience with running cognitive neuroscience studies * Programming skills (matlab, python or similar) * Knowledge of general statistics for data analysis * Very good English language skills * Strong commitment, flexibility, independence and team work For more information please contact Prof. Dr. Joachim Gross, University of Muenster, Institute for Biomagnetism and Biosignalanalysis, 48149 Münster, Germany, Email: Joachim.Gross(at)­wwu(dot)­de Please send your application (including the above reference number) with all relevant information (CV, cover letter) until 11.11.2018 to Personalgewinnung des Universitätsklinikums Münster, Bewerbermanagement, Domagkstraße 5, 48149 Münster or via e-mail (PDF-file, max. 5 MB) to Bewerbung(at)­ukmuenster(dot)­de. Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference. -------------- next part -------------- An HTML attachment was scrubbed... URL: From agathalenartowicz at gmail.com Wed Oct 24 20:37:00 2018 From: agathalenartowicz at gmail.com (Agatha Lenartowicz) Date: Wed, 24 Oct 2018 11:37:00 -0700 Subject: [FieldTrip] postdoc opportunity - alpha oscillations/concurrent EEG-fMRI Message-ID: We’d like to share a *post-doctoral opening* at the Semel Institute for Neuroscience and Behavior at University of California, Los Angeles (Loo/Lenartowicz labs). The position is part of a funded 5-year project to map, using *concurrent EEG and fMRI,* the *brain circuitry of alpha-range oscillations and their impairments in ADHD.* The position requires familiarity with MRI and/or EEG data, knowledge of UNIX, Matlab (and/or Python), and is ideally suited for candidates with a strong background in neuroimaging, signal processing & electrical/biomedical engineering, physics or some combination. Excellent communication skills, initiative, ability to anticipate changes and develop solutions, and attention to detail are imperative. The position offers a vibrant working environment, as well as ample opportunity to participate in collaborative and independent research. For inquiries please contact Agatha Lenartowicz ( alenarto at g.ucla.edu). -------------- next part -------------- An HTML attachment was scrubbed... URL: From cornelia.quaedflieg at uni-hamburg.de Wed Oct 24 23:42:54 2018 From: cornelia.quaedflieg at uni-hamburg.de (Conny Quaedflieg) Date: Wed, 24 Oct 2018 23:42:54 +0200 Subject: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics In-Reply-To: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> References: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> Message-ID: <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> Dear Jan-Mathijs, Thank you for your quick reply. @1R Indeed ft_connectivityanalysis on individual dipole positions (PCC). Cfg.refindx’ seems not to do anything, are there other options to run a seed-based connectivity analysis? @R2 We indeed performed source-reconstruction for each subject on a subject-specific grid, that maps onto a template grid in spatially normalized space. I constructed a GA of the individual data and would like to plot these on a standard cortical sheet / brain surface. Best Conny Van: Schoffelen, J.M. (Jan Mathijs) Verzonden: woensdag 24 oktober 2018 10:16 Aan: FieldTrip discussion list Onderwerp: Re: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to  http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) wrote:   Dear Fieldtripers,   I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx.    Though, the analysis looks exactly the same with and without the refindx specified.   I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long.   Best   Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164   _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Oct 25 08:49:44 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 25 Oct 2018 06:49:44 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics In-Reply-To: <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> References: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> Message-ID: <0FD19B0D-E3D7-4A46-99B7-19840CC3B488@donders.ru.nl> Hi Conny, Again, could you please spill more details? -what is the connectivity metric you want to compute? -what do you mean with ‘cfg.refindx seems not to do anything’? -what have you done in terms of diagnostics yourself? have you looked into the code with breakpoints etc.? Jan-Mathijs On 24 Oct 2018, at 23:42, Conny Quaedflieg > wrote: Dear Jan-Mathijs, Thank you for your quick reply. @1R Indeed ft_connectivityanalysis on individual dipole positions (PCC). Cfg.refindx’ seems not to do anything, are there other options to run a seed-based connectivity analysis? @R2 We indeed performed source-reconstruction for each subject on a subject-specific grid, that maps onto a template grid in spatially normalized space. I constructed a GA of the individual data and would like to plot these on a standard cortical sheet / brain surface. Best Conny Van: Schoffelen, J.M. (Jan Mathijs) Verzonden: woensdag 24 oktober 2018 10:16 Aan: FieldTrip discussion list Onderwerp: Re: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) > wrote: Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From gerhard.jocham at uni-duesseldorf.de Thu Oct 25 10:23:42 2018 From: gerhard.jocham at uni-duesseldorf.de (Gerhard Jocham) Date: Thu, 25 Oct 2018 10:23:42 +0200 Subject: [FieldTrip] =?utf-8?q?Three_Postdoc_and_one_PhD_student_position?= =?utf-8?q?=2C_Heinrich_Heine_University_D=C3=BCsseldorf?= Message-ID: <9F20E20B-DFEB-4458-AA99-D8509F0556C0@uni-duesseldorf.de> Two postdoctoral positions (up to five years) and one PhD student position (four years) on an ERC-funded project, and one postdoctoral position (three years) on a DFG-funded project are available at the Heinrich Heine University Düsseldorf. Please check the links for detailed information and for how to apply: http://www.cns-jocham.de/01_advert_postdocs_erc.pdf http://www.cns-jocham.de/03_advert_postdoc_dfg_heise.pdf http://www.cns-jocham.de/02_advert_phd_erc.pdf The projects focus on how decision variables are represented in cortical dynamics (recorded with MEG), and how these representations are shaped by neuromodulatory systems. We are seeking candidates with a strong interest in decision making. Applicants for the postdoc positions should have a PhD in psychology, neuroscience, or related field. Demonstrable experience with either MEG or EEG and good programming skills (e.g. Matlab, Python) are essential. Applicants for the PhD position should have an MSc (or equivalent degree) in psychology, neuroscience, or related field, and sound knowledge of statistics. The ideal candidate should also possess programming skills (e.g., Matlab, Python) and have prior experience with MEG/EEG analysis. Kind regards Gerhard Jocham ================================= Prof. Dr. Gerhard Jocham Biological Psychology of Decision Making Institute of Experimental Psychology Heinrich Heine University Düsseldorf Universitätsstraße 1 40225 Düsseldorf, Germany +49 (0) 211 81 12468 -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Thu Oct 25 12:45:03 2018 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Thu, 25 Oct 2018 12:45:03 +0200 Subject: [FieldTrip] Quick eloreta question In-Reply-To: References: Message-ID: <499624E4-5D51-4042-B1A9-883FCE337164@donders.ru.nl> Hi Arno, Let me CC this to the mailing list, as I think that it might be of iterest to others. You should preferably be calling eloreta through ft_sourceanalysis. There you can see on line 1016 how it is called for ERP/ERF data with the data covariance (and elsewhere with the cross-spectrum). The phase information is certainly not ignored: an obvious one that you need is the exact out-of-phase (i.e. 180 degree rotation) of the positive and negative channels that see the same dipole. So taking the “abs” is not appropriate. But there is sometimes a reason to take “real” and ignore “imag”: For beamforming - where we scan with a single dipole - we do know that the dipole can only be in phase or exactly out-of-phase, which means that the complex part of the CSD cannot be attibuted to the source of interest. That is implemented with the ‘realfilter’ option in beamformer_dics (default=no, consistent with the publication) and beamformer_pcc (default=yes, because it works slightly better). Since we are not doing a scalar beamformer in most cases, but a vector beamformer (which can rotate in 2 or 3 dimensions), it is not obvious under which conditions realfilter works the best. A strongly rotating source has out-of-phase CSD coponents between the different dipole orientations, so in that case realfilter=no should be better able to reconstruct it. But in our data we (anecdotically) tend to get slightly better SNR at the source level for realfilter=yes. For frequency domain eloreta you are estimating many source simultaneously, where the sources can have any phase relation to each other, so there is a priori no point in only taking the real part of the CSD. Hence it is also not implemented. However, if you were to hypothesize that the underlying source is very one-dimensional (as with an ICA) and not rotating, the same reasoning would apply as for the beamformer and you would expect slightly better performance ignoring the imaginary part of the CSD in the inversion. best Robert PS the getting_started page on “loreta” refers to the specific Loreta software, not the inverse methods. Furthermore, it is >10 years old and probably outdated. > On 24 Oct 2018, at 19:49, Arnaud Delorme wrote: > > HI Robert, > > Another quick follow up, still on cross-spectrum and Loreta. > > When I use ft_freqanalysis, I get a complex crossspectrum (dataset with 80 trials). > However, my understanding is that you only use the absolute value of the crossspectrum for source localization (phase information is ignored), and that the crossspectrum should be computed on a single trial basis then averaged the absolute value should be averaged accross trials. It is unclear to me how you can obtain a complex estimate on multiple trials (are you averaging the complex cross-spectrum values across trials - I have done some more test and it seems that this is what you are doing). Attached is a cross-spectrum calculated using this method (custom code, left) versus the absolute value of the cross-spectrum returned by ft_freqanalysis (right) on the same data. > > When I do take the absolute value before doing the average of the cross-spectrum, the eLoreta solution is also more focal. > > Cheers, > > Arno > > > >> On Oct 24, 2018, at 9:41 AM, Arnaud Delorme > wrote: >> >> Hi Robert, >> >> I have a quick eLoreta question. In Fieldtrip ft_sourcelocalize, it seems that eLoreta requires the cross-spectrum. I have tried without (or used NaN) but the function is not functioning properly in that case. This means that eLoreta cannot be applied to ERPs, is that correct? What about ICA component scalp topographies (in that case I can weight the cross-spectrum using the channel inverse weight matrix - the cross-spectrum matrix would be proportional to the product of the column in the inverse weight matrix corresponding to the component by its transpose). For spectral decomposition, assuming the spectrum is in the diagonal of the cross-spectrum, is the spectrum field even used at all (I was not able to find information about that). >> >> Aso my intuition is that performing statistics (as outlined on this page http://www.fieldtriptoolbox.org/getting_started/loreta ) at the voxel level does not make sense if the statistics at the electrode level is not significant. >> >> Thank you, >> >> Arno > -------------- next part -------------- An HTML attachment was scrubbed... URL: From rdm146 at newark.rutgers.edu Thu Oct 25 23:05:46 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Thu, 25 Oct 2018 21:05:46 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Message-ID: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi -------------- next part -------------- An HTML attachment was scrubbed... URL: From manuela.costa at ctb.upm.es Fri Oct 26 18:50:41 2018 From: manuela.costa at ctb.upm.es (Manuela Costa) Date: Fri, 26 Oct 2018 18:50:41 +0200 Subject: [FieldTrip] Across regions PAC Message-ID: Dear community, I performed phase low amplitude high coupling within a region (A) following fieldtrip tutorial. http://www.fieldtriptoolbox.org/example/crossfreq/phalow_amphigh Now I would like to see wether low frequency in my region (A) modulate the amplitude of high frequency in region (B). Which is the correct way to perform this analysis? Best regards, Manuela -------------- next part -------------- An HTML attachment was scrubbed... URL: From gopalar.ccf at gmail.com Fri Oct 26 20:02:16 2018 From: gopalar.ccf at gmail.com (Raghavan Gopalakrishnan) Date: Fri, 26 Oct 2018 14:02:16 -0400 Subject: [FieldTrip] Across regions PAC Message-ID: <049501d46d56$08895520$199bff60$@gmail.com> Hi Manuela I suggest you use Brainstorm to perform Phase-amplitude coupling analysis (PAC). Brainstorm has advanced PAC algorithms. Now you can import fieldtrip structure to Brainstorm easily. https://neuroimage.usc.edu/brainstorm/Tutorials/TutPac https://neuroimage.usc.edu/brainstorm/Tutorials/Resting Thanks, Raghavan Dear community, I performed phase low amplitude high coupling within a region (A) following fieldtrip tutorial. http://www.fieldtriptoolbox.org/example/crossfreq/phalow_amphigh Now I would like to see wether low frequency in my region (A) modulate the amplitude of high frequency in region (B). Which is the correct way to perform this analysis? Best regards, Manuela -------------- next part -------------- An HTML attachment was scrubbed... URL: From carsten.wolters at uni-muenster.de Mon Oct 29 12:31:25 2018 From: carsten.wolters at uni-muenster.de (Carsten Wolters) Date: Mon, 29 Oct 2018 12:31:25 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: Message-ID: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR    Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: > > Dear Fieldtrippers > > > I have applied the FEM simbio head modeling pipeline implemented > in Fieldtrip to my EEG data. My understanding is that this pipeline > assumes isotropic conductivities for 5 head compartments (as specified > by cfg.conductivity in ft_prepare_headmodel). After reading some > papers (e.g. Vorwerk et al 2014 > https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like > incorporating white matter conductivity anisotropy has a relatively > small albeit significant effect on the source solution. I am > interested in comparing FEM results when treating white matter as > anisotropic. My questions are as follows: > > > 1. Is there a way to implement the FEM simbio head model whilst > treating WM as anisotropic within Fieldtrip? If so, how would one > do this (or are there any resources available that demonstrate this)? > 2. From previous papers and some simbio documentation > (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) > it seems like diffusion MRI data is required to calculate the WM > conductivity for each individual subject. I only have T1 and T2 > scans for my subjects. So would it be possible to use WM > anisotropic information obtained from some kind of diffusion > MRI group average/atlas instead (accepting some loss in > subject-level precision)? If so, does such a group average/atlas > exist? > > > Any help would be greatly appreciated! > > > Thanks > > Ravi > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From maria.hakonen at gmail.com Mon Oct 29 13:33:15 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Mon, 29 Oct 2018 14:33:15 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? Message-ID: Dear FieldTrip experts, I have run ft_mergealign across subjects to align the head positions. However, the residual variance between the original and the realigned data seems to be high: original -> template RV 21232.46 % original -> original RV 36.96 % original -> template -> original RV 9579.95 % Could someone please let me know what would be the largest acceptable change in the residual variance, and what should I do if the residual variance is too high? Does the increase in residual variance mean that there is a large shift in the head position? I have used ft_mergealign as follows: template = list of subjects (i.e. I want to calculate an average head position over the subjects) grad = data.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); vol = ft_headmodel_localspheres(hs,grad); cfg = []; cfg.template = template; cfg.inwardshift = 1.0; cfg.vol = vol; data_aligned = ft_megrealign(cfg, data); Best, Maria -------------- next part -------------- An HTML attachment was scrubbed... URL: From j.vorw01 at gmail.com Mon Oct 29 15:14:16 2018 From: j.vorw01 at gmail.com (Johannes Vorwerk) Date: Mon, 29 Oct 2018 15:14:16 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: Message-ID: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes > Am 29.10.2018 um 12:31 schrieb Carsten Wolters : > > Dear Ravi, > > 1) You can use the pure SimBio-code from > https://www.mrt.uni-jena.de/simbio/index.php/Main_Page > to treat WM anisotropy. > While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, > this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented > Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: > "Depending on your matlab skills and your available time, I could help you to give it a > try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." > > 2) We recommend > http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf > on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main > bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details > in the cortices are individual. We always measure T1, T2 and DTI from each subject > and I personally do not have experience with such a group-level anisotropy compared > to the individual one. Might be interesting to hear from others what they think!? > > BR > Carsten > > > > Am 25.10.18 um 23:05 schrieb Ravi Mill: >> Dear Fieldtrippers >> >> I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040 ), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: >> >> Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? >> From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples ) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? >> >> Any help would be greatly appreciated! >> >> Thanks >> Ravi >> >> >> >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From victor.rg.gib at gmail.com Tue Oct 30 10:43:08 2018 From: victor.rg.gib at gmail.com (Victor RG) Date: Tue, 30 Oct 2018 10:43:08 +0100 Subject: [FieldTrip] Problem using ft_sensrorealign with Yokogawa MEG Message-ID: HI Fieldtrip experts! I'm trying to align a headmodel (the one from example "Subject1") with my MEG sensors (Yokogawa system). I'm trying to do that interactively, in order to construct a Leadfied matrix as accurate as possible. To do that, I am testing this code: *cfg = [];* *cfg.method = 'interactive';* *cfg.headshape = vol.bnd(1);* *cfg.senstype = 'meggrad';* *grad_aligned = ft_sensorrealign(cfg, grad); % Im using ft_sensorrealign cause I think is the MEG version of ft_electroderealign* The variables employed consist of: *>> grad = * struct with fields: - balance: [1×1 struct] - chanori: [160×3 double] - chanpos: [160×3 double] - chantype: {160×1 cell} - chanunit: {160×1 cell} - coilori: [320×3 double] - coilpos: [320×3 double] - label: {160×1 cell} - tra: [160×320 double] - type: 'yokogawa160' - unit: 'cm' - fid: [1×1 struct] *>> vol.bnd(1) =* struct with fields: - pos: [1000×3 double] - tri: [1996×3 double] - coordsys: 'ctf' I have looked at the tutorial for EEG sensors realignment, and I have copied the procedure, since there is not a specific tutorial for MEG sensors. I thought it would be the same, but when executing it I obtain the following errors: *>> Undefined function 'fixpos' for input arguments of type 'struct'.* *Error in ft_sensorrealign (line 255)* *headshape = fixpos(cfg.headshape);* *Error in generating_leadfield (line 63)* *grad_aligned = ft_sensorrealign(cfg, grad);* Does anybody know how to do that, or how to do an interactive realignment with MEG sensors? Thanks in advance. Víctor. Víctor Rodríguez González Grupo de Ingeniería Biomédica, ETSIT. Universidad de Valladolid, España. -------------- next part -------------- An HTML attachment was scrubbed... URL: From hesham.elshafei at inserm.fr Tue Oct 30 17:05:08 2018 From: hesham.elshafei at inserm.fr (Hesham ElShafei) Date: Tue, 30 Oct 2018 17:05:08 +0100 Subject: [FieldTrip] Phase Information For PCC Beamformer Message-ID: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> Hello Fieldtrippers! So I am trying to do some whole brain connectivity analysis according to this tutorial: http://www.fieldtriptoolbox.org/tutorial/networkanalysis All is going fine :) I just would like to understand how the phase information is obtained using these options in ft_freqanalysis cfg.method = 'mtmfft'; cfg.output = 'fourier'; In other words , how is time incorporated in the computed phase values? In other other words, these phase values represent the signal at which time point? hope I was clear enough Cheers! Hesham From ignasi.sols at nyu.edu Tue Oct 30 19:38:13 2018 From: ignasi.sols at nyu.edu (Ignasi Sols) Date: Tue, 30 Oct 2018 14:38:13 -0400 Subject: [FieldTrip] Brain-shift compensation Error Message-ID: Dear all, I'm following the method developed by Stolk et al (2018) to localize the electrodes of ECoG data. I'm getting this error on step 23 (Project the electrode grids to the surface hull of the implanted hemisphere) and I can't solve it. Could anyone help me with this? Thanks, Ignasi *using electrodes specified in the configuration* *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: 22155045* *creating electrode pairs based on electrode positions* *Error using fmincon (line 241)* *You must provide a non-empty starting* *point.* *Error in warp_dykstra2012 (line 156)* *coord_snapped = fmincon(efun, coord0,* *[], [], [], [], [], [], cfun,* *options);* *Error in ft_electroderealign (line* *406)* * norm.elecpos =* warp_dykstra2012(cfg, elec, headshape); -- Ignasi Sols Postdoctoral Fellow Department of Psychology New York University -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Wed Oct 31 07:23:37 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Tue, 30 Oct 2018 23:23:37 -0700 Subject: [FieldTrip] Brain-shift compensation Error In-Reply-To: References: Message-ID: Hi Ignasi, One can only guess based on that error msg alone. You might want to put a debug marker at line 156, check whether coord0 is truly empty, and then try to trace back to what's causing it to be empty (e.g., an empty elecpos field in your elec structure?). Arjen On Tue, Oct 30, 2018 at 12:04 PM Ignasi Sols wrote: > Dear all, > I'm following the method developed by Stolk et al (2018) to localize the > electrodes of ECoG data. > I'm getting this error on step 23 (Project the electrode grids to the > surface hull of the implanted hemisphere) and I can't solve it. Could > anyone help me with this? > > Thanks, > Ignasi > > > *using electrodes specified in the configuration* > *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: > 22155045* > *creating electrode pairs based on electrode positions* > *Error using fmincon (line 241)* > *You must provide a non-empty starting* > *point.* > *Error in warp_dykstra2012 (line 156)* > *coord_snapped = fmincon(efun, coord0,* > *[], [], [], [], [], [], cfun,* > *options);* > *Error in ft_electroderealign (line* > *406)* > * norm.elecpos =* > warp_dykstra2012(cfg, elec, > headshape); > > -- > Ignasi Sols > Postdoctoral Fellow > Department of Psychology > New York University > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From assaf.harel at wright.edu Wed Oct 31 17:43:59 2018 From: assaf.harel at wright.edu (Harel, Assaf) Date: Wed, 31 Oct 2018 16:43:59 +0000 Subject: [FieldTrip] 20th International Symposium on Aviation Psychology Call for Proposals : New Proposal Submission Deadline Message-ID: <95AD2984-3DA8-4F45-9664-8BE71009B580@wright.edu> 20th International Symposium on Aviation Psychology Call for Proposals The 20th ISAP will be held in Dayton, Ohio, U.S.A., May 7-10, 2019 (Tuesday – Friday). Proposal Submission is Live! New Proposal Submission Deadline: November 9, 2018 Proposals are sought for posters, papers, symposium and panel sessions, and workshops. Any topic related to the field of aviation psychology is welcomed. Topics on human performance problems and opportunities within aviation systems, and design solutions that best utilize human capabilities for creating safe and efficient aviation systems are all appropriate. Any basic or applied research domain that generalizes from or to the aviation domain will be considered. Students are especially encouraged to participate in the The Stanley Nelson Roscoe Best Student Paper Competition. Visit http://aviation-psychology.org for more information. Pamela Tsang and Michael Vidulich (Symposium Co-Chairs) Contact isap2019 at isap.wright.edu for any questions. -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 1 03:58:21 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 30 Sep 2018 21:58:21 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster Message-ID: Dear FieldTrip community, I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From L.M.Talamini at uva.nl Mon Oct 1 13:52:49 2018 From: L.M.Talamini at uva.nl (Talamini, Lucia) Date: Mon, 1 Oct 2018 11:52:49 +0000 Subject: [FieldTrip] Job post for the Fieldtrip mailing list Message-ID: Postdoctoral position on Sleep and Memory University of Amsterdam Job description We are looking for a postdoctoral scientist to join our research on manipulation of sleep and memories using EEG-guided neurostimulation. You will use a well-validated and highly flexible closed-loop neurostimulation (CLNS) method, developed at the UvA-Sleep and Memory lab. We have recently using this method deepen sleep and that to boost or depress individual memories. Responsibilities will also involve co-supervision of interns and junior investigators working on other CLNS projects. The lab The research will be executed at the UvA’s state of the art Sleep and Memory Lab, embedded in the Brain and Cognition Group, Department of Psychology, University of Amsterdam. The lab takes part in the interdisciplinary ABC (Amsterdam Brain and Cognitive Science Center). Requirements Applicants should have a PhD in (cognitive) neuroscience, bio-electrical signal analysis, or a related field. The candidate should have extensive experience in with EEG signal analysis (preferably involving methods development and application) and strong programming skills, e.g. in MATLAB and/or Python. Excellent scientific writing skills in English are required and some level of IT and engineering know-how will be considered a plus. Finally, flexibility with regard to working hours is expected in view of the type of research. Appointment Initial appointment will be for approximately 10 months, in case of full time employment. Part-time employment can be discussed. The appointment may be extended using expected ulterior funding. The salary will be in accordance with the university regulations for academic personnel (Collective Labor Agreement Dutch Universities). Job application For further information about this position please contact Dr. L.M. Talamini (phone: +31 6 4764 5932; e-mail: L.M.Talamini at uva.nl). Please send your application letter, curriculum vitae, a transcript of academic grades and courses, a proof of writing (e.g. a research publication) and the names and contact information of two persons willing to provide reference information to Lucia Talamini (L.M.Talamini at uva.nl). Or submit them through this link: http://www.uva.nl/en/content/vacancies/2018/09/18-552-postdoctoral-position-sleep-and-memory.html?a Application closing date: October15th 2018. -------------- next part -------------- An HTML attachment was scrubbed... URL: From L.M.Talamini at uva.nl Mon Oct 1 14:30:07 2018 From: L.M.Talamini at uva.nl (Talamini, Lucia) Date: Mon, 1 Oct 2018 12:30:07 +0000 Subject: [FieldTrip] Postdoctoral position on Sleep and Memory Message-ID: Postdoctoral position on Sleep and Memory University of Amsterdam Job description We are looking for a postdoctoral scientist to join our research on manipulation of sleep and memories using EEG-guided neurostimulation. You will use a well-validated and highly flexible closed-loop neurostimulation (CLNS) method, developed at the UvA-Sleep and Memory lab. We have recently used this method to deepen sleep and to boost or depress individual memories. Responsibilities will also involve co-supervision of interns and junior investigators working on other CLNS projects. The lab The research will be executed at the UvA’s state of the art Sleep and Memory Lab, (Brain and Cognition Group, Dept. of Psychology, University of Amsterdam). The lab takes part in the interdisciplinary ABC (Amsterdam Brain and Cognitive Science Center). Requirements Applicants should have a PhD in (cognitive) neuroscience, bio-electrical signal analysis, or a related field. The candidate should have extensive experience in with EEG signal analysis (preferably involving methods development and application) and strong programming skills, e.g. in MATLAB and/or Python. Excellent scientific writing skills in English are required and some level of IT and engineering know-how will be considered a plus. Finally, flexibility with regard to working hours is expected in view of the type of research. Appointment Initial appointment will be for approximately 10 months, in case of full time employment. Part-time employment can be discussed. The appointment may be extended using expected ulterior funding. The salary will be in accordance with the university regulations for academic personnel (Collective Labor Agreement Dutch Universities). Job application For further information about this position please contact Dr. L.M. Talamini (phone: +31 6 4764 5932; e-mail: L.M.Talamini at uva.nl). Please send your application letter, curriculum vitae, a transcript of academic courses and grades, a proof of writing (e.g. a research publication) and the names and contact information of two persons willing to provide reference information to Lucia Talamini (L.M.Talamini at uva.nl). Or apply through this link: http://www.uva.nl/en/content/vacancies/2018/09/18-552-postdoctoral-position-sleep-and-memory.html?a Application closing date: October15th 2018. -------------- next part -------------- An HTML attachment was scrubbed... URL: From narun.pornpattananangkul at nih.gov Tue Oct 2 05:07:23 2018 From: narun.pornpattananangkul at nih.gov (Pornpattananangkul, Narun (NIH/NIMH) [F]) Date: Tue, 2 Oct 2018 03:07:23 +0000 Subject: [FieldTrip] Postdoctoral position at the National Institute of Mental Health, National Institute of Health Message-ID: The Mood Brain and Development Unit (MBDU) at the US National Institute of Health (NIH) led by Dr Argyris Stringaris is looking for a post-doc. The focus of the MBDU is on how reward processing aberrations impact on human mood and may lead to pathologies such as depression. There is a lot of promise that reward processing abnormalities underlie psychiatric disorders. Yet, only if causal links reward processing aberrations and mood still are elucidated, will they form the basis of targeted treatment design. To resolve these questions, we use a variety of tools, benefitting from the unique scientific environment and resources that the NIH offers. In particular, we use longitudinal imaging protocols at three different time scales, work on methodological development of resting-state and task-based imaging data fusion, leverage treatment designs (such as psychological or pharmacological therapies) and develop closed loop devices for mood manipulation. We are a team that works closely together, has many regular science and social meetings and collaborates extensively with others in and out of NIH. Candidates with a strong background in MRI and at least an interest in neurophysiological methods such as EEG/MEG, as well as a keen interest in methodology and computational modeling will be most suited for the job. Advanced coding in R, Python, Matlab or Shell scripts is a requirement. Cognitive neuroscientists, engineers and other candidates with strong numerical and computational skills are particularly encouraged to apply. The postdoctoral community at the NIH is large (approximately 4,000) strong and vibrant. Trainees come from across the U.S. and around the world. Salary for this position is defined by type of training and years of experience. https://www.training.nih.gov/postdoctoral_irta_stipend_ranges Benefits include health insurance for the trainee and his/her family, and support for coursework related to the trainee's research and travel to meetings is often available. https://www.training.nih.gov/programs/postdoc_irp The NIH is among the largest and best communities of neuroimaging researchers in the world, with opportunities to collaborate with leaders in the field of fMRI, MEG, machine learning, computational psychiatry and neuromodulation. Our group has access to high performance computing, has been allocated timeslots to use fMRI and has our own in-patient unit with full-time clinicians. See more information here https://www.nimh.nih.gov/labs-at-nimh/research-areas/clinics-and-labs/edb/mbdu/index.shtml For more information, please write with CV and expression of interest to Argyris Stringaris: argyris.stringaris at nih.gov and Narun Pornpattananangkul: narun.pornpattananangkul at nih.gov . The National Institutes of Health is an equal opportunity employer. -------------- next part -------------- An HTML attachment was scrubbed... URL: From michelic72 at gmail.com Tue Oct 2 10:31:30 2018 From: michelic72 at gmail.com (Cristiano Micheli) Date: Tue, 2 Oct 2018 10:31:30 +0200 Subject: [FieldTrip] Fwd: please circulate In-Reply-To: References: <8621bbfc-7ad6-af42-629c-604c5237452a@gmail.com> <73582e5b-25bd-8af9-7d70-a49d7e4233fc@gmail.com> <90669498-b492-d745-cdfa-d7cb0b85e1ff@gmail.com> <6ff95a55-5ac0-627b-6fc8-2ad8b26b5692@gmail.com> Message-ID: ---------- Forwarded message --------- From: Jean-Claude Dreher Date: Tue, Oct 2, 2018 at 10:29 AM Subject: please circulate To: Cristiano Micheli Dear Cristiano, Could you please post this add on the fieldtrip website? Thanks a lot, JC The Institute of Cognitive Science, Lyon, France is inviting applications for a postdoctoral position to study social decision making and reward processing using fMRI in healthy subjects and intracranial recordings in patients with epilepsy. The institute of Cognitive Science is located in Lyon, a thriving universitary city. The institute hosts an interdisciplinary community with access to several brain imaging facilities, such as one research-dedicated siemens scanner, one MEG, one TEP, EEG, TMS and other useful resources. Candidates, preferably with model-based fMRI experience, should send their CV, statement of research interests, and representative publications to Jean-Claude Dreher ( https://dreherteam.wixsite.com/neuroeconomics), Email: dreher at isc.cnrs.fr -- Dr Jean-Claude Dreher, Research director, CNRS UMR 5229 Neuroeconomics group, Reward and decision making Institut des Sciences Cognitives Marc Jeannerod, 67 Bd Pinel, 69675 Bron, France tel: 00 334 37 91 12 38 fax: 00 334 37 91 12 10 https://dreherteam.wixsite.com/neuroeconomicshttp://cnc.isc.cnrs.fr/en/research/neuroeco-en/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From michelic72 at gmail.com Tue Oct 2 11:19:58 2018 From: michelic72 at gmail.com (Cristiano Micheli) Date: Tue, 2 Oct 2018 11:19:58 +0200 Subject: [FieldTrip] please circulate - Postdoc Lyon Message-ID: Dear FieldTrip friends, please circulate this post from Lyon. Cris Micheli ==================== The Institute of Cognitive Science, Lyon, France is inviting applications for a postdoctoral position to study social decision making and reward processing using fMRI in healthy subjects and intracranial recordings in patients with epilepsy. The institute of Cognitive Science is located in Lyon, a thriving universitary city. The institute hosts an interdisciplinary community with access to several brain imaging facilities, such as one research-dedicated siemens scanner, one MEG, one TEP, EEG, TMS and other useful resources. Candidates, preferably with model-based fMRI experience, should send their CV, statement of research interests, and representative publications to Jean-Claude Dreher ( https://dreherteam.wixsite.com/neuroeconomics), Email: dreher at isc.cnrs.fr -- Dr Jean-Claude Dreher, Research director, CNRS UMR 5229 Neuroeconomics group, Reward and decision making Institut des Sciences Cognitives Marc Jeannerod, 67 Bd Pinel, 69675 Bron, France tel: 00 334 37 91 12 38 fax: 00 334 37 91 12 10 https://dreherteam.wixsite.com/neuroeconomicshttp://cnc.isc.cnrs.fr/en/research/neuroeco-en/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From junseok.kim at mail.utoronto.ca Tue Oct 2 19:31:40 2018 From: junseok.kim at mail.utoronto.ca (Junseok Kim) Date: Tue, 2 Oct 2018 17:31:40 +0000 Subject: [FieldTrip] Issue using ft_sourceanalysis Message-ID: Hello, I have just recently updated to the latest version of FieldTrip and I ran into a probelm I have never ran into before while using ft_sourceanalysis Reference to non-existent field 'dimord'. Error in ft_sourceanalysis (line 788) if strcmp(data.dimord, 'chan_time') in which the input was cfg = []; cfg.method = 'lcmv'; cfg.grid = source_model; cfg.headmodel = vol; cfg.lcmv.keepfilter = 'yes'; cfg.grad = dataica.grad; lcmvsource = ft_sourceanalysis(cfg, timelock); and the timelock analysis was performed by cfg = []; cfg.channel = 'meggrad'; cfg.covariance = 'yes'; cfg.covariancewindow = 'all'; cfg.vartrllength = 2; cfg.keeptrials = 'yes'; timelock = ft_timelockanalysis(cfg, dataica); This was performed on MATLAB 2015b on Neuromag resting state data divided into 10s epochs If anyone has run into this issue or has a solution for it, let me know. Cheers Andrew -------------- next part -------------- An HTML attachment was scrubbed... URL: From ajesse at psych.umass.edu Wed Oct 3 01:05:31 2018 From: ajesse at psych.umass.edu (Alexandra Jesse) Date: Tue, 2 Oct 2018 19:05:31 -0400 Subject: [FieldTrip] Captrak Message-ID: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> hi! I’m thinking about buying the Brain Vision’s Captrak system. Brain Vision recommended to buy BESA to analyze the data but I’d rather stick with Fieldtrip. Can that data easily be read into Fieldtrip? Thank you, Alexandra Sent from my iPhone From alik.widge at gmail.com Wed Oct 3 14:17:40 2018 From: alik.widge at gmail.com (Alik Widge) Date: Wed, 3 Oct 2018 07:17:40 -0500 Subject: [FieldTrip] Captrak In-Reply-To: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> References: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> Message-ID: We analyze data from BrainVision in MNE-Python, and if it fits into that cross-platform format, I am confident it can be used in FieldTrip also. The CapTrak is a pretty useful system. Alik Widge alik.widge at gmail.com (206) 866-5435 On Tue, Oct 2, 2018 at 6:24 PM Alexandra Jesse wrote: > hi! > I’m thinking about buying the Brain Vision’s Captrak system. Brain Vision > recommended to buy BESA to analyze the data but I’d rather stick with > Fieldtrip. Can that data easily be read into Fieldtrip? > Thank you, > Alexandra > > Sent from my iPhone > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From zangyl1983 at gmail.com Thu Oct 4 05:41:19 2018 From: zangyl1983 at gmail.com (Yunliang Zang) Date: Thu, 04 Oct 2018 12:41:19 +0900 Subject: [FieldTrip] Problem in running tutorial of preprocessing and analysis of spike data Message-ID: Hello, I am Yunliang Zang, a postdoc at OIST. I just began using fieldtrip. I met a problem when running the tutorial from the link: http://www.fieldtriptoolbox.org/tutorial/spike When reading event information by event = ft_read_event('p029_sort_final_01.nex'), there are 361626 events instead of 37689 events as shown in the tutorial. I checked further and found in the outputted structure called event, there are a lot of empty elements for the field value, and their type are Event002 instead of Strobed. I think ft_read_event does not function correctly in my case. This function is too long for me to check further. My Matlab version is MATLAB_R2018a. Does anybody have a similar problem and know how to fix it? Best, Yunliang -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Thu Oct 4 21:10:14 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Thu, 4 Oct 2018 12:10:14 -0700 Subject: [FieldTrip] ft_prepare_mesh ERROR In-Reply-To: References: <637223E2-7994-40FF-9D5A-A5C24E5B9374@neuro.med.kyushu-u.ac.jp> Message-ID: Just in case anyone experiences the same or a related issue in the future, Toshiki and I solved the problem for him, adjusting ft_prepare_mesh_cortexhull in the main repository . Simply re-download/pull if you had the same issue. Arjen On Fri, Sep 28, 2018 at 10:50 PM Arjen Stolk wrote: > Dear Toshiki, > > From that error, it seems your version of Freesurfer is referencing > libraries that do not exist while executing the *mris_fill *command. To > rule out that the issue is due to the call to *mris_fill* being made from > within the matlab environment as *prepare_mesh_cortexhull* does, you > could try to call *mris_fill* directly from within a terminal and see if > it replicates, as follows: > > export FREESURFER_HOME=/Applications/freesurfer > > source $FREESURFER_HOME/SetUpFreeSurfer.sh > > mris_fill -c -r 1 path_to_freesurfer/surf/lh.pial tmp.filled.mgz > > > If that reproduces the error, it might be worth consulting the > Freesurfer development team concerning this. A similar issue was reported > here > , > but it doesn't look like a fix has been suggested. Alternatively, you > could try a different version of Freesurfer for creating the cortical hull > (freesurfer-Darwin-lion-stable-pub-v5.3.0 runs fine on my mac). Hope > this helps, > Arjen > > > On Sep 25, 2018, at 12:13 AM, 岡留敏樹 > wrote: > > Hello fieldtrip experts, > > when trying to run ‘ft_prepare_mesh’ command from fieldtrip with MATLAB R2018a, I run into the error below. > > I was preprocessing MRI date according with the paper ’Nature Protocol 2018; 13: 1699-1723. http://doi.org/10.1038/s41596-018-0009-6 ‘. > > I’m running this on a iMac, macOS High Sierra 10.13.6, with fieldtrip-20180909, freesurfer-Darwin-OSX-stable-pub-v6.0.0-2beb96c. > > From past archives, I think this is because of SIP. So I disabled SIP of OSX, but I run into same errors. > I tried different subjects, which caused similar errors. > > Any ideas would be greatly appreciated, thank you! > > > [CODE] > > cfg = []; > > cfg.method = ‘cortexhull’; > > cfg.headshape = ; > > cfg.fshome = ; > > hull_lh = ft_prepare_mesh (cfg); > > > [ERROR] > > dyld: lazy symbol binding failed: Symbol not found: ___emutls_get_address > > Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib > Expected in: /usr/lib/libSystem.B.dylib > > dyld: Symbol not found: ___emutls_get_address > Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib > Expected in: /usr/lib/libSystem.B.dylib > > mris_fill -c -r 1 /Users/okadometoshiki/Desktop/SubjectUCI29/freesurfer/surf/lh.pial /private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz: Aborted > reading filled volume... > gunzip: can't stat: /private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz (/private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz.gz): No such file or directory > ERROR: problem reading fname > > > > ======== > > Toshiki Okadome > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From seunggoo.kim at duke.edu Thu Oct 4 22:44:03 2018 From: seunggoo.kim at duke.edu (Seung Goo Kim, Ph.D.) Date: Thu, 4 Oct 2018 20:44:03 +0000 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test Message-ID: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Dear FT-ers, I (also) want to know how we could perform one-sample T-test using the cluster-based permutation test to compute and correct p-values. I noticed that many people asked about this for a long time (https://mailman.science.ru.nl/pipermail/fieldtrip/2018-August/012314.html). I think it's because the one-sample T-test after baseline correction has been really a common way to analyze ERP/F data. Furthermore, in many functional studies (including fMRI studies), the first question at the second-level is if the effect of a certain contrast is non-zero over multiple subjects, thus one-sample T-test is a really natural thing to do. It is possible to compare baseline-vs-activation trials at the first level, although it could be a problem if the baseline period is too short compared to the activation period, which is not really uncommon (for example, it would be rather common to have short inter-stimulus-interval than the stimulus length itself). But at the second level, I have no idea how we could do that. I know that a permutation distribution can be generated by randomly flipping signs for one-sample T-test in permutation test, so I added another resampling method to flip signs of the half of trials in resampledesign() but the result was quite different from the two-sample t-test comparing baseline vs activation trials. Could be there any specific reason for flipping signs would not work for cluster-based correction? One possible reason I could think of would be that the noise distribution is not actually symmetric, thus permuting labels and flipping signs create different permutation distributions. Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? Best, -- Seung-Goo ("SG") Kim, PhD Postdoctoral Research Associate O-Lab, Department of Psychology & Neuroscience, Duke University Postal: 308 Research Drive, Durham, NC 27708, USA Email: solleo at gmail.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Fri Oct 5 10:07:18 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Fri, 5 Oct 2018 10:07:18 +0200 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test In-Reply-To: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> References: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Message-ID: Dear SG, Rather than go into the whole one-sample T test business again, I will respond to one aspect of your question which I think might be useful. > but the result was quite different from the two-sample t-test comparing baseline vs activation trials. I don't know exactly how you were doing the baseline vs activation test, but I'll note two things here. First, you will typically not want to do a *two-sample* test, but a *paired-sample* test; i.e. each unit of observation (trial or subject) has both a baseline and an activation period, and it's the paired difference that matters. Second, you will want to test the activation period against the *mean* across the entire baseline period (since we typically assume a stationary baseline). If you were to simply do (1) select baseline window as condition A; (2) select activation window as condition B; (3) compare the two using cluster stats; then the statistic would be comparing each time point for the activation period against the matching time point in the baseline. So basically, one way of doing activation versus baseline cluster stats is to average the baseline window across time (probably repmat() the mean over time again) and then use paired statistics against the activation window. This should work at first or second level. Hope that helps! Best, Eelke > > Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? > > Best, > -- > Seung-Goo ("SG") Kim, PhD > > Postdoctoral Research Associate > O-Lab, Department of Psychology & Neuroscience, Duke University > Postal: 308 Research Drive, Durham, NC 27708, USA > Email: solleo at gmail.com > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From seunggoo.kim at duke.edu Fri Oct 5 19:58:39 2018 From: seunggoo.kim at duke.edu (Seung Goo Kim, Ph.D.) Date: Fri, 5 Oct 2018 17:58:39 +0000 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test In-Reply-To: References: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Message-ID: <562AE551-038E-4BA8-9ED0-6BA7124F9115@duke.edu> Dear Eelke, Thank you for an informative response. I just wonder how the discussion around the one-sample T-test finally has ended and why it was decided not to be included. I think you're right that it should be tested with the paired T-test. I thought the autocorrelation in MEG data should be minimal, I think it would make more sense to pair them, especially the signal was low-pass filtered. I think if you create a flat timeseries with the mean values of each baseline period as a "baseline trial" and do a paired t-test, the result should be identical to flipping signs of baseline-corrected activation trials. During the permutation, if the labels are not swapped, then the paired difference at one trial-pair would be y - x0 (y is a timeseries in an activation period; x0 is the mean of the baseline period). If the labels are swapped, then the difference would be x0 - y. Since baseline-corrected activation trial is also y - x0, this is equivalent to flipping signs of the corrected activation trials. But I'm not sure if I really want to use only the mean of the baseline period because then it would discard underlying noise structure in the baseline period. But also because the baseline period is not evoked by any stimuli of interest, we don't assume any sample-by-sample correspondence between the baseline and activation periods. So it feels a bit strange to do so too. Best, -SG On 2018-10-05, at 04:07, Eelke Spaak > wrote: Dear SG, Rather than go into the whole one-sample T test business again, I will respond to one aspect of your question which I think might be useful. but the result was quite different from the two-sample t-test comparing baseline vs activation trials. I don't know exactly how you were doing the baseline vs activation test, but I'll note two things here. First, you will typically not want to do a *two-sample* test, but a *paired-sample* test; i.e. each unit of observation (trial or subject) has both a baseline and an activation period, and it's the paired difference that matters. Second, you will want to test the activation period against the *mean* across the entire baseline period (since we typically assume a stationary baseline). If you were to simply do (1) select baseline window as condition A; (2) select activation window as condition B; (3) compare the two using cluster stats; then the statistic would be comparing each time point for the activation period against the matching time point in the baseline. So basically, one way of doing activation versus baseline cluster stats is to average the baseline window across time (probably repmat() the mean over time again) and then use paired statistics against the activation window. This should work at first or second level. Hope that helps! Best, Eelke Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? Best, -- Seung-Goo ("SG") Kim, PhD Postdoctoral Research Associate O-Lab, Department of Psychology & Neuroscience, Duke University Postal: 308 Research Drive, Durham, NC 27708, USA Email: solleo at gmail.com _______________________________________________ fieldtrip mailing list https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e= https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e= _______________________________________________ fieldtrip mailing list https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e= https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e= -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Sat Oct 6 01:25:37 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Fri, 5 Oct 2018 16:25:37 -0700 Subject: [FieldTrip] Hiring for Postdoc (and RA) - Intracranial research Message-ID: The newly opened Cognitive Neurophysiology Lab at UC Davis headed by Dr. Saez is hiring a postdoctoral researcher. Lab research projects will focus on the neurobiological basis of human decision-making behavior, using a combination of intracranial recordings in human patients, neuroeconomic tasks and computational modeling of behavior. Candidates must have a strong background in *in vivo* electrophysiological recordings and/or decision neuroscience. For more information, including details on how to apply, please visit their lab webpage . -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Sun Oct 7 13:52:18 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 7 Oct 2018 07:52:18 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster Message-ID: Dear FieldTrip community, sorry for sending another email out, but I am still having trouble with this issue. I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Mon Oct 8 09:04:37 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 8 Oct 2018 07:04:37 +0000 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster In-Reply-To: References: Message-ID: <7E583DEC-CBEE-4554-9FBC-E0847816C100@donders.ru.nl> Hi Paul, Currently, it’s not possible to use ft_freqstatistics for statistical inference of coherence in an all-by-all channel pair fashion. The reason is that clustering in 4D space is not implemented. What is possible, though, is to compare coherence across conditions between all channels, and a separate reference channel (e.g. EMG). This will probably require some tweaks to the input data (e.g. replace ‘labelcmb’, with ‘label’, and replace ‘dimord’ with ‘chan_freq’). Using one of the EEG channels as a ‘reference’ should be in principle possible as well (at least in terms of algorithm, whether interpretations based on inferential decisions make sense in this case, that’s up to the user). Best wishes, Jan-Mathijs On 7 Oct 2018, at 13:52, Paul Dhami > wrote: Dear FieldTrip community, sorry for sending another email out, but I am still having trouble with this issue. I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 8 20:49:46 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Mon, 8 Oct 2018 14:49:46 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster (Schoffelen, J.M. (Jan Mathijs)) Message-ID: Hi Jan-Mathijs, thank you very much for your response, it's greatly appreciated. If I approached it from an a priori perspective, and wanted test the coherency difference between two groups for just a pair of electrodes, where one is the reference electrode (e.g. comparing the coherency values of F3 - F4 between two groups), would this be possible? My understanding is that it would become just as any other 3D matrix, with freq-time-subjects becoming the dimensions after squeezing the pair dimension. Would this then be something possible to run freqstatistics on with cluster analysis? Thank you again, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From sauppe.s at gmail.com Tue Oct 9 11:49:56 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Tue, 9 Oct 2018 11:49:56 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? Message-ID: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Dear list members, there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? Thanks a lot! — Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From S.Arana at donders.ru.nl Tue Oct 9 12:55:01 2018 From: S.Arana at donders.ru.nl (Arana, S.L. (Sophie)) Date: Tue, 9 Oct 2018 10:55:01 +0000 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: <1539082501488.92451@donders.ru.nl> Hi Sebastian, option 'normchange' will give you the normalized change to baseline (data-meanbsl)/(data+meanbsl) You can see the formulas for the different options at the end of the ft_freqbaseline code. I am not sure in which situation you would prefer this option over 'relchange', maybe someone else knows? Best, Sophie ________________________________ From: fieldtrip on behalf of Sebastian Sauppe Sent: Tuesday, October 9, 2018 11:49 AM To: fieldtrip at science.ru.nl Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? Dear list members, there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like "absolute" or "db". However, I wasn't able to find out what "normchange" does. Does anyone of you know (where to find information about this)? Thanks a lot! - Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Tue Oct 9 13:12:35 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Tue, 9 Oct 2018 13:12:35 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: Dear Sebastian, 'normchange' will compute (a-b) / (a+b). The best reference on what the options do is of course the source code itself; see here: https://github.com/fieldtrip/fieldtrip/blob/ce5bdd3d6433dbc4ca600eb127f369f3917c02cd/ft_freqbaseline.m#L205 Cheers, Eelke On Tue, 9 Oct 2018 at 11:49, Sebastian Sauppe wrote: > > Dear list members, > > there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? > > Thanks a lot! > — Sebastian > > ----------- > Dr. Sebastian Sauppe > Department of Comparative Linguistics, University of Zurich > Homepage: https://sites.google.com/site/sauppes/ > Twitter: @SebastianSauppe > Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ > ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe > ORCID ID: http://orcid.org/0000-0001-8670-8197 > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From david.schubring at uni-konstanz.de Tue Oct 9 13:27:15 2018 From: david.schubring at uni-konstanz.de (David Schubring) Date: Tue, 9 Oct 2018 13:27:15 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> Dear Sebastian, more information can be found in the code of ft_freqbaseline, where "data" is your poststimulus and "meanVals" is your baseline and normchange does the following: data = (data - meanVals) ./ (data + meanVals) Hope that helps. Best, David PS: The other baseline types do the following: if (strcmp(baselinetype, 'absolute')) data = data - meanVals; elseif (strcmp(baselinetype, 'relative')) data = data ./ meanVals; elseif (strcmp(baselinetype, 'relchange')) data = (data - meanVals) ./ meanVals; elseif (strcmp(baselinetype, 'normchange')) || (strcmp(baselinetype, 'vssum')) data = (data - meanVals) ./ (data + meanVals); elseif (strcmp(baselinetype, 'db')) data = 10*log10(data ./ meanVals); Am 09.10.2018 um 11:49 schrieb Sebastian Sauppe: > Dear list members, > > there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? > > Thanks a lot! > — Sebastian > > ----------- > Dr. Sebastian Sauppe > Department of Comparative Linguistics, University of Zurich > Homepage: https://sites.google.com/site/sauppes/ > Twitter: @SebastianSauppe > Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ > ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe > ORCID ID: http://orcid.org/0000-0001-8670-8197 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From mjones at sanantoniocounseling.net Wed Oct 10 04:54:46 2018 From: mjones at sanantoniocounseling.net (Mark Jones) Date: Tue, 9 Oct 2018 21:54:46 -0500 Subject: [FieldTrip] Moving means Message-ID: <5152EC6D-5544-4406-BC6B-336B3729B983@sanantoniocounseling.net> I have a time frequency series of continuous EEG, segmented in 10 second trials (which gives me 0.1hz resolution). How can I create moving means of the data based on a rolling average of 10 seconds that will update once a second? Thanks, Mark Jones San Antonio, Texas, USA From sauppe.s at gmail.com Wed Oct 10 09:47:38 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Wed, 10 Oct 2018 09:47:38 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> Message-ID: <2BAC407B-5208-49E3-A4F5-A0DA0D7E326B@gmail.com> Dear David, thanks a lot, that clarifies what the different methods do. — Sebastian > Am 09.10.2018 um 13:27 schrieb David Schubring : > > Dear Sebastian, > > more information can be found in the code of ft_freqbaseline, where "data" is your poststimulus and "meanVals" is your baseline and normchange does the following: > > data = (data - meanVals) ./ (data + meanVals) > > Hope that helps. > > Best, > David > > PS: The other baseline types do the following: > > if (strcmp(baselinetype, 'absolute')) > data = data - meanVals; > elseif (strcmp(baselinetype, 'relative')) > data = data ./ meanVals; > elseif (strcmp(baselinetype, 'relchange')) > data = (data - meanVals) ./ meanVals; > elseif (strcmp(baselinetype, 'normchange')) || (strcmp(baselinetype, 'vssum')) > data = (data - meanVals) ./ (data + meanVals); > elseif (strcmp(baselinetype, 'db')) > data = 10*log10(data ./ meanVals); > > > Am 09.10.2018 um 11:49 schrieb Sebastian Sauppe: >> Dear list members, >> there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? >> Thanks a lot! >> — Sebastian >> ----------- >> Dr. Sebastian Sauppe >> Department of Comparative Linguistics, University of Zurich >> Homepage: https://sites.google.com/site/sauppes/ >> Twitter: @SebastianSauppe >> Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ >> ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe >> ORCID ID: http://orcid.org/0000-0001-8670-8197 >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > -- > Dr. David Schubring > > General & Biological Psychology > University of Konstanz > Room C524 > P.O. Box 36 > 78457 Konstanz > > Phone: +49-(0)7531-88-5350 > Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From sauppe.s at gmail.com Wed Oct 10 10:21:50 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Wed, 10 Oct 2018 10:21:50 +0200 Subject: [FieldTrip] Question on baselining methods (absolute vs db) Message-ID: Dear FieldTrip list members, I’ve got a question on the different methods available in ft_freqbaseline. I have TFA data from an experiment where participants saw pictures and reacted to them. There is a pre-trial period (-1000 to 0 ms) during which the fixate a fixation cross and a trial period (0 to 1200 ms) during which they see the picture. I used mtmconvol in ft_freqstatistics to get the power. The frequencies are 4 to 20 Hz and I used 3 cycles (cfg.t_ftimwin = 3./cfg.foi). Then I want to baseline the data for plotting and analysis. As baseline period I chose -500 to -200 ms. (But using other baseline periods don’t change the picture.) When I use an absolute baseline, the activity during the baseline period is around 0 and during the trial there are increases and decreases (i.e. positive and negative values). So this looks like one would expect. When I use a dB baseline, however, the activity during the baseline period is not around 0, but rather around -1 to -2 dB. During the trial, I also get values around -1 to -3 dB. This is the command: cfg = []; cfg.baseline = [-0.5 -0.2]; % baseline period = -500 to -200 ms, relative to stimulus onset cfg.baselinetype = 'db'; cfg.parameter = 'powspctrm'; freq_data_baselined = ft_freqbaseline(cfg, freq_data); What could be the reason for this discrepancy between absolute and dB baselines? Thanks a lot for your help already in advance! Regards, Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From wwumedstanford at gmail.com Wed Oct 10 20:23:38 2018 From: wwumedstanford at gmail.com (Wei Wu) Date: Wed, 10 Oct 2018 11:23:38 -0700 Subject: [FieldTrip] Assistant, associate or full professor positions @ Department of Psychiatry and Behavioral Sciences, Stanford University Message-ID: The Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine is seeking new full-time faculty members at the rank of Assistant, Associate, or full Professor in the Medical Center Line. These are positions for mental health clinician scientists who will be based in the Department of Psychiatry and Behavioral Sciences, or at the Veterans Affairs Palo Alto Health Care System with presence in the Departmental programs on the Stanford campus. The chosen candidates will be expected to conduct scholarly research and teaching in the areas of psychiatry or psychology in adult and/or child and adolescent populations across several areas of programmatic need, including mood disorders, anxiety disorders, eating disorders, addiction medicine, public mental health and/or special patient populations, among others. Candidates are required to have a proven track record of success in producing high-quality scholarly work, in addition to excellence in teaching and established clinical expertise in their respective field. Possible candidates could include, but are not limited to, psychiatrists, psychologists, neuropsychiatrists, and neuropsychologists. Physician applicants must have a medical degree or equivalent degree, completed training in General Psychiatry, be board-certified in General Psychiatry, and possess or be fully eligible for a California medical license. Physicians interested in working with children must hold board certification in child psychiatry. Clinical psychologist applicants must have a doctoral degree in psychology or equivalent degree, have completed an APA-approved internship, and possess or be fully eligible for licensure as a psychologist in California. The major criteria for appointment for faculty in the Medical Center Line shall be excellence in the overall mix of clinical care, clinical teaching, scholarly activity that advances clinical medicine, and institutional service appropriate to the programmatic need the individual is expected to fulfill. Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford also welcomes applications from others who would bring additional dimensions to the University’s research, teaching and clinical missions. Interested candidates should send a copy of their curriculum vitae, a brief letter outlining their experiences and interests and the names of three references via e-mail only to: Search Co-Chairs: Natalie Rasgon, MD, Ph.D., and Alan Louie, M.D. c/o Heather Kenna Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine 401 Quarry Road Stanford, CA 94305 Phone: (650) 724-0521 Email: hkenna at stanford.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From fda.bolanos at gmail.com Thu Oct 11 20:19:47 2018 From: fda.bolanos at gmail.com (=?UTF-8?Q?Fernanda_Bola=C3=B1os?=) Date: Thu, 11 Oct 2018 13:19:47 -0500 Subject: [FieldTrip] Problems running FieldTrip Message-ID: Hi! My name is Fernanda and I am using FieldTrip for a school project. I download the most recent version, but I’m having problems to run the code. It happens with each and every code I make. I followed the instructions on how to install the toolbox. I placed the fieldtrip carpet at my (C:) folder and I added the startup.m at the C:\Program Files\MATLAB\R2017b\toolbox\local. May have some help on how to solve the problem, please? Best regards, Fernanda Bolaños -- Ma. Fernanda Bolaños De Regil -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Fri Oct 12 09:29:00 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Fri, 12 Oct 2018 09:29:00 +0200 Subject: [FieldTrip] Problems running FieldTrip In-Reply-To: References: Message-ID: Hi Fernanda, What sort of errors are you getting? In general, make sure you add the FieldTrip folder to your Matlab path and run ft_defaults() in order to check whether everything is OK. Cheers, Eelke On Thu, 11 Oct 2018 at 20:19, Fernanda Bolaños wrote: > Hi! My name is Fernanda and I am using FieldTrip for a school project. > > > > I download the most recent version, but I’m having problems to run the > code. It happens with each and every code I make. I followed the > instructions on how to install the toolbox. > > > > I placed the fieldtrip carpet at my (C:) folder and I added the startup.m > at the C:\Program Files\MATLAB\R2017b\toolbox\local. > > > > May have some help on how to solve the problem, please? > > > > Best regards, > > > > Fernanda Bolaños > > -- > Ma. Fernanda Bolaños De Regil > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Alexander_Nakhnikian at hms.harvard.edu Fri Oct 12 18:34:19 2018 From: Alexander_Nakhnikian at hms.harvard.edu (Nakhnikian, Alexander) Date: Fri, 12 Oct 2018 16:34:19 +0000 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling Message-ID: Dear All, Apologies for the long post, I've tried to be as succinct as possible while describing my problem is sufficient detail. I've been trying to solve a problem with source modeling for sometime. I've found that distributed source estimates (MNE, sLORETA, eLORETA) are heavily biased towards the ventral temporal lobe. This is the case with multiple data sets analyzed using both built-in field trip functions and imaging kernels generated by my own code. It occurs in within group grand averages and statistical contrasts between controls and patients. I've confirmed that sLORETA and eLORETA are unbiased for noiseless data by filtering point sources through the resolution kernel. I'm working on a Mac running OS 10.11.6 and the latest version of Field Trip. I'm using a standard 10/10 electrode layout (no individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) and Field Trip's template BEM. The forward model is restricted to the cortical mantle (I've had similar problems with whole brain forward models as well). I recently ran PCA at the source level to explore the issue. The data were collecting during quiet rest and bandpassed to isolate a peak that accounts for a significant difference between controls and patients at the sensor level. The imaging kernel was applied to the sensor spectral matrix. To obtain the PCs, I analyzed the covariance of power among voxels. The rank of the voxel covariance matrix was 31 with the first 3 eigenvalues account for approximately 95% of the variance. Interestingly, the spatial distribution of the first three principal components exhibited the same bias as the sensor data. When I reconstructed the source data omitting the first 3 components, the control-patient contrast localized to a collection of canonical DMN nodes. It seems extremely odd to me that removing so much of the information present in the original data returns a reasonable source-level contrast while the majority of variance is accounted for by what is clearly bias. I cannot complete this analysis and submit the results unless I can isolate the problem and correct it so I can run the analysis without reducing the rank of the source data. If anyone can speculate on possible reasons for this problem and/or potential solutions I would be grateful. Thank you, Alexander Alexander Nakhnikian, Ph.D. Research Investigator VA Boston Healthcare System Instructor in Psychiatry, Harvard Medical School -------------- next part -------------- An HTML attachment was scrubbed... URL: From kai.hwang at gmail.com Sat Oct 13 05:29:51 2018 From: kai.hwang at gmail.com (Kai Hwang) Date: Fri, 12 Oct 2018 22:29:51 -0500 Subject: [FieldTrip] postdoc position | cognitive neuroscience | University of Iowa Message-ID: The Hwang lab in the Psychological and Brain Sciences Department at the University of Iowa has a fully funded postdoc position available. The Hwang lab focuses on brain network mechanisms, cognitive control, and their developmental processes with a strong emphasis on the human thalamocortical system and neural oscillations. Our research utilizes multimodal neuroimaging (EEG and fMRI), TMS, and lesion studies in combination with network neuroscience approaches. For more info please see: https://kaihwang.github.io/ The lab is affiliated with the DeLTA Center and the Iowa Neuroscience Institute, which offers a collaborative research environment with access to research dedicated 3T and 7T MRI systems, TMS, EEG, neurosurgery patients, and a large lesion patient registry. Postdoc Candidate Qualifications - PhD in Psychology, Neuroscience, or other related disciplines. - Experience with neuroimaging (fMRI, EEG/MEG). - Strong Programming skills (Matlab or Python) The postdoc position is opened immediately until filled. To apply, please send a cover letter and CV to: kai-hwang at uiowa.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 15 04:37:52 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 14 Oct 2018 22:37:52 -0400 Subject: [FieldTrip] Attempt at cluster analysis on seed to whole brain coherency values Message-ID: Dear Fieldtrip, I have a dataset in which I'd like to compare, between controls and patients, their imaginary coherency values in a seed to whole brain manner. In other words, I would like to calculate the imaginary coherency between a single seed electrode and the rest of the remaining electrodes, and *ultimately use ft_freqstatistics' cluster analysis* to test for a significance difference between the groups' seed-to-whole-scalp coherency maps. >From my understanding, this would require a bit of hacking to implement this. I attempted to do so and describe what I did below, with the goal to eventually create something that would work with freqstatistics : - ran freq analysis with output as 'powandcsd' and method as 'mtmconvolv' - on the results of freqanalysis, ran ft_connectivityanalysis with cfg.method = 'coh' and cfg.complex = 'absimag' - with each subject's resulting structure from ft_connectivityanalysis, I first chose a seed of interest - I then found the indices of the 59 channel combinations in relation to the seed of interest in labelcmb - Using the indices, I then pruned/removed the remaining channel combinations of no interest from both the labelcmb and cohspctrm, reducing cohspctrm to a channel of interest (59) x frequency x time matrix (as in it only included values for the channel combinations of interest) - I rename the dimord as 'chan_freq_time' - create 'label' field with standard electrode labels - I then create powspctrm in my structure, which holds the exact same data as the cohspctrm (created powspctrm strictly for freqstatistics) - removed the fields of 'labelcmb' and 'cohspctrm' - because my powspctrm is missing the seed channel, I then inserted a matrix of ones with the appropriate dimensions into where it should be (e.g. if my seed of interest was channel FCZ, I would then insert into my matrix of ones into the 19th position of the powspctrm, thus shifting the latter matrices so now it becomes a matrix with 60 channels in the appropriate order) >From my understanding, the resulting structure of each subject should contain now the coherency values between the seed of interest and the rest of the electrodes in 'powspctrm'. I then used freq_statistics (in the standard way) with cluster correction to compare the coherency between groups, and from what I can tell with no errors popping up, it worked. I then interpreted the resulting clusters in a similar fashion as you would do for a typical frequency chan-freq-time analysis (instead of power, looking at coherency clusters now). My questions are: 1) In regards to implementation (assuming something like this can even be appropriately implemented), do things look okay? 2) Am I wrong in thinking that the cluster results of freq_statistics can be interpreted in a similar fashion as to a typical frequency chan-freq-time analsyis (just replacing power with coherency)? Sorry for the long-winded email, but any help would be greatly appreciated. Thank you, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From virginie.van.wassenhove at gmail.com Mon Oct 15 10:24:01 2018 From: virginie.van.wassenhove at gmail.com (Virginie van Wassenhove) Date: Mon, 15 Oct 2018 10:24:01 +0200 Subject: [FieldTrip] Fwd: [masters + postdoc fellowships] In-Reply-To: References: Message-ID: Dear colleagues, please, feel free to advertise for those interested in temporal cognition inside and outside the lab! Internships for masters: https://brainthemind.files.wordpress.com/2018/10/wildtimes_masters_add.pdf Postdoctoral fellowship: https://brainthemind.files.wordpress.com/2018/10/wildtimes_postdoc_add.pdf Best wishes, Virginie van Wassenhove *https://brainthemind.com/ * -------------- next part -------------- An HTML attachment was scrubbed... URL: From alexandra.korzeczek at med.uni-goettingen.de Mon Oct 15 12:55:43 2018 From: alexandra.korzeczek at med.uni-goettingen.de (Korzeczek, Alexandra) Date: Mon, 15 Oct 2018 10:55:43 +0000 Subject: [FieldTrip] ft_artifact_threshold not operating properly after resampling data Message-ID: Dear all, does anyone know if ft_resampledata is affecting EEG-data in an adverse manner? I’m asking because I’m having a similar problem wich was already described by Alexandrina Guran on Mon Mar 27 11:34:19 CEST 2017 (subject: Problem with downsampling / automatic artifact rejection) and wanted to ask if a solution has been found why this problem appears? The problem (black and bold) occurs while using the function ft_artifact_threshold: Example: Warning: the trial definition in the configuration is inconsistent with the actual data Warning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording threshold artifact scanning: trial 1 from 74 exceeds min-threshold threshold artifact scanning: trial 2 from 74 is ok threshold artifact scanning: trial 3 from 74 is ok threshold artifact scanning: trial 4 from 74 is ok threshold artifact scanning: trial 5 from 74 exceeds min-threshold threshold artifact scanning: trial 6 from 74 exceeds max-threshold Warning: data contains NaNs, no filtering or preprocessing applied I have backtracked why theses NANs occour, and it happens when ft_artifact_threshold (line 160) calls the function ft_fetch data. Within this function (line 167) the Matlab function unique is called. The Warning is posted when the variable utrl stays empty (line 169). Then in line 178 the data (variable dat), which until then are NANs, in the matrix data.trial are not converted into proper values. 167 - utrl = unique(trialnum); 168 - utrl(~isfinite(utrl)) = 0; 169 - utrl(utrl==0) = []; 170 - if length(utrl)==1 171 - ok = trialnum==utrl; 172 - smps = samplenum(ok); 173 - dat(:,ok) = data.trial{utrl}(chanindx,smps); 174 - else 175 - for xlop=1:length(utrl) 176 - ok = trialnum==utrl(xlop); 177 - smps = samplenum(ok); 178 - dat(:,ok) = data.trial{utrl(xlop)}(chanindx,smps); 179 - end 180 - end This problem is independent of my cfg configurations for ft_artifact threshold. However, as Alexandrina found out, it does not appear when I leave out my previous ft_resampledata step. So my question is: Can anybody explain, why resampling my data is giving this effect? And additionally if it is save to use resampling anyway (other ft functions as databrowser or rejectvisual do not seem to be affected by this resampling step – but maybe I just don’t notice that?). I’m using the EGI system with a 256 EEG cap. Here are my current steps before I call ft_artifact_threshold: Definetrial: cfg = []; cfg.dataformat = 'egi_mff_v2'; %uses the new format for egi files cfg.headerformat = 'egi_mff_v2'; cfg.eventformat = 'egi_mff_v2'; cfg.continuous = 'yes'; cfg.trialfun = 'ft_trialfun_general'; cfg.trialdef.eventtype = DIN4; cfg.trialdef.eventvalue = ''; % necessary for mff files. cfg.trialdef.prestim = 1; cfg.trialdef.poststim = 2,5; EEG_trldef_S1= ft_definetrial(cfg) 2x times Selectdata: cfg.trials = keep_trlinf;% specific rules for my experiment EEG_trls= ft_selectdata(cfg, EEG_trldef_S1); Preprocessing: EEG_trls.padding = Pad; EEG_trls.chantype = {'eeg'}; EEG_trls.channel=(1:257); EEG_trls.detrend = 'yes'; EEG_trls.trials = 'all'; EEG_trls.lpfilter ='yes'; EEG_trls.lpfreq = 60; EEG_preproc_1000Hz = ft_preprocessing (EEG_trls); Resample data cfg.resamplefs = 500; EEG_preproc = ft_resampledata(cfg, EEG_preproc_1000Hz); The difference between the data output of ft_preprocessing and ft_resampledata is that the variable sampleinfo is missing after ft_resampledata (see attached picture). Maybe this is the reason why ft_artifact_threshold is not working properly? I hope someone has an explanation? Thank you in advance, Alexandra _________________________________________________________________ Alexandra Korzeczek Wissenschaftliche Mitarbeiterin Klinik für Klinische Neurophysiologie Georg-August-Universität Göttingen Robert-Koch.Str. 40, 37075 Göttingen Tel. 0551- 39-65106 -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Matrix_ft_resampledata.png Type: image/png Size: 22979 bytes Desc: Matrix_ft_resampledata.png URL: From e.spaak at donders.ru.nl Mon Oct 15 13:22:13 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Mon, 15 Oct 2018 13:22:13 +0200 Subject: [FieldTrip] Attempt at cluster analysis on seed to whole brain coherency values In-Reply-To: References: Message-ID: Dear Paul, That all sounds good to me. Note that you could also have specified cfg.parameter = 'cohspctrm' in the call to ft_freqanalysis, so the renaming step to powspctrm was not strictly necessary. One thing you'll want to keep in mind when doing this is that imaginary part of coherency (ImC) is a biased quantity and depends on the number of trials. So comparing two groups statistically will not give you meaningful results if the number of data points for the two groups is unequal. It's worth having a look at this paper which goes into more details on this and related issues: Vinck, Oostenveld, Van Wingerden, Battaglia, & Pennartz. An Improved Index of Phase-Synchronization for Electrophysiological Data in the Presence of Volume-Conduction, Noise and Sample-Size Bias. NeuroImage 55, no. 4 (April 15, 2011): 1548–65. https://doi.org/10.1016/j.neuroimage.2011.01.055. Cheers, Eelke On Mon, 15 Oct 2018 at 04:37, Paul Dhami wrote: > > Dear Fieldtrip, > > I have a dataset in which I'd like to compare, between controls and patients, their imaginary coherency values in a seed to whole brain manner. In other words, I would like to calculate the imaginary coherency between a single seed electrode and the rest of the remaining electrodes, and ultimately use ft_freqstatistics' cluster analysis to test for a significance difference between the groups' seed-to-whole-scalp coherency maps. > > From my understanding, this would require a bit of hacking to implement this. I attempted to do so and describe what I did below, with the goal to eventually create something that would work with freqstatistics : > > ran freq analysis with output as 'powandcsd' and method as 'mtmconvolv' > on the results of freqanalysis, ran ft_connectivityanalysis with cfg.method = 'coh' and cfg.complex = 'absimag' > with each subject's resulting structure from ft_connectivityanalysis, I first chose a seed of interest > I then found the indices of the 59 channel combinations in relation to the seed of interest in labelcmb > Using the indices, I then pruned/removed the remaining channel combinations of no interest from both the labelcmb and cohspctrm, reducing cohspctrm to a channel of interest (59) x frequency x time matrix (as in it only included values for the channel combinations of interest) > I rename the dimord as 'chan_freq_time' > create 'label' field with standard electrode labels > I then create powspctrm in my structure, which holds the exact same data as the cohspctrm (created powspctrm strictly for freqstatistics) > removed the fields of 'labelcmb' and 'cohspctrm' > because my powspctrm is missing the seed channel, I then inserted a matrix of ones with the appropriate dimensions into where it should be (e.g. if my seed of interest was channel FCZ, I would then insert into my matrix of ones into the 19th position of the powspctrm, thus shifting the latter matrices so now it becomes a matrix with 60 channels in the appropriate order) > > From my understanding, the resulting structure of each subject should contain now the coherency values between the seed of interest and the rest of the electrodes in 'powspctrm'. > > I then used freq_statistics (in the standard way) with cluster correction to compare the coherency between groups, and from what I can tell with no errors popping up, it worked. I then interpreted the resulting clusters in a similar fashion as you would do for a typical frequency chan-freq-time analysis (instead of power, looking at coherency clusters now). > > My questions are: > 1) In regards to implementation (assuming something like this can even be appropriately implemented), do things look okay? > 2) Am I wrong in thinking that the cluster results of freq_statistics can be interpreted in a similar fashion as to a typical frequency chan-freq-time analsyis (just replacing power with coherency)? > > Sorry for the long-winded email, but any help would be greatly appreciated. > > Thank you, > Paul > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From jan.schoffelen at donders.ru.nl Tue Oct 16 09:51:22 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Tue, 16 Oct 2018 07:51:22 +0000 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling In-Reply-To: References: Message-ID: Dear Alexander, I am not sure whether I completely follow your diagnostic steps and your conclusions, but I would also check: - the alignment between the electrodes, volume conduction model, and source model. - are the electrode positions expressed in the same coordinate system as the volume conductor/source model? - are the leadfields well-behaved? For instance: if the dipole locations are too close to the innermost mesh, numerical problems may arise. Best wishes, Jan-Mathijs On 12 Oct 2018, at 18:34, Nakhnikian, Alexander > wrote: Dear All, Apologies for the long post, I've tried to be as succinct as possible while describing my problem is sufficient detail. I've been trying to solve a problem with source modeling for sometime. I've found that distributed source estimates (MNE, sLORETA, eLORETA) are heavily biased towards the ventral temporal lobe. This is the case with multiple data sets analyzed using both built-in field trip functions and imaging kernels generated by my own code. It occurs in within group grand averages and statistical contrasts between controls and patients. I've confirmed that sLORETA and eLORETA are unbiased for noiseless data by filtering point sources through the resolution kernel. I'm working on a Mac running OS 10.11.6 and the latest version of Field Trip. I'm using a standard 10/10 electrode layout (no individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) and Field Trip's template BEM. The forward model is restricted to the cortical mantle (I've had similar problems with whole brain forward models as well). I recently ran PCA at the source level to explore the issue. The data were collecting during quiet rest and bandpassed to isolate a peak that accounts for a significant difference between controls and patients at the sensor level. The imaging kernel was applied to the sensor spectral matrix. To obtain the PCs, I analyzed the covariance of power among voxels. The rank of the voxel covariance matrix was 31 with the first 3 eigenvalues account for approximately 95% of the variance. Interestingly, the spatial distribution of the first three principal components exhibited the same bias as the sensor data. When I reconstructed the source data omitting the first 3 components, the control-patient contrast localized to a collection of canonical DMN nodes. It seems extremely odd to me that removing so much of the information present in the original data returns a reasonable source-level contrast while the majority of variance is accounted for by what is clearly bias. I cannot complete this analysis and submit the results unless I can isolate the problem and correct it so I can run the analysis without reducing the rank of the source data. If anyone can speculate on possible reasons for this problem and/or potential solutions I would be grateful. Thank you, Alexander Alexander Nakhnikian, Ph.D. Research Investigator VA Boston Healthcare System Instructor in Psychiatry, Harvard Medical School _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Tue Oct 16 11:47:46 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 16 Oct 2018 11:47:46 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' Message-ID: Dear all, I have pre-processed some ERP data in BrainVision and marked bad segments instead of removing them. I now want to import this data into Fieldtrip, but how do I remove the marked segments? The following tells me that there are 10 bad segments out of 44, but when I set event type to 'Stimulus' and add the triggers at event values, it imports all 44 segments. cfg = []; cfg.dataset = '../data/export/28_preproc.dat'; cfg.trialdef.eventtype = 'Bad Interval'; ft_definetrial(cfg); Do I need to write my own conditional trialfun? If so, how? I've tried editing the tutorial example but I'm new to Matlab and am struggling. Thanks in advance, Kate -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From matti.stenroos at aalto.fi Tue Oct 16 12:16:11 2018 From: matti.stenroos at aalto.fi (Matti Stenroos) Date: Tue, 16 Oct 2018 13:16:11 +0300 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling In-Reply-To: References: Message-ID: Dear Alexander, Also I could not fully follow the pipeline. The terms "sensor spectral matrix" and "contrast" however, caught my eye. Is your "data vector" that you try to map on the brain a result of only linear processing of the measurement? Cheers, Matti On 2018-10-16 10:51, Schoffelen, J.M. (Jan Mathijs) wrote: > Dear Alexander, > > I am not sure whether I completely follow your diagnostic steps and your > conclusions, but I would also check: > > - the alignment between the electrodes, volume conduction model, and > source model. > - are the electrode positions expressed in the same coordinate system as > the volume conductor/source model? > - are the leadfields well-behaved? For instance: if the dipole locations > are too close to the innermost mesh, numerical problems may arise. > > Best wishes, > Jan-Mathijs > > >> On 12 Oct 2018, at 18:34, Nakhnikian, Alexander >> > > wrote: >> >> Dear All, >> >> Apologies for the long post, I've tried to be as succinct as possible >> while describing my problem is sufficient detail. >> >> I've been trying to solve a problem with source modeling for sometime. >> I've found that distributed source estimates (MNE, sLORETA, eLORETA) >> are heavily biased towards the ventral temporal lobe. This is the case >> with multiple data sets analyzed using both built-in field trip >> functions and imaging kernels generated by my own code. It occurs in >> within group grand averages and statistical contrasts between controls >> and patients. I've confirmed that sLORETA and eLORETA are unbiased for >> noiseless data by filtering point sources through the resolution >> kernel. I'm working on a Mac running OS 10.11.6 and the latest version >> of Field Trip. I'm using a standard 10/10 electrode layout (no >> individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) >> and Field Trip's template BEM. The forward model is restricted to the >> cortical mantle (I've had similar problems with whole brain forward >> models as well). >> >> I recently ran PCA at the source level to explore the issue. The data >> were collecting during quiet rest and bandpassed to isolate a peak >> that accounts for a significant difference between controls and >> patients at the sensor level. The imaging kernel was applied to the >> sensor spectral matrix. To obtain the PCs, I analyzed the covariance >> of power among voxels. The rank of the voxel covariance matrix was 31 >> with the first 3 eigenvalues account for approximately 95% of the >> variance. Interestingly, the spatial distribution of the first three >> principal components exhibited the same bias as the sensor data. When >> I reconstructed the source data/omitting/the first 3 components, the >> control-patient contrast localized to a collection of canonical DMN >> nodes. >> >> It seems extremely odd to me that removing so much of the information >> present in the original data returns a reasonable source-level >> contrast while the majority of variance is accounted for by what is >> clearly bias. I cannot complete this analysis and submit the results >> unless I can isolate the problem and correct it so I can run the >> analysis without reducing the rank of the source data. If anyone can >> speculate on possible reasons for this problem and/or potential >> solutions I would be grateful. >> >> Thank you, >> >> Alexander >> >> Alexander Nakhnikian, Ph.D. >> Research Investigator >> VA Boston Healthcare System >> Instructor in Psychiatry, Harvard Medical School >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > From bioeng.yoosofzadeh at gmail.com Tue Oct 16 22:12:06 2018 From: bioeng.yoosofzadeh at gmail.com (Vahab Yousofzadeh) Date: Tue, 16 Oct 2018 15:12:06 -0500 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: Hi Kate, if it is a continuous data (before epoching), you can treat bad segments as an artifact (e.g. eog or muscle, etc) and do something like this, % artifact_EOG = [100 500]; % in sample cfg = []; cfg.artfctdef.eog.artifact = artifact_EOG; cfg.artfctdef.reject = 'value'; % cfg.artfctdef.value = 0; % replacing values with nan or 0 data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is the output from ft_preprocessing. % inspecting cleaned data cfg = []; cfg.continuous = 'yes'; cfg.viewmode = 'vertical'; % all channels seperate cfg.blocksize = 5; % view the continous data in 30-s blocks ft_databrowser(cfg, data_continuous_eog_clean); if the data is epoched, simply use ft_redefinetrial. Best, Vahab From alessandro.orticoni at gmail.com Tue Oct 16 22:56:16 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Tue, 16 Oct 2018 22:56:16 +0200 Subject: [FieldTrip] ft_preprocessing filter order Message-ID: Dear all, I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. Thanks a lot, Alessandro Orticoni -------------- next part -------------- An HTML attachment was scrubbed... URL: From abela.eugenio at gmail.com Tue Oct 16 23:57:12 2018 From: abela.eugenio at gmail.com (Eugenio Abela) Date: Tue, 16 Oct 2018 22:57:12 +0100 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi Alessandro, any defaults are set in the low-level functions that ft_preprocessing calls. So, for the lowpass filter, go have a look at the help of ft_preproc_lowpassfilter.m, it says: % FT_PREPROC_LOWPASSFILTER applies a low-pass filter to the data and thereby % removes all high frequency components in the data % % Use as % [filt] = ft_preproc_lowpassfilter(dat, Fsample, Flp, N, type, dir, instabilityfix) % where % dat data matrix (Nchans X Ntime) % Fsample sampling frequency in Hz % Flp filter frequency % N optional filter order, default is 6 (but) or dependent upon % frequency band and data length (fir/firls) etc... The default for the Butterworth low-pass filter is thus 6 (for the bandpass it’s 4). You can set your preferred order when calling ft_preprocessing.m e.g. via cfg.lpfiltord = 4 or whatever makes sense. Hope that helps Eugenio On 16 Oct 2018, at 21:56, Alessandro Orticoni wrote: Dear all, I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. Thanks a lot, Alessandro Orticoni _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From dlozanosoldevilla at gmail.com Wed Oct 17 00:15:43 2018 From: dlozanosoldevilla at gmail.com (Diego Lozano-Soldevilla) Date: Wed, 17 Oct 2018 00:15:43 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi Alessandro, As explained in the help of ft_preprocessing, the default filter order can be found in the low level functions: fieldtrip/preproc/ft_preproc_bandpassfilter.m fieldtrip/preproc/ft_preproc_bandstopfilter.m fieldtrip/preproc/ft_preproc_highpassfilter.m fieldtrip/preproc/ft_preproc_lowpassfilter.m The filter order means something very different for the Butterworth (i.e. amount of samples used for the input-output recursion) https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 or for the Finite Impulse Response (FIR) filter (i.e. length of the filter kernel). https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 For the ones interested to know more about what that number does for different filters, please check this example script: http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics I hope that helps, Diego On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < alessandro.orticoni at gmail.com> wrote: > Dear all, > > I would like to ask you just a question: which is the default order of the > filters implemented by ft_preprocessing? I cannot find it anywhere. > > Thanks a lot, > Alessandro Orticoni > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.orticoni at gmail.com Wed Oct 17 00:54:24 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Wed, 17 Oct 2018 00:54:24 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi, Yes, thanks both! You have been very helpful. Best, Alessandro Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla < dlozanosoldevilla at gmail.com> ha scritto: > Hi Alessandro, > > As explained in the help > of > ft_preprocessing, the default filter order can be found in the low level > functions: > fieldtrip/preproc/ft_preproc_bandpassfilter.m > fieldtrip/preproc/ft_preproc_bandstopfilter.m > fieldtrip/preproc/ft_preproc_highpassfilter.m > fieldtrip/preproc/ft_preproc_lowpassfilter.m > > The filter order means something very different for the Butterworth (i.e. > amount of samples used for the input-output recursion) > > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 > > or for the Finite Impulse Response (FIR) filter (i.e. length of the filter > kernel). > > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 > > For the ones interested to know more about what that number does for > different filters, please check this example script: > > http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics > > I hope that helps, > > Diego > > > On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < > alessandro.orticoni at gmail.com> wrote: > >> Dear all, >> >> I would like to ask you just a question: which is the default order of >> the filters implemented by ft_preprocessing? I cannot find it anywhere. >> >> Thanks a lot, >> Alessandro Orticoni >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From k.kessler at aston.ac.uk Wed Oct 17 11:25:06 2018 From: k.kessler at aston.ac.uk (Kessler, Klaus) Date: Wed, 17 Oct 2018 09:25:06 +0000 Subject: [FieldTrip] Lectureship (faculty post) at Aston University Message-ID: Dear Fieldtrippers We would be grateful if you would disseminate or consider yourself applying for our faculty post at Aston University. We are particularly keen to strengthen our MEG group, but are open to other method backgrounds. For further details please follow the link. Many thanks Klaus https://jobs.aston.ac.uk/Vacancy.aspx?ref=R180444 Klaus Kessler (Professor of Cognitive Neuroscience) http://www.aston.ac.uk/lhs/staff/az-index/prof-klaus-kessler/ Aston Brain Centre (ABC), Aston Laboratory for Immersive Virtual Environments (ALIVE) School of Life and Health Sciences Aston University Aston Triangle Birmingham, B4 7ET Phone: +44 (0)121 204 3187 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Wed Oct 17 12:01:46 2018 From: katemsto at gmail.com (K S) Date: Wed, 17 Oct 2018 12:01:46 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: Hi Vahab, Thanks for the response. The data is already epoched so I tried ft_redefine trial as you suggested. I think the problem is that the segments marked as artefact are also marked with triggers. I therefore need some way of saying: "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried it also with ft_rejectartifact but I'm not sure how to get it to recognise the 'Bad Interval' marking. Any ideas? Many thanks, Kate On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < bioeng.yoosofzadeh at gmail.com> wrote: > Hi Kate, > > if it is a continuous data (before epoching), you can treat bad > segments as an artifact (e.g. eog or muscle, etc) and do something > like this, > > % artifact_EOG = [100 500]; % in sample > > cfg = []; > cfg.artfctdef.eog.artifact = artifact_EOG; > cfg.artfctdef.reject = 'value'; > % cfg.artfctdef.value = 0; % replacing values with nan or 0 > data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is > the output from ft_preprocessing. > > % inspecting cleaned data > cfg = []; > cfg.continuous = 'yes'; > cfg.viewmode = 'vertical'; % all channels seperate > cfg.blocksize = 5; % view the continous data in 30-s blocks > ft_databrowser(cfg, data_continuous_eog_clean); > > if the data is epoched, simply use ft_redefinetrial. > > Best, > Vahab > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Wed Oct 17 12:48:11 2018 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Wed, 17 Oct 2018 12:48:11 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> Hi Alessandro If you would want to apply them in a different order, just call ft_preprocessing multiple times, once for every filter you want to apply. best Robert > On 17 Oct 2018, at 00:54, Alessandro Orticoni wrote: > > Hi, > > Yes, thanks both! You have been very helpful. > > Best, > Alessandro > > Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla > ha scritto: > Hi Alessandro, > > As explained in the help of ft_preprocessing, the default filter order can be found in the low level functions: > fieldtrip/preproc/ft_preproc_bandpassfilter.m > fieldtrip/preproc/ft_preproc_bandstopfilter.m > fieldtrip/preproc/ft_preproc_highpassfilter.m > fieldtrip/preproc/ft_preproc_lowpassfilter.m > > The filter order means something very different for the Butterworth (i.e. amount of samples used for the input-output recursion) > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 > > or for the Finite Impulse Response (FIR) filter (i.e. length of the filter kernel). > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 > > For the ones interested to know more about what that number does for different filters, please check this example script: > http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics > > I hope that helps, > > Diego > > > On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni > wrote: > Dear all, > > I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. > > Thanks a lot, > Alessandro Orticoni > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From dmatthes at cbs.mpg.de Wed Oct 17 14:48:29 2018 From: dmatthes at cbs.mpg.de (Daniel Matthes) Date: Wed, 17 Oct 2018 14:48:29 +0200 (CEST) Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> Hi Kate, I did it in the following way: First, I did a regular import by ignoring the 'Bad Interval' markers. % ------------------------------------------------------------------------- % Data import % ------------------------------------------------------------------------- cfg = []; cfg.dataset = headerfile; cfg.trialfun = 'ft_trialfun_brainvision_segmented'; cfg.stimformat = 'S %d'; cfg.showcallinfo = 'no'; cfg = ft_definetrial(cfg); data = ft_preprocessing(cfg); After that step I have all trials in my data structure, also the trials which have bad intervals. In the second step I did this with the data: % ------------------------------------------------------------------------- % Estimate artifacts % ------------------------------------------------------------------------- events = data.cfg.event; % extract all events from the data structure artifact = zeros(length(events), 2); % allocate memory for the artifact array j = 1; for i=1:1:length(events) if(strcmp(events(i).type, 'Bad Interval')) % search for bad interval events artifact(j,1)=events(i).sample; % create artifact matrix artifact(j,2)=events(i).sample + events(i).duration - 1; j = j +1; end end artifact = artifact(1:j-1, :); % prune the artifact array to its actual size % ------------------------------------------------------------------------- % Revise data % ------------------------------------------------------------------------- cfg = []; cfg.event = events; cfg.artfctdef.reject = 'complete'; cfg.artfctdef.feedback = 'no'; cfg.artfctdef.xxx.artifact = artifact; cfg.showcallinfo = 'no'; data = ft_rejectartifact(cfg, data); These line are removing all trials with bad intervals completely from the data structure. But if you set the option cfg.artfctdef.reject to another value i.e. 'partial', you can also remove only the bad parts of certain trials I wrote this code some time ago, today I would replace the for-cycle with some more effective code. But in general it should work. All the best, Daniel ----- Original Message ----- From: "K S" To: "bioeng yoosofzadeh" Cc: fieldtrip at science.ru.nl Sent: Wednesday, October 17, 2018 12:01:46 PM Subject: Re: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' Hi Vahab, Thanks for the response. The data is already epoched so I tried ft_redefine trial as you suggested. I think the problem is that the segments marked as artefact are also marked with triggers. I therefore need some way of saying: "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried it also with ft_rejectartifact but I'm not sure how to get it to recognise the 'Bad Interval' marking. Any ideas? Many thanks, Kate On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < bioeng.yoosofzadeh at gmail.com > wrote: Hi Kate, if it is a continuous data (before epoching), you can treat bad segments as an artifact (e.g. eog or muscle, etc) and do something like this, % artifact_EOG = [100 500]; % in sample cfg = []; cfg.artfctdef.eog.artifact = artifact_EOG; cfg.artfctdef.reject = 'value'; % cfg.artfctdef.value = 0; % replacing values with nan or 0 data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is the output from ft_preprocessing. % inspecting cleaned data cfg = []; cfg.continuous = 'yes'; cfg.viewmode = 'vertical'; % all channels seperate cfg.blocksize = 5; % view the continous data in 30-s blocks ft_databrowser(cfg, data_continuous_eog_clean); if the data is epoched, simply use ft_redefinetrial. Best, Vahab -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From katemsto at gmail.com Wed Oct 17 17:59:52 2018 From: katemsto at gmail.com (Kate Stone) Date: Wed, 17 Oct 2018 17:59:52 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> References: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> Message-ID: Brilliant! Thanks Daniel, this worked perfectly. On Wed., 17 Oct. 2018, 15:35 Daniel Matthes, wrote: > Hi Kate, > > I did it in the following way: > > First, I did a regular import by ignoring the 'Bad Interval' markers. > > % ------------------------------------------------------------------------- > % Data import > % ------------------------------------------------------------------------- > > cfg = []; > cfg.dataset = headerfile; > cfg.trialfun = 'ft_trialfun_brainvision_segmented'; > cfg.stimformat = 'S %d'; > cfg.showcallinfo = 'no'; > > cfg = ft_definetrial(cfg); > data = ft_preprocessing(cfg); > > After that step I have all trials in my data structure, also the trials > which have bad intervals. > > In the second step I did this with the data: > > % ------------------------------------------------------------------------- > % Estimate artifacts > % ------------------------------------------------------------------------- > events = data.cfg.event; > % extract all events from the data structure > artifact = zeros(length(events), 2); > % allocate memory for the artifact array > j = 1; > > for i=1:1:length(events) > if(strcmp(events(i).type, 'Bad Interval')) > % search for bad interval events > artifact(j,1)=events(i).sample; > % create artifact matrix > artifact(j,2)=events(i).sample + events(i).duration - 1; > j = j +1; > end > end > > artifact = artifact(1:j-1, :); > % prune the artifact array to its actual size > > % ------------------------------------------------------------------------- > % Revise data > % ------------------------------------------------------------------------- > cfg = []; > cfg.event = events; > cfg.artfctdef.reject = 'complete'; > cfg.artfctdef.feedback = 'no'; > cfg.artfctdef.xxx.artifact = artifact; > cfg.showcallinfo = 'no'; > > data = ft_rejectartifact(cfg, data); > > These line are removing all trials with bad intervals completely from the > data structure. But if you set the option cfg.artfctdef.reject to another > value i.e. 'partial', you can also remove only the bad parts of certain > trials > > I wrote this code some time ago, today I would replace the for-cycle with > some more effective code. But in general it should work. > > All the best, > Daniel > > ----- Original Message ----- > From: "K S" > To: "bioeng yoosofzadeh" > Cc: fieldtrip at science.ru.nl > Sent: Wednesday, October 17, 2018 12:01:46 PM > Subject: Re: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' > > Hi Vahab, > > Thanks for the response. > > The data is already epoched so I tried ft_redefine trial as you suggested. > I think the problem is that the segments marked as artefact are also marked > with triggers. I therefore need some way of saying: > > "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" > > I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried > it also with ft_rejectartifact but I'm not sure how to get it to recognise > the 'Bad Interval' marking. > > Any ideas? > > Many thanks, > Kate > > > On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < > bioeng.yoosofzadeh at gmail.com > wrote: > > > Hi Kate, > > if it is a continuous data (before epoching), you can treat bad > segments as an artifact (e.g. eog or muscle, etc) and do something > like this, > > % artifact_EOG = [100 500]; % in sample > > cfg = []; > cfg.artfctdef.eog.artifact = artifact_EOG; > cfg.artfctdef.reject = 'value'; > % cfg.artfctdef.value = 0; % replacing values with nan or 0 > data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is > the output from ft_preprocessing. > > % inspecting cleaned data > cfg = []; > cfg.continuous = 'yes'; > cfg.viewmode = 'vertical'; % all channels seperate > cfg.blocksize = 5; % view the continous data in 30-s blocks > ft_databrowser(cfg, data_continuous_eog_clean); > > if the data is epoched, simply use ft_redefinetrial. > > Best, > Vahab > > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.orticoni at gmail.com Wed Oct 17 18:43:50 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Wed, 17 Oct 2018 18:43:50 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> References: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> Message-ID: Hi Robert, Thanks a lot. Best, Alessandro Il giorno mer 17 ott 2018 alle ore 13:09 Robert Oostenveld < r.oostenveld at donders.ru.nl> ha scritto: > Hi Alessandro > > If you would want to apply them in a different order, just call > ft_preprocessing multiple times, once for every filter you want to apply. > > best > Robert > > > On 17 Oct 2018, at 00:54, Alessandro Orticoni < > alessandro.orticoni at gmail.com> wrote: > > Hi, > > Yes, thanks both! You have been very helpful. > > Best, > Alessandro > > Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla < > dlozanosoldevilla at gmail.com> ha scritto: > >> Hi Alessandro, >> >> As explained in the help >> of >> ft_preprocessing, the default filter order can be found in the low level >> functions: >> fieldtrip/preproc/ft_preproc_bandpassfilter.m >> fieldtrip/preproc/ft_preproc_bandstopfilter.m >> fieldtrip/preproc/ft_preproc_highpassfilter.m >> fieldtrip/preproc/ft_preproc_lowpassfilter.m >> >> The filter order means something very different for the Butterworth (i.e. >> amount of samples used for the input-output recursion) >> >> https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 >> >> or for the Finite Impulse Response (FIR) filter (i.e. length of the >> filter kernel). >> >> https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 >> >> For the ones interested to know more about what that number does for >> different filters, please check this example script: >> >> http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics >> >> I hope that helps, >> >> Diego >> >> >> On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < >> alessandro.orticoni at gmail.com> wrote: >> >>> Dear all, >>> >>> I would like to ask you just a question: which is the default order of >>> the filters implemented by ft_preprocessing? I cannot find it anywhere. >>> >>> Thanks a lot, >>> Alessandro Orticoni >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >>> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From sauppe.s at gmail.com Thu Oct 18 12:49:00 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Thu, 18 Oct 2018 12:49:00 +0200 Subject: [FieldTrip] Interpolate outlier power values within trials Message-ID: <52E7BDAC-086E-4606-BB0E-060C21807A96@gmail.com> Dear FieldTrip list members, I have EEG data that I transform to power values with ft_freqanalysis. Is there a way to identify for each trial (= each frequency time series within each trial) outlier values that are much higher and lower than the other values in that frequency for that trial and then interpolate them? Regards, Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From danielkrauel96 at gmail.com Fri Oct 19 11:03:06 2018 From: danielkrauel96 at gmail.com (Daniel Krauel) Date: Fri, 19 Oct 2018 11:03:06 +0200 Subject: [FieldTrip] Fieldtrip Problem ft_default Message-ID: Subject: MCG Dear community, My name is Daniel Krauel and I am working for the UKSH in Kiel Germany. Currently I am analysing data of a MCG measurement. I am currently at the fieldtrip version from 27.05.2017 I used fieldtrip to read and extract the datas and also for plotting of sensor maps.It worked perfectly fine till one start. I couldn`t use ft_defaults, only executing 2 lines add path and ft default to get the error message below. addpath('C:\Users\Daniel\Documents\MATLAB\Till_2\fieldtrip-20170527'); ft_defaults; Undefined function or variable 'ft_platform_supports'. Error in ft_defaults>checkMultipleToolbox (line 280) if ~ft_platform_supports('which-all') Error in ft_defaults (line 109) checkMultipleToolbox('FieldTrip', 'ft_defaults.m'); Can someone explain me how I fix this problem? It looks so easy because I only execute two lines but I can`t fix it. I tried 4 different versions of fieldtrip and I also reinstalled matlab and resets all settings. In Addition the error message changes a little if I am using an other version of fieldtrip. Yours sincerly, Daniel Krauel -------------- next part -------------- An HTML attachment was scrubbed... URL: From gopalar.ccf at gmail.com Fri Oct 19 19:34:35 2018 From: gopalar.ccf at gmail.com (Raghavan Gopalakrishnan) Date: Fri, 19 Oct 2018 13:34:35 -0400 Subject: [FieldTrip] single subject coherence statistics In-Reply-To: <01d601d467d0$a397a570$eac6f050$@gmail.com> References: <01d601d467d0$a397a570$eac6f050$@gmail.com> Message-ID: <01e501d467d2$013c2920$03b47b60$@gmail.com> Dear all, There has been a lot of discussion on the topic of finding statistical significance between two conditions within the same subject, but there still seems to be some lack of clarity and issues. As I understand, The first step is to compute frequencies using ft_freqanalysis, with keep trials as ‘yes’ and output=’fourier’ cfg = []; cfg.output = 'fourier'; cfg.method = 'mtmfft'; cfg.foilim = [5 100]; cfg.tapsmofrq = 5; cfg.keeptrials = 'yes'; cfg.trials = find(data_redef.trialinfo(:,1)==trialopt); freq = ft_freqanalysis(cfg, data_redef); freq = struct with fields: label: {36×1 cell} dimord: 'rpttap_chan_freq' freq: [1×191 double] fourierspctrm: [1444×36×191 double] cumsumcnt: [76×1 double] cumtapcnt: [76×1 double] trialinfo: [76×5 double] cfg: [1×1 struct] The second step is to compute statistics using cfg.statistic = ‘indepsamplesZcoh’ Below is the example code cfg1 = []; cfg1.method = 'montecarlo'; cfg1.statistic = 'indepsamplesZcoh'; cfg1.correctm = 'cluster'; cfg1.clusteralpha = 0.05; cfg1.minnbchan = 2; cfg1.neighbours = neighbours; % neighbours computed separately before cfg1.tail = 0; % -1, 1 or 0 (default = 0); one-sided or two-sided test cfg1.clustertail = 0; cfg1.alpha = 0.025; % alpha level of the permutation test cfg1.numrandomization = 500; % number of draws from the permutation distribution cfg1.parameter = 'fourierspctrm'; design = zeros(1,size(freq1.fourierspctrm,1) + size(freq2.fourierspctrm,1)); design(1,1:size(freq1.fourierspctrm,1)) = 1; design(1,(size(freq1.fourierspctrm,1)+1):(size(freq1.fourierspctrm,1) + size(freq2.fourierspctrm,1)))= 2; cfg1.design = design'; cfg1.ivar = 1; cfg1.channelcmb = {'FC1' 'REMG'}; % coherence between two channels cfg1.computecritval = 'yes'; stat=ft_freqstatistics(cfg1, freq1, freq2); % freq1 and freq2 are two conditions from same subject But the second step gives an error because of dimension issues with the data Assignment has more non-singleton rhs dimensions than non-singleton subscripts Error in ft_statistics_montecarlo (line 319) statrand(:,i) = dum.stat; Error in ft_freqstatistics (line 193) [stat, cfg] = statmethod(cfg, dat, design); Error in Griptask_RT_coherence_analysis (line 140) stat=ft_freqstatistics(cfg1, freq1, freq2); There are couple of previous posts (below) that reported the same kind of error, but there is no solution yet https://mailman.science.ru.nl/pipermail/fieldtrip/2013-July/006821.html https://mailman.science.ru.nl/pipermail/fieldtrip/2010-June/002900.html In the below post, Jan-Mathijs says not to use ‘cluster’ for correctm option. However, if we don’t use then how to reproduce the effects reported in “Nonparametric statistical testing of coherence differences” paper? https://mailman.science.ru.nl/pipermail/fieldtrip/2010-April/002830.html Is this error due to a bug? Or am I doing any mistake? I appreciate developers addressing this issue. Thanks, Raghavan -------------- next part -------------- An HTML attachment was scrubbed... URL: From shaegens at gmail.com Mon Oct 22 23:19:34 2018 From: shaegens at gmail.com (Saskia Haegens) Date: Mon, 22 Oct 2018 17:19:34 -0400 Subject: [FieldTrip] postdoc opportunity at donders Message-ID: Applications are invited for a postdoc position in my lab, studying oscillations using MEG. For details please see: https://www.ru.nl/english/working-at/jobopportunities/details/details-vacature/?recid=601751 -- Saskia Haegens, PhD haegenslab.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From conny.quaedflieg at maastrichtuniversity.nl Tue Oct 23 00:18:57 2018 From: conny.quaedflieg at maastrichtuniversity.nl (Quaedflieg, Conny (PSYCHOLOGY)) Date: Mon, 22 Oct 2018 22:18:57 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis between Groups - GA, plotting and statistics Message-ID: <4ac933a569fb432fae71404f91a37f0e@UM-MAIL3218.unimaas.nl> Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Tue Oct 23 14:04:15 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 23 Oct 2018 14:04:15 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs Message-ID: Hi all, I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. Thanks in advance, Kate -------------- next part -------------- An HTML attachment was scrubbed... URL: From m.manahova at gmail.com Tue Oct 23 14:55:30 2018 From: m.manahova at gmail.com (Mariya Manahova) Date: Tue, 23 Oct 2018 14:55:30 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, You mention that you're using indepsamplesT, so are you doing a test between groups (comparing two different groups) or within participants (comparing the same participants in two conditions)? If it's the latter, then I'd suggest using depsamplesT. I am pasting below a good way to define your design matrix. This works well for a within-participant comparison. You'll need to adapt it if yours is indeed between groups. See the comments for the explanation. Nsub = 29; cfg.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)]; cfg.design(2,1:2*Nsub) = [1:Nsub 1:Nsub]; cfg.ivar = 1; % the 1st row in cfg.design contains the independent variable cfg.uvar = 2; % the 2nd row in cfg.design contains the subject number A possible problem is if you've computed grand averages and haven't used cfg.keepindividual = 'yes'. If that's the case, I'd suggest keeping the individual participants' data when calling ft_timelockgrandaverage. And then using the correct design matrix. I hope this helps! Let us know if it still doesn't work. All the best, Marisha On Tue, Oct 23, 2018 at 2:35 PM Kate Stone wrote: > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP > data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using > indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't > apply as I am using grand averages and the reference for > ft_timelockstatistics doesn't mention the design matrix, although it is > definitely required. I have tried design = [1,2], but this doesn't seem to > give sensible results. > > Thanks in advance, > Kate > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Tue Oct 23 14:59:26 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Tue, 23 Oct 2018 14:59:26 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, You can't do permutation statistics if you've already consumed the degrees of freedom you want to do statistics across (i.e., subjects in this case). Did you use cfg.keepindividual = 'yes' in the call to ft_timelockgrandaverage? If so, then the design matrix should be specified exactly as in the tutorial; just as if you had been using the individual subjects' data structures. If not, then indeed you only have a grand-average left, and no permutation stats can be performed. Cheers, Eelke On Tue, 23 Oct 2018 at 14:04, Kate Stone wrote: > > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. > > Thanks in advance, > Kate > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From katemsto at gmail.com Tue Oct 23 17:21:22 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 23 Oct 2018 17:21:22 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi again, Further to the below, what I would actually prefer to do is use the depsamplesT test on my data structure containing averages over trials for each subject (i.e. the output of ft_timelockanalysis). But every time I try, I get the message "length of the design matrix (2) doesn't equal the number of observations (694)" - no matter what the dimensions of the design matrix are. I've set up the matrix exactly as specified in the tutorial (relevant to my data of course). Any idea what this error is about? Thanks again, Kate On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP > data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using > indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't > apply as I am using grand averages and the reference for > ft_timelockstatistics doesn't mention the design matrix, although it is > definitely required. I have tried design = [1,2], but this doesn't seem to > give sensible results. > > Thanks in advance, > Kate > > > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Wed Oct 24 09:39:39 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Wed, 24 Oct 2018 09:39:39 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, It sounds like what you're dealing with is a single group of participants, each of whom is measured under two conditions. You are correct in stating that depsamplesT is the way to go here (as Marisha also suggested). This is, I believe, exactly the case described in this section of the "gentle" stats tutorial: http://www.fieldtriptoolbox.org/tutorial/eventrelatedstatistics#permutation_test_based_on_cluster_statistics and this section of the more "in depth" tutorial: http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock#within-subjects_experiments . See those tutorials (and Marisha's email) for more info on how to set up your design matrix in this case. Cheers, Eelke On Tue, 23 Oct 2018 at 17:21, Kate Stone wrote: > > Hi again, > > Further to the below, what I would actually prefer to do is use the depsamplesT test on my data structure containing averages over trials for each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) doesn't equal the number of observations (694)" - no matter what the dimensions of the design matrix are. I've set up the matrix exactly as specified in the tutorial (relevant to my data of course). Any idea what this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: >> >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. >> >> Thanks in advance, >> Kate >> >> > > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From m.manahova at gmail.com Wed Oct 24 09:41:43 2018 From: m.manahova at gmail.com (Mariya Manahova) Date: Wed, 24 Oct 2018 09:41:43 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, Clearly, there's a problem with the way you're defining your design matrix. Can you tell me what cfg.design looks like when you define it? With the code I sent you, the design matrix is a 2x58 matrix (the number of participants is 29). There are two rows, one for the independent variable (condition 1 or 2) and one for the participant number. The first row consists of 29 1's and 29 2's because I have two conditions (1 and 2), and this denotes that each participant is on the one hand in condition 1 and on the other hand in condition 2. The second row is 1:29 and then again 1:29, referring to the participant number per condition. Does this make sense? In this case, the length of my design matrix is 58 because I have 2 observations (1 for each condition) per participant. Why do you end up having 694? Can you paste the output of your cfg.design? Here's mine: [image: image.png] All the best, Marisha On Tue, Oct 23, 2018 at 6:35 PM Kate Stone wrote: > Hi again, > > Further to the below, what I would actually prefer to do is use the > depsamplesT test on my data structure containing averages over trials for > each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) > doesn't equal the number of observations (694)" - no matter what the > dimensions of the design matrix are. I've set up the matrix exactly as > specified in the tutorial (relevant to my data of course). Any idea what > this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on >> ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using >> indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't >> apply as I am using grand averages and the reference for >> ft_timelockstatistics doesn't mention the design matrix, although it is >> definitely required. I have tried design = [1,2], but this doesn't seem to >> give sensible results. >> >> Thanks in advance, >> Kate >> >> >> > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 15551 bytes Desc: not available URL: From jan.schoffelen at donders.ru.nl Wed Oct 24 10:07:26 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 24 Oct 2018 08:07:26 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis between Groups - GA, plotting and statistics References: Message-ID: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) > wrote: Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Wed Oct 24 10:49:10 2018 From: katemsto at gmail.com (Kate Stone) Date: Wed, 24 Oct 2018 10:49:10 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hello, one last email just in case anyone is following my series of very silly questions or ever searches the following warnings when trying to run ft_timelockstatistics: Warning: timelock structure contains field with and without repetitions Error: the length of the design matrix does not match the number of observations in the data In my case, it was because, in the previous step where I averaged ERPs over trials using ft_timelockanalysis, I had set cfg.keeptrials = 'yes' . It should be left at the default 'no' (for a within-subjects design at least), otherwise ft_timelockstatistics will go looking for observations in the wrong spot (trial) and will discard the ones it really needs (avg, var, dof). Thanks and apologies for over-posting, Kate On Tue, Oct 23, 2018 at 5:21 PM Kate Stone wrote: > Hi again, > > Further to the below, what I would actually prefer to do is use the > depsamplesT test on my data structure containing averages over trials for > each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) > doesn't equal the number of observations (694)" - no matter what the > dimensions of the design matrix are. I've set up the matrix exactly as > specified in the tutorial (relevant to my data of course). Any idea what > this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on >> ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using >> indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't >> apply as I am using grand averages and the reference for >> ft_timelockstatistics doesn't mention the design matrix, although it is >> definitely required. I have tried design = [1,2], but this doesn't seem to >> give sensible results. >> >> Thanks in advance, >> Kate >> >> >> > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From Joachim.Gross at glasgow.ac.uk Wed Oct 24 11:03:17 2018 From: Joachim.Gross at glasgow.ac.uk (Joachim Gross) Date: Wed, 24 Oct 2018 09:03:17 +0000 Subject: [FieldTrip] PhD student position in Muenster, Germany Message-ID: <4C93D541-3D77-4398-8160-A0FE9C3F152F@glasgow.ac.uk> The University Hospital of Münster is one of the leading hospitals in Germany. Such a position cannot be achieved by size and medical successes alone. The individual commitment counts above all. We need your commitment so that even with little things we can achieve great things for our patients. There are many possibilities open for you so that you may develop with them. The Institute for Biomagnetism and Biosignalanalysis at University of Muenster invites applications for a position as a PhD Student (gn*) Ref.: 03170 Part-Time with 65% (Germany salary grade: E 13 TV-L, 65%) (*gn=gender neutral) The position starts on 1.1.2019 and is available for 3 years in the research group of Prof. Dr. Joachim Gross. The successful candidate will coordinate, perform and publish research using MEG to study brain oscillations. We offer a stimulating environment in a successful team with high-level experience in MEG research. Successful candidates will benefit from personal mentoring, weekly seminars and general training and knowledge dissemination within the Institute for Biomagnetism and Biosignalanalysis (IBB). The position offers opportunity for further academic qualifications (PhD). The successful applicants will join a dynamic team of both experienced and junior researchers with many national and international collaborations and a sustained record of high-quality research output. We are searching for trained and highly motivated scientists ideally having experience in electrophysiology, cognitive neuroimaging or similar disciplines. The research projects require acquisition of MEG data and analysis of neuronal oscillations and their relationship to behaviour. Therefore, experience with MEG/EEG, programming and spectral analysis is highly desirable. Requirements: * Degree in psychology, medicine, physics, or a related discipline relevant for neuroscience * Experience with cognitive neuroscience research ideally with MEG or EEG * Experience with running cognitive neuroscience studies * Programming skills (matlab, python or similar) * Knowledge of general statistics for data analysis * Very good English language skills * Strong commitment, flexibility, independence and team work For more information please contact Prof. Dr. Joachim Gross, University of Muenster, Institute for Biomagnetism and Biosignalanalysis, 48149 Münster, Germany, Email: Joachim.Gross(at)­wwu(dot)­de Please send your application (including the above reference number) with all relevant information (CV, cover letter) until 11.11.2018 to Personalgewinnung des Universitätsklinikums Münster, Bewerbermanagement, Domagkstraße 5, 48149 Münster or via e-mail (PDF-file, max. 5 MB) to Bewerbung(at)­ukmuenster(dot)­de. Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference. -------------- next part -------------- An HTML attachment was scrubbed... URL: From Joachim.Gross at glasgow.ac.uk Wed Oct 24 11:03:56 2018 From: Joachim.Gross at glasgow.ac.uk (Joachim Gross) Date: Wed, 24 Oct 2018 09:03:56 +0000 Subject: [FieldTrip] PostDoc Position in Muenster, Germany Message-ID: <0EC39993-381F-49E2-B52F-FE1CD36F4058@glasgow.ac.uk> The University Hospital of Münster is one of the leading hospitals in Germany. Such a position cannot be achieved by size and medical successes alone. The individual commitment counts above all. We need your commitment so that even with little things we can achieve great things for our patients. There are many possibilities open for you so that you may develop with them. The Institute for Biomagnetism and Biosignalanalysis at University of Muenster invites applications for a Research Associate/Postdoctoral Scientist (gn*) Ref.: 03171 Full-Time with 38,5 (hours/week) (Germany salary grade: E 13 TV-L, 100%) (*gn=gender neutral) The position starts on 1.1.2019 and is available for 3 years in the research group of Prof. Dr. Joachim Gross. The successful candidate will coordinate, perform and publish research using MEG to study brain oscillations. We offer a stimulating environment in a successful team with high-level experience in MEG research. Successful candidates will benefit from personal mentoring, weekly seminars and general training and knowledge dissemination within the Institute for Biomagnetism and Biosignalanalysis (IBB). The position offers opportunity for further academic qualifications (Habilitation). The successful applicants will join a dynamic team of both experienced and junior researchers with many national and international collaborations and a sustained record of high-quality research output. We are searching for trained and highly motivated scientists ideally having experience in electrophysiology, cognitive neuroimaging or similar disciplines. The research projects require acquisition of MEG data and analysis of neuronal oscillations and their relationship to behaviour. Therefore, experience with MEG/EEG, programming and spectral analysis is highly desirable. Requirements: * PhD in psychology, medicine, physics, or a related discipline relevant for neuroscience * Experience with cognitive neuroscience research ideally with MEG or EEG * Publications in peer-reviewed journals * Experience with running cognitive neuroscience studies * Programming skills (matlab, python or similar) * Knowledge of general statistics for data analysis * Very good English language skills * Strong commitment, flexibility, independence and team work For more information please contact Prof. Dr. Joachim Gross, University of Muenster, Institute for Biomagnetism and Biosignalanalysis, 48149 Münster, Germany, Email: Joachim.Gross(at)­wwu(dot)­de Please send your application (including the above reference number) with all relevant information (CV, cover letter) until 11.11.2018 to Personalgewinnung des Universitätsklinikums Münster, Bewerbermanagement, Domagkstraße 5, 48149 Münster or via e-mail (PDF-file, max. 5 MB) to Bewerbung(at)­ukmuenster(dot)­de. Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference. -------------- next part -------------- An HTML attachment was scrubbed... URL: From agathalenartowicz at gmail.com Wed Oct 24 20:37:00 2018 From: agathalenartowicz at gmail.com (Agatha Lenartowicz) Date: Wed, 24 Oct 2018 11:37:00 -0700 Subject: [FieldTrip] postdoc opportunity - alpha oscillations/concurrent EEG-fMRI Message-ID: We’d like to share a *post-doctoral opening* at the Semel Institute for Neuroscience and Behavior at University of California, Los Angeles (Loo/Lenartowicz labs). The position is part of a funded 5-year project to map, using *concurrent EEG and fMRI,* the *brain circuitry of alpha-range oscillations and their impairments in ADHD.* The position requires familiarity with MRI and/or EEG data, knowledge of UNIX, Matlab (and/or Python), and is ideally suited for candidates with a strong background in neuroimaging, signal processing & electrical/biomedical engineering, physics or some combination. Excellent communication skills, initiative, ability to anticipate changes and develop solutions, and attention to detail are imperative. The position offers a vibrant working environment, as well as ample opportunity to participate in collaborative and independent research. For inquiries please contact Agatha Lenartowicz ( alenarto at g.ucla.edu). -------------- next part -------------- An HTML attachment was scrubbed... URL: From cornelia.quaedflieg at uni-hamburg.de Wed Oct 24 23:42:54 2018 From: cornelia.quaedflieg at uni-hamburg.de (Conny Quaedflieg) Date: Wed, 24 Oct 2018 23:42:54 +0200 Subject: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics In-Reply-To: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> References: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> Message-ID: <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> Dear Jan-Mathijs, Thank you for your quick reply. @1R Indeed ft_connectivityanalysis on individual dipole positions (PCC). Cfg.refindx’ seems not to do anything, are there other options to run a seed-based connectivity analysis? @R2 We indeed performed source-reconstruction for each subject on a subject-specific grid, that maps onto a template grid in spatially normalized space. I constructed a GA of the individual data and would like to plot these on a standard cortical sheet / brain surface. Best Conny Van: Schoffelen, J.M. (Jan Mathijs) Verzonden: woensdag 24 oktober 2018 10:16 Aan: FieldTrip discussion list Onderwerp: Re: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to  http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) wrote:   Dear Fieldtripers,   I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx.    Though, the analysis looks exactly the same with and without the refindx specified.   I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long.   Best   Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164   _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Oct 25 08:49:44 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 25 Oct 2018 06:49:44 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics In-Reply-To: <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> References: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> Message-ID: <0FD19B0D-E3D7-4A46-99B7-19840CC3B488@donders.ru.nl> Hi Conny, Again, could you please spill more details? -what is the connectivity metric you want to compute? -what do you mean with ‘cfg.refindx seems not to do anything’? -what have you done in terms of diagnostics yourself? have you looked into the code with breakpoints etc.? Jan-Mathijs On 24 Oct 2018, at 23:42, Conny Quaedflieg > wrote: Dear Jan-Mathijs, Thank you for your quick reply. @1R Indeed ft_connectivityanalysis on individual dipole positions (PCC). Cfg.refindx’ seems not to do anything, are there other options to run a seed-based connectivity analysis? @R2 We indeed performed source-reconstruction for each subject on a subject-specific grid, that maps onto a template grid in spatially normalized space. I constructed a GA of the individual data and would like to plot these on a standard cortical sheet / brain surface. Best Conny Van: Schoffelen, J.M. (Jan Mathijs) Verzonden: woensdag 24 oktober 2018 10:16 Aan: FieldTrip discussion list Onderwerp: Re: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) > wrote: Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From gerhard.jocham at uni-duesseldorf.de Thu Oct 25 10:23:42 2018 From: gerhard.jocham at uni-duesseldorf.de (Gerhard Jocham) Date: Thu, 25 Oct 2018 10:23:42 +0200 Subject: [FieldTrip] =?utf-8?q?Three_Postdoc_and_one_PhD_student_position?= =?utf-8?q?=2C_Heinrich_Heine_University_D=C3=BCsseldorf?= Message-ID: <9F20E20B-DFEB-4458-AA99-D8509F0556C0@uni-duesseldorf.de> Two postdoctoral positions (up to five years) and one PhD student position (four years) on an ERC-funded project, and one postdoctoral position (three years) on a DFG-funded project are available at the Heinrich Heine University Düsseldorf. Please check the links for detailed information and for how to apply: http://www.cns-jocham.de/01_advert_postdocs_erc.pdf http://www.cns-jocham.de/03_advert_postdoc_dfg_heise.pdf http://www.cns-jocham.de/02_advert_phd_erc.pdf The projects focus on how decision variables are represented in cortical dynamics (recorded with MEG), and how these representations are shaped by neuromodulatory systems. We are seeking candidates with a strong interest in decision making. Applicants for the postdoc positions should have a PhD in psychology, neuroscience, or related field. Demonstrable experience with either MEG or EEG and good programming skills (e.g. Matlab, Python) are essential. Applicants for the PhD position should have an MSc (or equivalent degree) in psychology, neuroscience, or related field, and sound knowledge of statistics. The ideal candidate should also possess programming skills (e.g., Matlab, Python) and have prior experience with MEG/EEG analysis. Kind regards Gerhard Jocham ================================= Prof. Dr. Gerhard Jocham Biological Psychology of Decision Making Institute of Experimental Psychology Heinrich Heine University Düsseldorf Universitätsstraße 1 40225 Düsseldorf, Germany +49 (0) 211 81 12468 -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Thu Oct 25 12:45:03 2018 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Thu, 25 Oct 2018 12:45:03 +0200 Subject: [FieldTrip] Quick eloreta question In-Reply-To: References: Message-ID: <499624E4-5D51-4042-B1A9-883FCE337164@donders.ru.nl> Hi Arno, Let me CC this to the mailing list, as I think that it might be of iterest to others. You should preferably be calling eloreta through ft_sourceanalysis. There you can see on line 1016 how it is called for ERP/ERF data with the data covariance (and elsewhere with the cross-spectrum). The phase information is certainly not ignored: an obvious one that you need is the exact out-of-phase (i.e. 180 degree rotation) of the positive and negative channels that see the same dipole. So taking the “abs” is not appropriate. But there is sometimes a reason to take “real” and ignore “imag”: For beamforming - where we scan with a single dipole - we do know that the dipole can only be in phase or exactly out-of-phase, which means that the complex part of the CSD cannot be attibuted to the source of interest. That is implemented with the ‘realfilter’ option in beamformer_dics (default=no, consistent with the publication) and beamformer_pcc (default=yes, because it works slightly better). Since we are not doing a scalar beamformer in most cases, but a vector beamformer (which can rotate in 2 or 3 dimensions), it is not obvious under which conditions realfilter works the best. A strongly rotating source has out-of-phase CSD coponents between the different dipole orientations, so in that case realfilter=no should be better able to reconstruct it. But in our data we (anecdotically) tend to get slightly better SNR at the source level for realfilter=yes. For frequency domain eloreta you are estimating many source simultaneously, where the sources can have any phase relation to each other, so there is a priori no point in only taking the real part of the CSD. Hence it is also not implemented. However, if you were to hypothesize that the underlying source is very one-dimensional (as with an ICA) and not rotating, the same reasoning would apply as for the beamformer and you would expect slightly better performance ignoring the imaginary part of the CSD in the inversion. best Robert PS the getting_started page on “loreta” refers to the specific Loreta software, not the inverse methods. Furthermore, it is >10 years old and probably outdated. > On 24 Oct 2018, at 19:49, Arnaud Delorme wrote: > > HI Robert, > > Another quick follow up, still on cross-spectrum and Loreta. > > When I use ft_freqanalysis, I get a complex crossspectrum (dataset with 80 trials). > However, my understanding is that you only use the absolute value of the crossspectrum for source localization (phase information is ignored), and that the crossspectrum should be computed on a single trial basis then averaged the absolute value should be averaged accross trials. It is unclear to me how you can obtain a complex estimate on multiple trials (are you averaging the complex cross-spectrum values across trials - I have done some more test and it seems that this is what you are doing). Attached is a cross-spectrum calculated using this method (custom code, left) versus the absolute value of the cross-spectrum returned by ft_freqanalysis (right) on the same data. > > When I do take the absolute value before doing the average of the cross-spectrum, the eLoreta solution is also more focal. > > Cheers, > > Arno > > > >> On Oct 24, 2018, at 9:41 AM, Arnaud Delorme > wrote: >> >> Hi Robert, >> >> I have a quick eLoreta question. In Fieldtrip ft_sourcelocalize, it seems that eLoreta requires the cross-spectrum. I have tried without (or used NaN) but the function is not functioning properly in that case. This means that eLoreta cannot be applied to ERPs, is that correct? What about ICA component scalp topographies (in that case I can weight the cross-spectrum using the channel inverse weight matrix - the cross-spectrum matrix would be proportional to the product of the column in the inverse weight matrix corresponding to the component by its transpose). For spectral decomposition, assuming the spectrum is in the diagonal of the cross-spectrum, is the spectrum field even used at all (I was not able to find information about that). >> >> Aso my intuition is that performing statistics (as outlined on this page http://www.fieldtriptoolbox.org/getting_started/loreta ) at the voxel level does not make sense if the statistics at the electrode level is not significant. >> >> Thank you, >> >> Arno > -------------- next part -------------- An HTML attachment was scrubbed... URL: From rdm146 at newark.rutgers.edu Thu Oct 25 23:05:46 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Thu, 25 Oct 2018 21:05:46 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Message-ID: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi -------------- next part -------------- An HTML attachment was scrubbed... URL: From manuela.costa at ctb.upm.es Fri Oct 26 18:50:41 2018 From: manuela.costa at ctb.upm.es (Manuela Costa) Date: Fri, 26 Oct 2018 18:50:41 +0200 Subject: [FieldTrip] Across regions PAC Message-ID: Dear community, I performed phase low amplitude high coupling within a region (A) following fieldtrip tutorial. http://www.fieldtriptoolbox.org/example/crossfreq/phalow_amphigh Now I would like to see wether low frequency in my region (A) modulate the amplitude of high frequency in region (B). Which is the correct way to perform this analysis? Best regards, Manuela -------------- next part -------------- An HTML attachment was scrubbed... URL: From gopalar.ccf at gmail.com Fri Oct 26 20:02:16 2018 From: gopalar.ccf at gmail.com (Raghavan Gopalakrishnan) Date: Fri, 26 Oct 2018 14:02:16 -0400 Subject: [FieldTrip] Across regions PAC Message-ID: <049501d46d56$08895520$199bff60$@gmail.com> Hi Manuela I suggest you use Brainstorm to perform Phase-amplitude coupling analysis (PAC). Brainstorm has advanced PAC algorithms. Now you can import fieldtrip structure to Brainstorm easily. https://neuroimage.usc.edu/brainstorm/Tutorials/TutPac https://neuroimage.usc.edu/brainstorm/Tutorials/Resting Thanks, Raghavan Dear community, I performed phase low amplitude high coupling within a region (A) following fieldtrip tutorial. http://www.fieldtriptoolbox.org/example/crossfreq/phalow_amphigh Now I would like to see wether low frequency in my region (A) modulate the amplitude of high frequency in region (B). Which is the correct way to perform this analysis? Best regards, Manuela -------------- next part -------------- An HTML attachment was scrubbed... URL: From carsten.wolters at uni-muenster.de Mon Oct 29 12:31:25 2018 From: carsten.wolters at uni-muenster.de (Carsten Wolters) Date: Mon, 29 Oct 2018 12:31:25 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: Message-ID: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR    Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: > > Dear Fieldtrippers > > > I have applied the FEM simbio head modeling pipeline implemented > in Fieldtrip to my EEG data. My understanding is that this pipeline > assumes isotropic conductivities for 5 head compartments (as specified > by cfg.conductivity in ft_prepare_headmodel). After reading some > papers (e.g. Vorwerk et al 2014 > https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like > incorporating white matter conductivity anisotropy has a relatively > small albeit significant effect on the source solution. I am > interested in comparing FEM results when treating white matter as > anisotropic. My questions are as follows: > > > 1. Is there a way to implement the FEM simbio head model whilst > treating WM as anisotropic within Fieldtrip? If so, how would one > do this (or are there any resources available that demonstrate this)? > 2. From previous papers and some simbio documentation > (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) > it seems like diffusion MRI data is required to calculate the WM > conductivity for each individual subject. I only have T1 and T2 > scans for my subjects. So would it be possible to use WM > anisotropic information obtained from some kind of diffusion > MRI group average/atlas instead (accepting some loss in > subject-level precision)? If so, does such a group average/atlas > exist? > > > Any help would be greatly appreciated! > > > Thanks > > Ravi > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From maria.hakonen at gmail.com Mon Oct 29 13:33:15 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Mon, 29 Oct 2018 14:33:15 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? Message-ID: Dear FieldTrip experts, I have run ft_mergealign across subjects to align the head positions. However, the residual variance between the original and the realigned data seems to be high: original -> template RV 21232.46 % original -> original RV 36.96 % original -> template -> original RV 9579.95 % Could someone please let me know what would be the largest acceptable change in the residual variance, and what should I do if the residual variance is too high? Does the increase in residual variance mean that there is a large shift in the head position? I have used ft_mergealign as follows: template = list of subjects (i.e. I want to calculate an average head position over the subjects) grad = data.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); vol = ft_headmodel_localspheres(hs,grad); cfg = []; cfg.template = template; cfg.inwardshift = 1.0; cfg.vol = vol; data_aligned = ft_megrealign(cfg, data); Best, Maria -------------- next part -------------- An HTML attachment was scrubbed... URL: From j.vorw01 at gmail.com Mon Oct 29 15:14:16 2018 From: j.vorw01 at gmail.com (Johannes Vorwerk) Date: Mon, 29 Oct 2018 15:14:16 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: Message-ID: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes > Am 29.10.2018 um 12:31 schrieb Carsten Wolters : > > Dear Ravi, > > 1) You can use the pure SimBio-code from > https://www.mrt.uni-jena.de/simbio/index.php/Main_Page > to treat WM anisotropy. > While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, > this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented > Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: > "Depending on your matlab skills and your available time, I could help you to give it a > try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." > > 2) We recommend > http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf > on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main > bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details > in the cortices are individual. We always measure T1, T2 and DTI from each subject > and I personally do not have experience with such a group-level anisotropy compared > to the individual one. Might be interesting to hear from others what they think!? > > BR > Carsten > > > > Am 25.10.18 um 23:05 schrieb Ravi Mill: >> Dear Fieldtrippers >> >> I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040 ), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: >> >> Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? >> From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples ) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? >> >> Any help would be greatly appreciated! >> >> Thanks >> Ravi >> >> >> >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From victor.rg.gib at gmail.com Tue Oct 30 10:43:08 2018 From: victor.rg.gib at gmail.com (Victor RG) Date: Tue, 30 Oct 2018 10:43:08 +0100 Subject: [FieldTrip] Problem using ft_sensrorealign with Yokogawa MEG Message-ID: HI Fieldtrip experts! I'm trying to align a headmodel (the one from example "Subject1") with my MEG sensors (Yokogawa system). I'm trying to do that interactively, in order to construct a Leadfied matrix as accurate as possible. To do that, I am testing this code: *cfg = [];* *cfg.method = 'interactive';* *cfg.headshape = vol.bnd(1);* *cfg.senstype = 'meggrad';* *grad_aligned = ft_sensorrealign(cfg, grad); % Im using ft_sensorrealign cause I think is the MEG version of ft_electroderealign* The variables employed consist of: *>> grad = * struct with fields: - balance: [1×1 struct] - chanori: [160×3 double] - chanpos: [160×3 double] - chantype: {160×1 cell} - chanunit: {160×1 cell} - coilori: [320×3 double] - coilpos: [320×3 double] - label: {160×1 cell} - tra: [160×320 double] - type: 'yokogawa160' - unit: 'cm' - fid: [1×1 struct] *>> vol.bnd(1) =* struct with fields: - pos: [1000×3 double] - tri: [1996×3 double] - coordsys: 'ctf' I have looked at the tutorial for EEG sensors realignment, and I have copied the procedure, since there is not a specific tutorial for MEG sensors. I thought it would be the same, but when executing it I obtain the following errors: *>> Undefined function 'fixpos' for input arguments of type 'struct'.* *Error in ft_sensorrealign (line 255)* *headshape = fixpos(cfg.headshape);* *Error in generating_leadfield (line 63)* *grad_aligned = ft_sensorrealign(cfg, grad);* Does anybody know how to do that, or how to do an interactive realignment with MEG sensors? Thanks in advance. Víctor. Víctor Rodríguez González Grupo de Ingeniería Biomédica, ETSIT. Universidad de Valladolid, España. -------------- next part -------------- An HTML attachment was scrubbed... URL: From hesham.elshafei at inserm.fr Tue Oct 30 17:05:08 2018 From: hesham.elshafei at inserm.fr (Hesham ElShafei) Date: Tue, 30 Oct 2018 17:05:08 +0100 Subject: [FieldTrip] Phase Information For PCC Beamformer Message-ID: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> Hello Fieldtrippers! So I am trying to do some whole brain connectivity analysis according to this tutorial: http://www.fieldtriptoolbox.org/tutorial/networkanalysis All is going fine :) I just would like to understand how the phase information is obtained using these options in ft_freqanalysis cfg.method = 'mtmfft'; cfg.output = 'fourier'; In other words , how is time incorporated in the computed phase values? In other other words, these phase values represent the signal at which time point? hope I was clear enough Cheers! Hesham From ignasi.sols at nyu.edu Tue Oct 30 19:38:13 2018 From: ignasi.sols at nyu.edu (Ignasi Sols) Date: Tue, 30 Oct 2018 14:38:13 -0400 Subject: [FieldTrip] Brain-shift compensation Error Message-ID: Dear all, I'm following the method developed by Stolk et al (2018) to localize the electrodes of ECoG data. I'm getting this error on step 23 (Project the electrode grids to the surface hull of the implanted hemisphere) and I can't solve it. Could anyone help me with this? Thanks, Ignasi *using electrodes specified in the configuration* *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: 22155045* *creating electrode pairs based on electrode positions* *Error using fmincon (line 241)* *You must provide a non-empty starting* *point.* *Error in warp_dykstra2012 (line 156)* *coord_snapped = fmincon(efun, coord0,* *[], [], [], [], [], [], cfun,* *options);* *Error in ft_electroderealign (line* *406)* * norm.elecpos =* warp_dykstra2012(cfg, elec, headshape); -- Ignasi Sols Postdoctoral Fellow Department of Psychology New York University -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Wed Oct 31 07:23:37 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Tue, 30 Oct 2018 23:23:37 -0700 Subject: [FieldTrip] Brain-shift compensation Error In-Reply-To: References: Message-ID: Hi Ignasi, One can only guess based on that error msg alone. You might want to put a debug marker at line 156, check whether coord0 is truly empty, and then try to trace back to what's causing it to be empty (e.g., an empty elecpos field in your elec structure?). Arjen On Tue, Oct 30, 2018 at 12:04 PM Ignasi Sols wrote: > Dear all, > I'm following the method developed by Stolk et al (2018) to localize the > electrodes of ECoG data. > I'm getting this error on step 23 (Project the electrode grids to the > surface hull of the implanted hemisphere) and I can't solve it. Could > anyone help me with this? > > Thanks, > Ignasi > > > *using electrodes specified in the configuration* > *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: > 22155045* > *creating electrode pairs based on electrode positions* > *Error using fmincon (line 241)* > *You must provide a non-empty starting* > *point.* > *Error in warp_dykstra2012 (line 156)* > *coord_snapped = fmincon(efun, coord0,* > *[], [], [], [], [], [], cfun,* > *options);* > *Error in ft_electroderealign (line* > *406)* > * norm.elecpos =* > warp_dykstra2012(cfg, elec, > headshape); > > -- > Ignasi Sols > Postdoctoral Fellow > Department of Psychology > New York University > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From assaf.harel at wright.edu Wed Oct 31 17:43:59 2018 From: assaf.harel at wright.edu (Harel, Assaf) Date: Wed, 31 Oct 2018 16:43:59 +0000 Subject: [FieldTrip] 20th International Symposium on Aviation Psychology Call for Proposals : New Proposal Submission Deadline Message-ID: <95AD2984-3DA8-4F45-9664-8BE71009B580@wright.edu> 20th International Symposium on Aviation Psychology Call for Proposals The 20th ISAP will be held in Dayton, Ohio, U.S.A., May 7-10, 2019 (Tuesday – Friday). Proposal Submission is Live! New Proposal Submission Deadline: November 9, 2018 Proposals are sought for posters, papers, symposium and panel sessions, and workshops. Any topic related to the field of aviation psychology is welcomed. Topics on human performance problems and opportunities within aviation systems, and design solutions that best utilize human capabilities for creating safe and efficient aviation systems are all appropriate. Any basic or applied research domain that generalizes from or to the aviation domain will be considered. Students are especially encouraged to participate in the The Stanley Nelson Roscoe Best Student Paper Competition. Visit http://aviation-psychology.org for more information. Pamela Tsang and Michael Vidulich (Symposium Co-Chairs) Contact isap2019 at isap.wright.edu for any questions. -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 1 03:58:21 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 30 Sep 2018 21:58:21 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster Message-ID: Dear FieldTrip community, I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From L.M.Talamini at uva.nl Mon Oct 1 13:52:49 2018 From: L.M.Talamini at uva.nl (Talamini, Lucia) Date: Mon, 1 Oct 2018 11:52:49 +0000 Subject: [FieldTrip] Job post for the Fieldtrip mailing list Message-ID: Postdoctoral position on Sleep and Memory University of Amsterdam Job description We are looking for a postdoctoral scientist to join our research on manipulation of sleep and memories using EEG-guided neurostimulation. You will use a well-validated and highly flexible closed-loop neurostimulation (CLNS) method, developed at the UvA-Sleep and Memory lab. We have recently using this method deepen sleep and that to boost or depress individual memories. Responsibilities will also involve co-supervision of interns and junior investigators working on other CLNS projects. The lab The research will be executed at the UvA’s state of the art Sleep and Memory Lab, embedded in the Brain and Cognition Group, Department of Psychology, University of Amsterdam. The lab takes part in the interdisciplinary ABC (Amsterdam Brain and Cognitive Science Center). Requirements Applicants should have a PhD in (cognitive) neuroscience, bio-electrical signal analysis, or a related field. The candidate should have extensive experience in with EEG signal analysis (preferably involving methods development and application) and strong programming skills, e.g. in MATLAB and/or Python. Excellent scientific writing skills in English are required and some level of IT and engineering know-how will be considered a plus. Finally, flexibility with regard to working hours is expected in view of the type of research. Appointment Initial appointment will be for approximately 10 months, in case of full time employment. Part-time employment can be discussed. The appointment may be extended using expected ulterior funding. The salary will be in accordance with the university regulations for academic personnel (Collective Labor Agreement Dutch Universities). Job application For further information about this position please contact Dr. L.M. Talamini (phone: +31 6 4764 5932; e-mail: L.M.Talamini at uva.nl). Please send your application letter, curriculum vitae, a transcript of academic grades and courses, a proof of writing (e.g. a research publication) and the names and contact information of two persons willing to provide reference information to Lucia Talamini (L.M.Talamini at uva.nl). Or submit them through this link: http://www.uva.nl/en/content/vacancies/2018/09/18-552-postdoctoral-position-sleep-and-memory.html?a Application closing date: October15th 2018. -------------- next part -------------- An HTML attachment was scrubbed... URL: From L.M.Talamini at uva.nl Mon Oct 1 14:30:07 2018 From: L.M.Talamini at uva.nl (Talamini, Lucia) Date: Mon, 1 Oct 2018 12:30:07 +0000 Subject: [FieldTrip] Postdoctoral position on Sleep and Memory Message-ID: Postdoctoral position on Sleep and Memory University of Amsterdam Job description We are looking for a postdoctoral scientist to join our research on manipulation of sleep and memories using EEG-guided neurostimulation. You will use a well-validated and highly flexible closed-loop neurostimulation (CLNS) method, developed at the UvA-Sleep and Memory lab. We have recently used this method to deepen sleep and to boost or depress individual memories. Responsibilities will also involve co-supervision of interns and junior investigators working on other CLNS projects. The lab The research will be executed at the UvA’s state of the art Sleep and Memory Lab, (Brain and Cognition Group, Dept. of Psychology, University of Amsterdam). The lab takes part in the interdisciplinary ABC (Amsterdam Brain and Cognitive Science Center). Requirements Applicants should have a PhD in (cognitive) neuroscience, bio-electrical signal analysis, or a related field. The candidate should have extensive experience in with EEG signal analysis (preferably involving methods development and application) and strong programming skills, e.g. in MATLAB and/or Python. Excellent scientific writing skills in English are required and some level of IT and engineering know-how will be considered a plus. Finally, flexibility with regard to working hours is expected in view of the type of research. Appointment Initial appointment will be for approximately 10 months, in case of full time employment. Part-time employment can be discussed. The appointment may be extended using expected ulterior funding. The salary will be in accordance with the university regulations for academic personnel (Collective Labor Agreement Dutch Universities). Job application For further information about this position please contact Dr. L.M. Talamini (phone: +31 6 4764 5932; e-mail: L.M.Talamini at uva.nl). Please send your application letter, curriculum vitae, a transcript of academic courses and grades, a proof of writing (e.g. a research publication) and the names and contact information of two persons willing to provide reference information to Lucia Talamini (L.M.Talamini at uva.nl). Or apply through this link: http://www.uva.nl/en/content/vacancies/2018/09/18-552-postdoctoral-position-sleep-and-memory.html?a Application closing date: October15th 2018. -------------- next part -------------- An HTML attachment was scrubbed... URL: From narun.pornpattananangkul at nih.gov Tue Oct 2 05:07:23 2018 From: narun.pornpattananangkul at nih.gov (Pornpattananangkul, Narun (NIH/NIMH) [F]) Date: Tue, 2 Oct 2018 03:07:23 +0000 Subject: [FieldTrip] Postdoctoral position at the National Institute of Mental Health, National Institute of Health Message-ID: The Mood Brain and Development Unit (MBDU) at the US National Institute of Health (NIH) led by Dr Argyris Stringaris is looking for a post-doc. The focus of the MBDU is on how reward processing aberrations impact on human mood and may lead to pathologies such as depression. There is a lot of promise that reward processing abnormalities underlie psychiatric disorders. Yet, only if causal links reward processing aberrations and mood still are elucidated, will they form the basis of targeted treatment design. To resolve these questions, we use a variety of tools, benefitting from the unique scientific environment and resources that the NIH offers. In particular, we use longitudinal imaging protocols at three different time scales, work on methodological development of resting-state and task-based imaging data fusion, leverage treatment designs (such as psychological or pharmacological therapies) and develop closed loop devices for mood manipulation. We are a team that works closely together, has many regular science and social meetings and collaborates extensively with others in and out of NIH. Candidates with a strong background in MRI and at least an interest in neurophysiological methods such as EEG/MEG, as well as a keen interest in methodology and computational modeling will be most suited for the job. Advanced coding in R, Python, Matlab or Shell scripts is a requirement. Cognitive neuroscientists, engineers and other candidates with strong numerical and computational skills are particularly encouraged to apply. The postdoctoral community at the NIH is large (approximately 4,000) strong and vibrant. Trainees come from across the U.S. and around the world. Salary for this position is defined by type of training and years of experience. https://www.training.nih.gov/postdoctoral_irta_stipend_ranges Benefits include health insurance for the trainee and his/her family, and support for coursework related to the trainee's research and travel to meetings is often available. https://www.training.nih.gov/programs/postdoc_irp The NIH is among the largest and best communities of neuroimaging researchers in the world, with opportunities to collaborate with leaders in the field of fMRI, MEG, machine learning, computational psychiatry and neuromodulation. Our group has access to high performance computing, has been allocated timeslots to use fMRI and has our own in-patient unit with full-time clinicians. See more information here https://www.nimh.nih.gov/labs-at-nimh/research-areas/clinics-and-labs/edb/mbdu/index.shtml For more information, please write with CV and expression of interest to Argyris Stringaris: argyris.stringaris at nih.gov and Narun Pornpattananangkul: narun.pornpattananangkul at nih.gov . The National Institutes of Health is an equal opportunity employer. -------------- next part -------------- An HTML attachment was scrubbed... URL: From michelic72 at gmail.com Tue Oct 2 10:31:30 2018 From: michelic72 at gmail.com (Cristiano Micheli) Date: Tue, 2 Oct 2018 10:31:30 +0200 Subject: [FieldTrip] Fwd: please circulate In-Reply-To: References: <8621bbfc-7ad6-af42-629c-604c5237452a@gmail.com> <73582e5b-25bd-8af9-7d70-a49d7e4233fc@gmail.com> <90669498-b492-d745-cdfa-d7cb0b85e1ff@gmail.com> <6ff95a55-5ac0-627b-6fc8-2ad8b26b5692@gmail.com> Message-ID: ---------- Forwarded message --------- From: Jean-Claude Dreher Date: Tue, Oct 2, 2018 at 10:29 AM Subject: please circulate To: Cristiano Micheli Dear Cristiano, Could you please post this add on the fieldtrip website? Thanks a lot, JC The Institute of Cognitive Science, Lyon, France is inviting applications for a postdoctoral position to study social decision making and reward processing using fMRI in healthy subjects and intracranial recordings in patients with epilepsy. The institute of Cognitive Science is located in Lyon, a thriving universitary city. The institute hosts an interdisciplinary community with access to several brain imaging facilities, such as one research-dedicated siemens scanner, one MEG, one TEP, EEG, TMS and other useful resources. Candidates, preferably with model-based fMRI experience, should send their CV, statement of research interests, and representative publications to Jean-Claude Dreher ( https://dreherteam.wixsite.com/neuroeconomics), Email: dreher at isc.cnrs.fr -- Dr Jean-Claude Dreher, Research director, CNRS UMR 5229 Neuroeconomics group, Reward and decision making Institut des Sciences Cognitives Marc Jeannerod, 67 Bd Pinel, 69675 Bron, France tel: 00 334 37 91 12 38 fax: 00 334 37 91 12 10 https://dreherteam.wixsite.com/neuroeconomicshttp://cnc.isc.cnrs.fr/en/research/neuroeco-en/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From michelic72 at gmail.com Tue Oct 2 11:19:58 2018 From: michelic72 at gmail.com (Cristiano Micheli) Date: Tue, 2 Oct 2018 11:19:58 +0200 Subject: [FieldTrip] please circulate - Postdoc Lyon Message-ID: Dear FieldTrip friends, please circulate this post from Lyon. Cris Micheli ==================== The Institute of Cognitive Science, Lyon, France is inviting applications for a postdoctoral position to study social decision making and reward processing using fMRI in healthy subjects and intracranial recordings in patients with epilepsy. The institute of Cognitive Science is located in Lyon, a thriving universitary city. The institute hosts an interdisciplinary community with access to several brain imaging facilities, such as one research-dedicated siemens scanner, one MEG, one TEP, EEG, TMS and other useful resources. Candidates, preferably with model-based fMRI experience, should send their CV, statement of research interests, and representative publications to Jean-Claude Dreher ( https://dreherteam.wixsite.com/neuroeconomics), Email: dreher at isc.cnrs.fr -- Dr Jean-Claude Dreher, Research director, CNRS UMR 5229 Neuroeconomics group, Reward and decision making Institut des Sciences Cognitives Marc Jeannerod, 67 Bd Pinel, 69675 Bron, France tel: 00 334 37 91 12 38 fax: 00 334 37 91 12 10 https://dreherteam.wixsite.com/neuroeconomicshttp://cnc.isc.cnrs.fr/en/research/neuroeco-en/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From junseok.kim at mail.utoronto.ca Tue Oct 2 19:31:40 2018 From: junseok.kim at mail.utoronto.ca (Junseok Kim) Date: Tue, 2 Oct 2018 17:31:40 +0000 Subject: [FieldTrip] Issue using ft_sourceanalysis Message-ID: Hello, I have just recently updated to the latest version of FieldTrip and I ran into a probelm I have never ran into before while using ft_sourceanalysis Reference to non-existent field 'dimord'. Error in ft_sourceanalysis (line 788) if strcmp(data.dimord, 'chan_time') in which the input was cfg = []; cfg.method = 'lcmv'; cfg.grid = source_model; cfg.headmodel = vol; cfg.lcmv.keepfilter = 'yes'; cfg.grad = dataica.grad; lcmvsource = ft_sourceanalysis(cfg, timelock); and the timelock analysis was performed by cfg = []; cfg.channel = 'meggrad'; cfg.covariance = 'yes'; cfg.covariancewindow = 'all'; cfg.vartrllength = 2; cfg.keeptrials = 'yes'; timelock = ft_timelockanalysis(cfg, dataica); This was performed on MATLAB 2015b on Neuromag resting state data divided into 10s epochs If anyone has run into this issue or has a solution for it, let me know. Cheers Andrew -------------- next part -------------- An HTML attachment was scrubbed... URL: From ajesse at psych.umass.edu Wed Oct 3 01:05:31 2018 From: ajesse at psych.umass.edu (Alexandra Jesse) Date: Tue, 2 Oct 2018 19:05:31 -0400 Subject: [FieldTrip] Captrak Message-ID: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> hi! I’m thinking about buying the Brain Vision’s Captrak system. Brain Vision recommended to buy BESA to analyze the data but I’d rather stick with Fieldtrip. Can that data easily be read into Fieldtrip? Thank you, Alexandra Sent from my iPhone From alik.widge at gmail.com Wed Oct 3 14:17:40 2018 From: alik.widge at gmail.com (Alik Widge) Date: Wed, 3 Oct 2018 07:17:40 -0500 Subject: [FieldTrip] Captrak In-Reply-To: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> References: <41C6CA4D-7D08-4F2F-909A-6F885478B6F9@psych.umass.edu> Message-ID: We analyze data from BrainVision in MNE-Python, and if it fits into that cross-platform format, I am confident it can be used in FieldTrip also. The CapTrak is a pretty useful system. Alik Widge alik.widge at gmail.com (206) 866-5435 On Tue, Oct 2, 2018 at 6:24 PM Alexandra Jesse wrote: > hi! > I’m thinking about buying the Brain Vision’s Captrak system. Brain Vision > recommended to buy BESA to analyze the data but I’d rather stick with > Fieldtrip. Can that data easily be read into Fieldtrip? > Thank you, > Alexandra > > Sent from my iPhone > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From zangyl1983 at gmail.com Thu Oct 4 05:41:19 2018 From: zangyl1983 at gmail.com (Yunliang Zang) Date: Thu, 04 Oct 2018 12:41:19 +0900 Subject: [FieldTrip] Problem in running tutorial of preprocessing and analysis of spike data Message-ID: Hello, I am Yunliang Zang, a postdoc at OIST. I just began using fieldtrip. I met a problem when running the tutorial from the link: http://www.fieldtriptoolbox.org/tutorial/spike When reading event information by event = ft_read_event('p029_sort_final_01.nex'), there are 361626 events instead of 37689 events as shown in the tutorial. I checked further and found in the outputted structure called event, there are a lot of empty elements for the field value, and their type are Event002 instead of Strobed. I think ft_read_event does not function correctly in my case. This function is too long for me to check further. My Matlab version is MATLAB_R2018a. Does anybody have a similar problem and know how to fix it? Best, Yunliang -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Thu Oct 4 21:10:14 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Thu, 4 Oct 2018 12:10:14 -0700 Subject: [FieldTrip] ft_prepare_mesh ERROR In-Reply-To: References: <637223E2-7994-40FF-9D5A-A5C24E5B9374@neuro.med.kyushu-u.ac.jp> Message-ID: Just in case anyone experiences the same or a related issue in the future, Toshiki and I solved the problem for him, adjusting ft_prepare_mesh_cortexhull in the main repository . Simply re-download/pull if you had the same issue. Arjen On Fri, Sep 28, 2018 at 10:50 PM Arjen Stolk wrote: > Dear Toshiki, > > From that error, it seems your version of Freesurfer is referencing > libraries that do not exist while executing the *mris_fill *command. To > rule out that the issue is due to the call to *mris_fill* being made from > within the matlab environment as *prepare_mesh_cortexhull* does, you > could try to call *mris_fill* directly from within a terminal and see if > it replicates, as follows: > > export FREESURFER_HOME=/Applications/freesurfer > > source $FREESURFER_HOME/SetUpFreeSurfer.sh > > mris_fill -c -r 1 path_to_freesurfer/surf/lh.pial tmp.filled.mgz > > > If that reproduces the error, it might be worth consulting the > Freesurfer development team concerning this. A similar issue was reported > here > , > but it doesn't look like a fix has been suggested. Alternatively, you > could try a different version of Freesurfer for creating the cortical hull > (freesurfer-Darwin-lion-stable-pub-v5.3.0 runs fine on my mac). Hope > this helps, > Arjen > > > On Sep 25, 2018, at 12:13 AM, 岡留敏樹 > wrote: > > Hello fieldtrip experts, > > when trying to run ‘ft_prepare_mesh’ command from fieldtrip with MATLAB R2018a, I run into the error below. > > I was preprocessing MRI date according with the paper ’Nature Protocol 2018; 13: 1699-1723. http://doi.org/10.1038/s41596-018-0009-6 ‘. > > I’m running this on a iMac, macOS High Sierra 10.13.6, with fieldtrip-20180909, freesurfer-Darwin-OSX-stable-pub-v6.0.0-2beb96c. > > From past archives, I think this is because of SIP. So I disabled SIP of OSX, but I run into same errors. > I tried different subjects, which caused similar errors. > > Any ideas would be greatly appreciated, thank you! > > > [CODE] > > cfg = []; > > cfg.method = ‘cortexhull’; > > cfg.headshape = ; > > cfg.fshome = ; > > hull_lh = ft_prepare_mesh (cfg); > > > [ERROR] > > dyld: lazy symbol binding failed: Symbol not found: ___emutls_get_address > > Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib > Expected in: /usr/lib/libSystem.B.dylib > > dyld: Symbol not found: ___emutls_get_address > Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib > Expected in: /usr/lib/libSystem.B.dylib > > mris_fill -c -r 1 /Users/okadometoshiki/Desktop/SubjectUCI29/freesurfer/surf/lh.pial /private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz: Aborted > reading filled volume... > gunzip: can't stat: /private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz (/private/var/folders/cc/fv5fk09d3hj5g5kx7dg7pqq80000gn/T/tp0a8d7856_136c_4156_bf73_d23f64bbf687_pial.filled.mgz.gz): No such file or directory > ERROR: problem reading fname > > > > ======== > > Toshiki Okadome > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From seunggoo.kim at duke.edu Thu Oct 4 22:44:03 2018 From: seunggoo.kim at duke.edu (Seung Goo Kim, Ph.D.) Date: Thu, 4 Oct 2018 20:44:03 +0000 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test Message-ID: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Dear FT-ers, I (also) want to know how we could perform one-sample T-test using the cluster-based permutation test to compute and correct p-values. I noticed that many people asked about this for a long time (https://mailman.science.ru.nl/pipermail/fieldtrip/2018-August/012314.html). I think it's because the one-sample T-test after baseline correction has been really a common way to analyze ERP/F data. Furthermore, in many functional studies (including fMRI studies), the first question at the second-level is if the effect of a certain contrast is non-zero over multiple subjects, thus one-sample T-test is a really natural thing to do. It is possible to compare baseline-vs-activation trials at the first level, although it could be a problem if the baseline period is too short compared to the activation period, which is not really uncommon (for example, it would be rather common to have short inter-stimulus-interval than the stimulus length itself). But at the second level, I have no idea how we could do that. I know that a permutation distribution can be generated by randomly flipping signs for one-sample T-test in permutation test, so I added another resampling method to flip signs of the half of trials in resampledesign() but the result was quite different from the two-sample t-test comparing baseline vs activation trials. Could be there any specific reason for flipping signs would not work for cluster-based correction? One possible reason I could think of would be that the noise distribution is not actually symmetric, thus permuting labels and flipping signs create different permutation distributions. Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? Best, -- Seung-Goo ("SG") Kim, PhD Postdoctoral Research Associate O-Lab, Department of Psychology & Neuroscience, Duke University Postal: 308 Research Drive, Durham, NC 27708, USA Email: solleo at gmail.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Fri Oct 5 10:07:18 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Fri, 5 Oct 2018 10:07:18 +0200 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test In-Reply-To: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> References: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Message-ID: Dear SG, Rather than go into the whole one-sample T test business again, I will respond to one aspect of your question which I think might be useful. > but the result was quite different from the two-sample t-test comparing baseline vs activation trials. I don't know exactly how you were doing the baseline vs activation test, but I'll note two things here. First, you will typically not want to do a *two-sample* test, but a *paired-sample* test; i.e. each unit of observation (trial or subject) has both a baseline and an activation period, and it's the paired difference that matters. Second, you will want to test the activation period against the *mean* across the entire baseline period (since we typically assume a stationary baseline). If you were to simply do (1) select baseline window as condition A; (2) select activation window as condition B; (3) compare the two using cluster stats; then the statistic would be comparing each time point for the activation period against the matching time point in the baseline. So basically, one way of doing activation versus baseline cluster stats is to average the baseline window across time (probably repmat() the mean over time again) and then use paired statistics against the activation window. This should work at first or second level. Hope that helps! Best, Eelke > > Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? > > Best, > -- > Seung-Goo ("SG") Kim, PhD > > Postdoctoral Research Associate > O-Lab, Department of Psychology & Neuroscience, Duke University > Postal: 308 Research Drive, Durham, NC 27708, USA > Email: solleo at gmail.com > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From seunggoo.kim at duke.edu Fri Oct 5 19:58:39 2018 From: seunggoo.kim at duke.edu (Seung Goo Kim, Ph.D.) Date: Fri, 5 Oct 2018 17:58:39 +0000 Subject: [FieldTrip] One-sample t-test with cluster-based permutation test In-Reply-To: References: <107F2DA0-31F8-4151-92E1-211636EF0744@duke.edu> Message-ID: <562AE551-038E-4BA8-9ED0-6BA7124F9115@duke.edu> Dear Eelke, Thank you for an informative response. I just wonder how the discussion around the one-sample T-test finally has ended and why it was decided not to be included. I think you're right that it should be tested with the paired T-test. I thought the autocorrelation in MEG data should be minimal, I think it would make more sense to pair them, especially the signal was low-pass filtered. I think if you create a flat timeseries with the mean values of each baseline period as a "baseline trial" and do a paired t-test, the result should be identical to flipping signs of baseline-corrected activation trials. During the permutation, if the labels are not swapped, then the paired difference at one trial-pair would be y - x0 (y is a timeseries in an activation period; x0 is the mean of the baseline period). If the labels are swapped, then the difference would be x0 - y. Since baseline-corrected activation trial is also y - x0, this is equivalent to flipping signs of the corrected activation trials. But I'm not sure if I really want to use only the mean of the baseline period because then it would discard underlying noise structure in the baseline period. But also because the baseline period is not evoked by any stimuli of interest, we don't assume any sample-by-sample correspondence between the baseline and activation periods. So it feels a bit strange to do so too. Best, -SG On 2018-10-05, at 04:07, Eelke Spaak > wrote: Dear SG, Rather than go into the whole one-sample T test business again, I will respond to one aspect of your question which I think might be useful. but the result was quite different from the two-sample t-test comparing baseline vs activation trials. I don't know exactly how you were doing the baseline vs activation test, but I'll note two things here. First, you will typically not want to do a *two-sample* test, but a *paired-sample* test; i.e. each unit of observation (trial or subject) has both a baseline and an activation period, and it's the paired difference that matters. Second, you will want to test the activation period against the *mean* across the entire baseline period (since we typically assume a stationary baseline). If you were to simply do (1) select baseline window as condition A; (2) select activation window as condition B; (3) compare the two using cluster stats; then the statistic would be comparing each time point for the activation period against the matching time point in the baseline. So basically, one way of doing activation versus baseline cluster stats is to average the baseline window across time (probably repmat() the mean over time again) and then use paired statistics against the activation window. This should work at first or second level. Hope that helps! Best, Eelke Is there really no way to use cluster-based permutation test for one-sample T-test at the second level? Best, -- Seung-Goo ("SG") Kim, PhD Postdoctoral Research Associate O-Lab, Department of Psychology & Neuroscience, Duke University Postal: 308 Research Drive, Durham, NC 27708, USA Email: solleo at gmail.com _______________________________________________ fieldtrip mailing list https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e= https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e= _______________________________________________ fieldtrip mailing list https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e= https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e= -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Sat Oct 6 01:25:37 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Fri, 5 Oct 2018 16:25:37 -0700 Subject: [FieldTrip] Hiring for Postdoc (and RA) - Intracranial research Message-ID: The newly opened Cognitive Neurophysiology Lab at UC Davis headed by Dr. Saez is hiring a postdoctoral researcher. Lab research projects will focus on the neurobiological basis of human decision-making behavior, using a combination of intracranial recordings in human patients, neuroeconomic tasks and computational modeling of behavior. Candidates must have a strong background in *in vivo* electrophysiological recordings and/or decision neuroscience. For more information, including details on how to apply, please visit their lab webpage . -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Sun Oct 7 13:52:18 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 7 Oct 2018 07:52:18 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster Message-ID: Dear FieldTrip community, sorry for sending another email out, but I am still having trouble with this issue. I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Mon Oct 8 09:04:37 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 8 Oct 2018 07:04:37 +0000 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster In-Reply-To: References: Message-ID: <7E583DEC-CBEE-4554-9FBC-E0847816C100@donders.ru.nl> Hi Paul, Currently, it’s not possible to use ft_freqstatistics for statistical inference of coherence in an all-by-all channel pair fashion. The reason is that clustering in 4D space is not implemented. What is possible, though, is to compare coherence across conditions between all channels, and a separate reference channel (e.g. EMG). This will probably require some tweaks to the input data (e.g. replace ‘labelcmb’, with ‘label’, and replace ‘dimord’ with ‘chan_freq’). Using one of the EEG channels as a ‘reference’ should be in principle possible as well (at least in terms of algorithm, whether interpretations based on inferential decisions make sense in this case, that’s up to the user). Best wishes, Jan-Mathijs On 7 Oct 2018, at 13:52, Paul Dhami > wrote: Dear FieldTrip community, sorry for sending another email out, but I am still having trouble with this issue. I am attempting to do a between group comparison of coherency values from one reference channel to all other remaining 59 EEG channels. For thoroughness, below is my call to freqanalysis for one participant's data: cfg = []; cfg.output = 'powandcsd'; cfg.method = 'mtmconvol'; cfg.foi = 2:1:40; cfg.t_ftimwin = 2 ./ cfg.foi; cfg.tapsmofrq = 0.4 *cfg.foi; cfg.toi = -0.4:0.01:0.4; dataSPfreq_coh = ft_freqanalysis(cfg, dataSPfreq_coh); and then for my call to connectivity analysis: cfg = [] ; cfg.method = 'coh'; cfg.complex = 'absimag'; mycoh = ft_connectivityanalysis(cfg, dataSPfreq_coh) In an attempt to reduce the pairs in 'labelcmb', I then replaced it with only those pairs of electrodes I was interested in (e.g. F3 and its 59 available pairings with all other electrodes), as well as changed the first dimension of cohspctrm (electrode pairs) to include only those of interest (thus reducing it to 59 x 39 x 81 chncmb_freq_time dimord). I then attempted to apply freqstatistics, by first defining my neighbours which seems fine, and then creating the follow cfg: cfg = []; cfg.neighbours = neighbours; % defined as above %cfg.channelcmb = {'F3', 'FCZ'} cfg.parameter = 'cohspctrm'; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_indepsamplesT'; cfg.correcttail = 'alpha'; cfg.correctm = 'cluster'; cfg.numrandomization = 100; % just for testing purposes cfg.minnbchan = 2; %cfg.latency = mylatency; % latency of interest cfg.spmversion = 'spm12'; I then ran into the error of a 'Reference to non-existent field 'label', an error that no longer appeared once I specified the neighbours information. However, I then into in errors: Error using findcluster (line 50) invalid dimension of spatdimneighbstructmat Error in clusterstat (line 201) posclusobs = findcluster(reshape(postailobs, [cfg.dim,1]),channeighbstructmat,cfg.minnbchan); Error in ft_statistics_montecarlo (line 352) [stat, cfg] = clusterstat(cfg, statrand, statobs); Error in ft_freqstatistics (line 190) [stat, cfg] = statmethod(cfg, dat, design); I had primarily two questions: 1) Going through the findcluster document, is it right to say that this error is related to my editing the chncmbs and the cohspctrm which conflicts with the neighbour I specified. 2) Is the overall pipeline even correct for attempting to do a between groups coherence values analysis? Any help would be greatly appreciated. Best, Paul _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 8 20:49:46 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Mon, 8 Oct 2018 14:49:46 -0400 Subject: [FieldTrip] Coherency cluster analysis between groups - trouble with label field and finecluster (Schoffelen, J.M. (Jan Mathijs)) Message-ID: Hi Jan-Mathijs, thank you very much for your response, it's greatly appreciated. If I approached it from an a priori perspective, and wanted test the coherency difference between two groups for just a pair of electrodes, where one is the reference electrode (e.g. comparing the coherency values of F3 - F4 between two groups), would this be possible? My understanding is that it would become just as any other 3D matrix, with freq-time-subjects becoming the dimensions after squeezing the pair dimension. Would this then be something possible to run freqstatistics on with cluster analysis? Thank you again, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From sauppe.s at gmail.com Tue Oct 9 11:49:56 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Tue, 9 Oct 2018 11:49:56 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? Message-ID: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Dear list members, there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? Thanks a lot! — Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From S.Arana at donders.ru.nl Tue Oct 9 12:55:01 2018 From: S.Arana at donders.ru.nl (Arana, S.L. (Sophie)) Date: Tue, 9 Oct 2018 10:55:01 +0000 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: <1539082501488.92451@donders.ru.nl> Hi Sebastian, option 'normchange' will give you the normalized change to baseline (data-meanbsl)/(data+meanbsl) You can see the formulas for the different options at the end of the ft_freqbaseline code. I am not sure in which situation you would prefer this option over 'relchange', maybe someone else knows? Best, Sophie ________________________________ From: fieldtrip on behalf of Sebastian Sauppe Sent: Tuesday, October 9, 2018 11:49 AM To: fieldtrip at science.ru.nl Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? Dear list members, there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like "absolute" or "db". However, I wasn't able to find out what "normchange" does. Does anyone of you know (where to find information about this)? Thanks a lot! - Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Tue Oct 9 13:12:35 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Tue, 9 Oct 2018 13:12:35 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: Dear Sebastian, 'normchange' will compute (a-b) / (a+b). The best reference on what the options do is of course the source code itself; see here: https://github.com/fieldtrip/fieldtrip/blob/ce5bdd3d6433dbc4ca600eb127f369f3917c02cd/ft_freqbaseline.m#L205 Cheers, Eelke On Tue, 9 Oct 2018 at 11:49, Sebastian Sauppe wrote: > > Dear list members, > > there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? > > Thanks a lot! > — Sebastian > > ----------- > Dr. Sebastian Sauppe > Department of Comparative Linguistics, University of Zurich > Homepage: https://sites.google.com/site/sauppes/ > Twitter: @SebastianSauppe > Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ > ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe > ORCID ID: http://orcid.org/0000-0001-8670-8197 > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From david.schubring at uni-konstanz.de Tue Oct 9 13:27:15 2018 From: david.schubring at uni-konstanz.de (David Schubring) Date: Tue, 9 Oct 2018 13:27:15 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> Message-ID: <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> Dear Sebastian, more information can be found in the code of ft_freqbaseline, where "data" is your poststimulus and "meanVals" is your baseline and normchange does the following: data = (data - meanVals) ./ (data + meanVals) Hope that helps. Best, David PS: The other baseline types do the following: if (strcmp(baselinetype, 'absolute')) data = data - meanVals; elseif (strcmp(baselinetype, 'relative')) data = data ./ meanVals; elseif (strcmp(baselinetype, 'relchange')) data = (data - meanVals) ./ meanVals; elseif (strcmp(baselinetype, 'normchange')) || (strcmp(baselinetype, 'vssum')) data = (data - meanVals) ./ (data + meanVals); elseif (strcmp(baselinetype, 'db')) data = 10*log10(data ./ meanVals); Am 09.10.2018 um 11:49 schrieb Sebastian Sauppe: > Dear list members, > > there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? > > Thanks a lot! > — Sebastian > > ----------- > Dr. Sebastian Sauppe > Department of Comparative Linguistics, University of Zurich > Homepage: https://sites.google.com/site/sauppes/ > Twitter: @SebastianSauppe > Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ > ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe > ORCID ID: http://orcid.org/0000-0001-8670-8197 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From mjones at sanantoniocounseling.net Wed Oct 10 04:54:46 2018 From: mjones at sanantoniocounseling.net (Mark Jones) Date: Tue, 9 Oct 2018 21:54:46 -0500 Subject: [FieldTrip] Moving means Message-ID: <5152EC6D-5544-4406-BC6B-336B3729B983@sanantoniocounseling.net> I have a time frequency series of continuous EEG, segmented in 10 second trials (which gives me 0.1hz resolution). How can I create moving means of the data based on a rolling average of 10 seconds that will update once a second? Thanks, Mark Jones San Antonio, Texas, USA From sauppe.s at gmail.com Wed Oct 10 09:47:38 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Wed, 10 Oct 2018 09:47:38 +0200 Subject: [FieldTrip] What is baseline method "normchange" in ft_freqbaseline? In-Reply-To: <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> References: <42FD2B93-F852-4E1D-8E50-D20D8105F23D@gmail.com> <6d6366a5-2991-41ad-0708-fe1e340edcdc@uni-konstanz.de> Message-ID: <2BAC407B-5208-49E3-A4F5-A0DA0D7E326B@gmail.com> Dear David, thanks a lot, that clarifies what the different methods do. — Sebastian > Am 09.10.2018 um 13:27 schrieb David Schubring : > > Dear Sebastian, > > more information can be found in the code of ft_freqbaseline, where "data" is your poststimulus and "meanVals" is your baseline and normchange does the following: > > data = (data - meanVals) ./ (data + meanVals) > > Hope that helps. > > Best, > David > > PS: The other baseline types do the following: > > if (strcmp(baselinetype, 'absolute')) > data = data - meanVals; > elseif (strcmp(baselinetype, 'relative')) > data = data ./ meanVals; > elseif (strcmp(baselinetype, 'relchange')) > data = (data - meanVals) ./ meanVals; > elseif (strcmp(baselinetype, 'normchange')) || (strcmp(baselinetype, 'vssum')) > data = (data - meanVals) ./ (data + meanVals); > elseif (strcmp(baselinetype, 'db')) > data = 10*log10(data ./ meanVals); > > > Am 09.10.2018 um 11:49 schrieb Sebastian Sauppe: >> Dear list members, >> there are several options for baselining in ft_freqbaseline. Most of them relatively intuitively to understand, like „absolute“ or „db“. However, I wasn’t able to find out what „normchange“ does. Does anyone of you know (where to find information about this)? >> Thanks a lot! >> — Sebastian >> ----------- >> Dr. Sebastian Sauppe >> Department of Comparative Linguistics, University of Zurich >> Homepage: https://sites.google.com/site/sauppes/ >> Twitter: @SebastianSauppe >> Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ >> ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe >> ORCID ID: http://orcid.org/0000-0001-8670-8197 >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > -- > Dr. David Schubring > > General & Biological Psychology > University of Konstanz > Room C524 > P.O. Box 36 > 78457 Konstanz > > Phone: +49-(0)7531-88-5350 > Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From sauppe.s at gmail.com Wed Oct 10 10:21:50 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Wed, 10 Oct 2018 10:21:50 +0200 Subject: [FieldTrip] Question on baselining methods (absolute vs db) Message-ID: Dear FieldTrip list members, I’ve got a question on the different methods available in ft_freqbaseline. I have TFA data from an experiment where participants saw pictures and reacted to them. There is a pre-trial period (-1000 to 0 ms) during which the fixate a fixation cross and a trial period (0 to 1200 ms) during which they see the picture. I used mtmconvol in ft_freqstatistics to get the power. The frequencies are 4 to 20 Hz and I used 3 cycles (cfg.t_ftimwin = 3./cfg.foi). Then I want to baseline the data for plotting and analysis. As baseline period I chose -500 to -200 ms. (But using other baseline periods don’t change the picture.) When I use an absolute baseline, the activity during the baseline period is around 0 and during the trial there are increases and decreases (i.e. positive and negative values). So this looks like one would expect. When I use a dB baseline, however, the activity during the baseline period is not around 0, but rather around -1 to -2 dB. During the trial, I also get values around -1 to -3 dB. This is the command: cfg = []; cfg.baseline = [-0.5 -0.2]; % baseline period = -500 to -200 ms, relative to stimulus onset cfg.baselinetype = 'db'; cfg.parameter = 'powspctrm'; freq_data_baselined = ft_freqbaseline(cfg, freq_data); What could be the reason for this discrepancy between absolute and dB baselines? Thanks a lot for your help already in advance! Regards, Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From wwumedstanford at gmail.com Wed Oct 10 20:23:38 2018 From: wwumedstanford at gmail.com (Wei Wu) Date: Wed, 10 Oct 2018 11:23:38 -0700 Subject: [FieldTrip] Assistant, associate or full professor positions @ Department of Psychiatry and Behavioral Sciences, Stanford University Message-ID: The Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine is seeking new full-time faculty members at the rank of Assistant, Associate, or full Professor in the Medical Center Line. These are positions for mental health clinician scientists who will be based in the Department of Psychiatry and Behavioral Sciences, or at the Veterans Affairs Palo Alto Health Care System with presence in the Departmental programs on the Stanford campus. The chosen candidates will be expected to conduct scholarly research and teaching in the areas of psychiatry or psychology in adult and/or child and adolescent populations across several areas of programmatic need, including mood disorders, anxiety disorders, eating disorders, addiction medicine, public mental health and/or special patient populations, among others. Candidates are required to have a proven track record of success in producing high-quality scholarly work, in addition to excellence in teaching and established clinical expertise in their respective field. Possible candidates could include, but are not limited to, psychiatrists, psychologists, neuropsychiatrists, and neuropsychologists. Physician applicants must have a medical degree or equivalent degree, completed training in General Psychiatry, be board-certified in General Psychiatry, and possess or be fully eligible for a California medical license. Physicians interested in working with children must hold board certification in child psychiatry. Clinical psychologist applicants must have a doctoral degree in psychology or equivalent degree, have completed an APA-approved internship, and possess or be fully eligible for licensure as a psychologist in California. The major criteria for appointment for faculty in the Medical Center Line shall be excellence in the overall mix of clinical care, clinical teaching, scholarly activity that advances clinical medicine, and institutional service appropriate to the programmatic need the individual is expected to fulfill. Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford also welcomes applications from others who would bring additional dimensions to the University’s research, teaching and clinical missions. Interested candidates should send a copy of their curriculum vitae, a brief letter outlining their experiences and interests and the names of three references via e-mail only to: Search Co-Chairs: Natalie Rasgon, MD, Ph.D., and Alan Louie, M.D. c/o Heather Kenna Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine 401 Quarry Road Stanford, CA 94305 Phone: (650) 724-0521 Email: hkenna at stanford.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From fda.bolanos at gmail.com Thu Oct 11 20:19:47 2018 From: fda.bolanos at gmail.com (=?UTF-8?Q?Fernanda_Bola=C3=B1os?=) Date: Thu, 11 Oct 2018 13:19:47 -0500 Subject: [FieldTrip] Problems running FieldTrip Message-ID: Hi! My name is Fernanda and I am using FieldTrip for a school project. I download the most recent version, but I’m having problems to run the code. It happens with each and every code I make. I followed the instructions on how to install the toolbox. I placed the fieldtrip carpet at my (C:) folder and I added the startup.m at the C:\Program Files\MATLAB\R2017b\toolbox\local. May have some help on how to solve the problem, please? Best regards, Fernanda Bolaños -- Ma. Fernanda Bolaños De Regil -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Fri Oct 12 09:29:00 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Fri, 12 Oct 2018 09:29:00 +0200 Subject: [FieldTrip] Problems running FieldTrip In-Reply-To: References: Message-ID: Hi Fernanda, What sort of errors are you getting? In general, make sure you add the FieldTrip folder to your Matlab path and run ft_defaults() in order to check whether everything is OK. Cheers, Eelke On Thu, 11 Oct 2018 at 20:19, Fernanda Bolaños wrote: > Hi! My name is Fernanda and I am using FieldTrip for a school project. > > > > I download the most recent version, but I’m having problems to run the > code. It happens with each and every code I make. I followed the > instructions on how to install the toolbox. > > > > I placed the fieldtrip carpet at my (C:) folder and I added the startup.m > at the C:\Program Files\MATLAB\R2017b\toolbox\local. > > > > May have some help on how to solve the problem, please? > > > > Best regards, > > > > Fernanda Bolaños > > -- > Ma. Fernanda Bolaños De Regil > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Alexander_Nakhnikian at hms.harvard.edu Fri Oct 12 18:34:19 2018 From: Alexander_Nakhnikian at hms.harvard.edu (Nakhnikian, Alexander) Date: Fri, 12 Oct 2018 16:34:19 +0000 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling Message-ID: Dear All, Apologies for the long post, I've tried to be as succinct as possible while describing my problem is sufficient detail. I've been trying to solve a problem with source modeling for sometime. I've found that distributed source estimates (MNE, sLORETA, eLORETA) are heavily biased towards the ventral temporal lobe. This is the case with multiple data sets analyzed using both built-in field trip functions and imaging kernels generated by my own code. It occurs in within group grand averages and statistical contrasts between controls and patients. I've confirmed that sLORETA and eLORETA are unbiased for noiseless data by filtering point sources through the resolution kernel. I'm working on a Mac running OS 10.11.6 and the latest version of Field Trip. I'm using a standard 10/10 electrode layout (no individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) and Field Trip's template BEM. The forward model is restricted to the cortical mantle (I've had similar problems with whole brain forward models as well). I recently ran PCA at the source level to explore the issue. The data were collecting during quiet rest and bandpassed to isolate a peak that accounts for a significant difference between controls and patients at the sensor level. The imaging kernel was applied to the sensor spectral matrix. To obtain the PCs, I analyzed the covariance of power among voxels. The rank of the voxel covariance matrix was 31 with the first 3 eigenvalues account for approximately 95% of the variance. Interestingly, the spatial distribution of the first three principal components exhibited the same bias as the sensor data. When I reconstructed the source data omitting the first 3 components, the control-patient contrast localized to a collection of canonical DMN nodes. It seems extremely odd to me that removing so much of the information present in the original data returns a reasonable source-level contrast while the majority of variance is accounted for by what is clearly bias. I cannot complete this analysis and submit the results unless I can isolate the problem and correct it so I can run the analysis without reducing the rank of the source data. If anyone can speculate on possible reasons for this problem and/or potential solutions I would be grateful. Thank you, Alexander Alexander Nakhnikian, Ph.D. Research Investigator VA Boston Healthcare System Instructor in Psychiatry, Harvard Medical School -------------- next part -------------- An HTML attachment was scrubbed... URL: From kai.hwang at gmail.com Sat Oct 13 05:29:51 2018 From: kai.hwang at gmail.com (Kai Hwang) Date: Fri, 12 Oct 2018 22:29:51 -0500 Subject: [FieldTrip] postdoc position | cognitive neuroscience | University of Iowa Message-ID: The Hwang lab in the Psychological and Brain Sciences Department at the University of Iowa has a fully funded postdoc position available. The Hwang lab focuses on brain network mechanisms, cognitive control, and their developmental processes with a strong emphasis on the human thalamocortical system and neural oscillations. Our research utilizes multimodal neuroimaging (EEG and fMRI), TMS, and lesion studies in combination with network neuroscience approaches. For more info please see: https://kaihwang.github.io/ The lab is affiliated with the DeLTA Center and the Iowa Neuroscience Institute, which offers a collaborative research environment with access to research dedicated 3T and 7T MRI systems, TMS, EEG, neurosurgery patients, and a large lesion patient registry. Postdoc Candidate Qualifications - PhD in Psychology, Neuroscience, or other related disciplines. - Experience with neuroimaging (fMRI, EEG/MEG). - Strong Programming skills (Matlab or Python) The postdoc position is opened immediately until filled. To apply, please send a cover letter and CV to: kai-hwang at uiowa.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Mon Oct 15 04:37:52 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Sun, 14 Oct 2018 22:37:52 -0400 Subject: [FieldTrip] Attempt at cluster analysis on seed to whole brain coherency values Message-ID: Dear Fieldtrip, I have a dataset in which I'd like to compare, between controls and patients, their imaginary coherency values in a seed to whole brain manner. In other words, I would like to calculate the imaginary coherency between a single seed electrode and the rest of the remaining electrodes, and *ultimately use ft_freqstatistics' cluster analysis* to test for a significance difference between the groups' seed-to-whole-scalp coherency maps. >From my understanding, this would require a bit of hacking to implement this. I attempted to do so and describe what I did below, with the goal to eventually create something that would work with freqstatistics : - ran freq analysis with output as 'powandcsd' and method as 'mtmconvolv' - on the results of freqanalysis, ran ft_connectivityanalysis with cfg.method = 'coh' and cfg.complex = 'absimag' - with each subject's resulting structure from ft_connectivityanalysis, I first chose a seed of interest - I then found the indices of the 59 channel combinations in relation to the seed of interest in labelcmb - Using the indices, I then pruned/removed the remaining channel combinations of no interest from both the labelcmb and cohspctrm, reducing cohspctrm to a channel of interest (59) x frequency x time matrix (as in it only included values for the channel combinations of interest) - I rename the dimord as 'chan_freq_time' - create 'label' field with standard electrode labels - I then create powspctrm in my structure, which holds the exact same data as the cohspctrm (created powspctrm strictly for freqstatistics) - removed the fields of 'labelcmb' and 'cohspctrm' - because my powspctrm is missing the seed channel, I then inserted a matrix of ones with the appropriate dimensions into where it should be (e.g. if my seed of interest was channel FCZ, I would then insert into my matrix of ones into the 19th position of the powspctrm, thus shifting the latter matrices so now it becomes a matrix with 60 channels in the appropriate order) >From my understanding, the resulting structure of each subject should contain now the coherency values between the seed of interest and the rest of the electrodes in 'powspctrm'. I then used freq_statistics (in the standard way) with cluster correction to compare the coherency between groups, and from what I can tell with no errors popping up, it worked. I then interpreted the resulting clusters in a similar fashion as you would do for a typical frequency chan-freq-time analysis (instead of power, looking at coherency clusters now). My questions are: 1) In regards to implementation (assuming something like this can even be appropriately implemented), do things look okay? 2) Am I wrong in thinking that the cluster results of freq_statistics can be interpreted in a similar fashion as to a typical frequency chan-freq-time analsyis (just replacing power with coherency)? Sorry for the long-winded email, but any help would be greatly appreciated. Thank you, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From virginie.van.wassenhove at gmail.com Mon Oct 15 10:24:01 2018 From: virginie.van.wassenhove at gmail.com (Virginie van Wassenhove) Date: Mon, 15 Oct 2018 10:24:01 +0200 Subject: [FieldTrip] Fwd: [masters + postdoc fellowships] In-Reply-To: References: Message-ID: Dear colleagues, please, feel free to advertise for those interested in temporal cognition inside and outside the lab! Internships for masters: https://brainthemind.files.wordpress.com/2018/10/wildtimes_masters_add.pdf Postdoctoral fellowship: https://brainthemind.files.wordpress.com/2018/10/wildtimes_postdoc_add.pdf Best wishes, Virginie van Wassenhove *https://brainthemind.com/ * -------------- next part -------------- An HTML attachment was scrubbed... URL: From alexandra.korzeczek at med.uni-goettingen.de Mon Oct 15 12:55:43 2018 From: alexandra.korzeczek at med.uni-goettingen.de (Korzeczek, Alexandra) Date: Mon, 15 Oct 2018 10:55:43 +0000 Subject: [FieldTrip] ft_artifact_threshold not operating properly after resampling data Message-ID: Dear all, does anyone know if ft_resampledata is affecting EEG-data in an adverse manner? I’m asking because I’m having a similar problem wich was already described by Alexandrina Guran on Mon Mar 27 11:34:19 CEST 2017 (subject: Problem with downsampling / automatic artifact rejection) and wanted to ask if a solution has been found why this problem appears? The problem (black and bold) occurs while using the function ft_artifact_threshold: Example: Warning: the trial definition in the configuration is inconsistent with the actual data Warning: reconstructing sampleinfo by assuming that the trials are consecutive segments of a continuous recording threshold artifact scanning: trial 1 from 74 exceeds min-threshold threshold artifact scanning: trial 2 from 74 is ok threshold artifact scanning: trial 3 from 74 is ok threshold artifact scanning: trial 4 from 74 is ok threshold artifact scanning: trial 5 from 74 exceeds min-threshold threshold artifact scanning: trial 6 from 74 exceeds max-threshold Warning: data contains NaNs, no filtering or preprocessing applied I have backtracked why theses NANs occour, and it happens when ft_artifact_threshold (line 160) calls the function ft_fetch data. Within this function (line 167) the Matlab function unique is called. The Warning is posted when the variable utrl stays empty (line 169). Then in line 178 the data (variable dat), which until then are NANs, in the matrix data.trial are not converted into proper values. 167 - utrl = unique(trialnum); 168 - utrl(~isfinite(utrl)) = 0; 169 - utrl(utrl==0) = []; 170 - if length(utrl)==1 171 - ok = trialnum==utrl; 172 - smps = samplenum(ok); 173 - dat(:,ok) = data.trial{utrl}(chanindx,smps); 174 - else 175 - for xlop=1:length(utrl) 176 - ok = trialnum==utrl(xlop); 177 - smps = samplenum(ok); 178 - dat(:,ok) = data.trial{utrl(xlop)}(chanindx,smps); 179 - end 180 - end This problem is independent of my cfg configurations for ft_artifact threshold. However, as Alexandrina found out, it does not appear when I leave out my previous ft_resampledata step. So my question is: Can anybody explain, why resampling my data is giving this effect? And additionally if it is save to use resampling anyway (other ft functions as databrowser or rejectvisual do not seem to be affected by this resampling step – but maybe I just don’t notice that?). I’m using the EGI system with a 256 EEG cap. Here are my current steps before I call ft_artifact_threshold: Definetrial: cfg = []; cfg.dataformat = 'egi_mff_v2'; %uses the new format for egi files cfg.headerformat = 'egi_mff_v2'; cfg.eventformat = 'egi_mff_v2'; cfg.continuous = 'yes'; cfg.trialfun = 'ft_trialfun_general'; cfg.trialdef.eventtype = DIN4; cfg.trialdef.eventvalue = ''; % necessary for mff files. cfg.trialdef.prestim = 1; cfg.trialdef.poststim = 2,5; EEG_trldef_S1= ft_definetrial(cfg) 2x times Selectdata: cfg.trials = keep_trlinf;% specific rules for my experiment EEG_trls= ft_selectdata(cfg, EEG_trldef_S1); Preprocessing: EEG_trls.padding = Pad; EEG_trls.chantype = {'eeg'}; EEG_trls.channel=(1:257); EEG_trls.detrend = 'yes'; EEG_trls.trials = 'all'; EEG_trls.lpfilter ='yes'; EEG_trls.lpfreq = 60; EEG_preproc_1000Hz = ft_preprocessing (EEG_trls); Resample data cfg.resamplefs = 500; EEG_preproc = ft_resampledata(cfg, EEG_preproc_1000Hz); The difference between the data output of ft_preprocessing and ft_resampledata is that the variable sampleinfo is missing after ft_resampledata (see attached picture). Maybe this is the reason why ft_artifact_threshold is not working properly? I hope someone has an explanation? Thank you in advance, Alexandra _________________________________________________________________ Alexandra Korzeczek Wissenschaftliche Mitarbeiterin Klinik für Klinische Neurophysiologie Georg-August-Universität Göttingen Robert-Koch.Str. 40, 37075 Göttingen Tel. 0551- 39-65106 -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Matrix_ft_resampledata.png Type: image/png Size: 22979 bytes Desc: Matrix_ft_resampledata.png URL: From e.spaak at donders.ru.nl Mon Oct 15 13:22:13 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Mon, 15 Oct 2018 13:22:13 +0200 Subject: [FieldTrip] Attempt at cluster analysis on seed to whole brain coherency values In-Reply-To: References: Message-ID: Dear Paul, That all sounds good to me. Note that you could also have specified cfg.parameter = 'cohspctrm' in the call to ft_freqanalysis, so the renaming step to powspctrm was not strictly necessary. One thing you'll want to keep in mind when doing this is that imaginary part of coherency (ImC) is a biased quantity and depends on the number of trials. So comparing two groups statistically will not give you meaningful results if the number of data points for the two groups is unequal. It's worth having a look at this paper which goes into more details on this and related issues: Vinck, Oostenveld, Van Wingerden, Battaglia, & Pennartz. An Improved Index of Phase-Synchronization for Electrophysiological Data in the Presence of Volume-Conduction, Noise and Sample-Size Bias. NeuroImage 55, no. 4 (April 15, 2011): 1548–65. https://doi.org/10.1016/j.neuroimage.2011.01.055. Cheers, Eelke On Mon, 15 Oct 2018 at 04:37, Paul Dhami wrote: > > Dear Fieldtrip, > > I have a dataset in which I'd like to compare, between controls and patients, their imaginary coherency values in a seed to whole brain manner. In other words, I would like to calculate the imaginary coherency between a single seed electrode and the rest of the remaining electrodes, and ultimately use ft_freqstatistics' cluster analysis to test for a significance difference between the groups' seed-to-whole-scalp coherency maps. > > From my understanding, this would require a bit of hacking to implement this. I attempted to do so and describe what I did below, with the goal to eventually create something that would work with freqstatistics : > > ran freq analysis with output as 'powandcsd' and method as 'mtmconvolv' > on the results of freqanalysis, ran ft_connectivityanalysis with cfg.method = 'coh' and cfg.complex = 'absimag' > with each subject's resulting structure from ft_connectivityanalysis, I first chose a seed of interest > I then found the indices of the 59 channel combinations in relation to the seed of interest in labelcmb > Using the indices, I then pruned/removed the remaining channel combinations of no interest from both the labelcmb and cohspctrm, reducing cohspctrm to a channel of interest (59) x frequency x time matrix (as in it only included values for the channel combinations of interest) > I rename the dimord as 'chan_freq_time' > create 'label' field with standard electrode labels > I then create powspctrm in my structure, which holds the exact same data as the cohspctrm (created powspctrm strictly for freqstatistics) > removed the fields of 'labelcmb' and 'cohspctrm' > because my powspctrm is missing the seed channel, I then inserted a matrix of ones with the appropriate dimensions into where it should be (e.g. if my seed of interest was channel FCZ, I would then insert into my matrix of ones into the 19th position of the powspctrm, thus shifting the latter matrices so now it becomes a matrix with 60 channels in the appropriate order) > > From my understanding, the resulting structure of each subject should contain now the coherency values between the seed of interest and the rest of the electrodes in 'powspctrm'. > > I then used freq_statistics (in the standard way) with cluster correction to compare the coherency between groups, and from what I can tell with no errors popping up, it worked. I then interpreted the resulting clusters in a similar fashion as you would do for a typical frequency chan-freq-time analysis (instead of power, looking at coherency clusters now). > > My questions are: > 1) In regards to implementation (assuming something like this can even be appropriately implemented), do things look okay? > 2) Am I wrong in thinking that the cluster results of freq_statistics can be interpreted in a similar fashion as to a typical frequency chan-freq-time analsyis (just replacing power with coherency)? > > Sorry for the long-winded email, but any help would be greatly appreciated. > > Thank you, > Paul > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From jan.schoffelen at donders.ru.nl Tue Oct 16 09:51:22 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Tue, 16 Oct 2018 07:51:22 +0000 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling In-Reply-To: References: Message-ID: Dear Alexander, I am not sure whether I completely follow your diagnostic steps and your conclusions, but I would also check: - the alignment between the electrodes, volume conduction model, and source model. - are the electrode positions expressed in the same coordinate system as the volume conductor/source model? - are the leadfields well-behaved? For instance: if the dipole locations are too close to the innermost mesh, numerical problems may arise. Best wishes, Jan-Mathijs On 12 Oct 2018, at 18:34, Nakhnikian, Alexander > wrote: Dear All, Apologies for the long post, I've tried to be as succinct as possible while describing my problem is sufficient detail. I've been trying to solve a problem with source modeling for sometime. I've found that distributed source estimates (MNE, sLORETA, eLORETA) are heavily biased towards the ventral temporal lobe. This is the case with multiple data sets analyzed using both built-in field trip functions and imaging kernels generated by my own code. It occurs in within group grand averages and statistical contrasts between controls and patients. I've confirmed that sLORETA and eLORETA are unbiased for noiseless data by filtering point sources through the resolution kernel. I'm working on a Mac running OS 10.11.6 and the latest version of Field Trip. I'm using a standard 10/10 electrode layout (no individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) and Field Trip's template BEM. The forward model is restricted to the cortical mantle (I've had similar problems with whole brain forward models as well). I recently ran PCA at the source level to explore the issue. The data were collecting during quiet rest and bandpassed to isolate a peak that accounts for a significant difference between controls and patients at the sensor level. The imaging kernel was applied to the sensor spectral matrix. To obtain the PCs, I analyzed the covariance of power among voxels. The rank of the voxel covariance matrix was 31 with the first 3 eigenvalues account for approximately 95% of the variance. Interestingly, the spatial distribution of the first three principal components exhibited the same bias as the sensor data. When I reconstructed the source data omitting the first 3 components, the control-patient contrast localized to a collection of canonical DMN nodes. It seems extremely odd to me that removing so much of the information present in the original data returns a reasonable source-level contrast while the majority of variance is accounted for by what is clearly bias. I cannot complete this analysis and submit the results unless I can isolate the problem and correct it so I can run the analysis without reducing the rank of the source data. If anyone can speculate on possible reasons for this problem and/or potential solutions I would be grateful. Thank you, Alexander Alexander Nakhnikian, Ph.D. Research Investigator VA Boston Healthcare System Instructor in Psychiatry, Harvard Medical School _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Tue Oct 16 11:47:46 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 16 Oct 2018 11:47:46 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' Message-ID: Dear all, I have pre-processed some ERP data in BrainVision and marked bad segments instead of removing them. I now want to import this data into Fieldtrip, but how do I remove the marked segments? The following tells me that there are 10 bad segments out of 44, but when I set event type to 'Stimulus' and add the triggers at event values, it imports all 44 segments. cfg = []; cfg.dataset = '../data/export/28_preproc.dat'; cfg.trialdef.eventtype = 'Bad Interval'; ft_definetrial(cfg); Do I need to write my own conditional trialfun? If so, how? I've tried editing the tutorial example but I'm new to Matlab and am struggling. Thanks in advance, Kate -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From matti.stenroos at aalto.fi Tue Oct 16 12:16:11 2018 From: matti.stenroos at aalto.fi (Matti Stenroos) Date: Tue, 16 Oct 2018 13:16:11 +0300 Subject: [FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling In-Reply-To: References: Message-ID: Dear Alexander, Also I could not fully follow the pipeline. The terms "sensor spectral matrix" and "contrast" however, caught my eye. Is your "data vector" that you try to map on the brain a result of only linear processing of the measurement? Cheers, Matti On 2018-10-16 10:51, Schoffelen, J.M. (Jan Mathijs) wrote: > Dear Alexander, > > I am not sure whether I completely follow your diagnostic steps and your > conclusions, but I would also check: > > - the alignment between the electrodes, volume conduction model, and > source model. > - are the electrode positions expressed in the same coordinate system as > the volume conductor/source model? > - are the leadfields well-behaved? For instance: if the dipole locations > are too close to the innermost mesh, numerical problems may arise. > > Best wishes, > Jan-Mathijs > > >> On 12 Oct 2018, at 18:34, Nakhnikian, Alexander >> > > wrote: >> >> Dear All, >> >> Apologies for the long post, I've tried to be as succinct as possible >> while describing my problem is sufficient detail. >> >> I've been trying to solve a problem with source modeling for sometime. >> I've found that distributed source estimates (MNE, sLORETA, eLORETA) >> are heavily biased towards the ventral temporal lobe. This is the case >> with multiple data sets analyzed using both built-in field trip >> functions and imaging kernels generated by my own code. It occurs in >> within group grand averages and statistical contrasts between controls >> and patients. I've confirmed that sLORETA and eLORETA are unbiased for >> noiseless data by filtering point sources through the resolution >> kernel. I'm working on a Mac running OS 10.11.6 and the latest version >> of Field Trip. I'm using a standard 10/10 electrode layout (no >> individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) >> and Field Trip's template BEM. The forward model is restricted to the >> cortical mantle (I've had similar problems with whole brain forward >> models as well). >> >> I recently ran PCA at the source level to explore the issue. The data >> were collecting during quiet rest and bandpassed to isolate a peak >> that accounts for a significant difference between controls and >> patients at the sensor level. The imaging kernel was applied to the >> sensor spectral matrix. To obtain the PCs, I analyzed the covariance >> of power among voxels. The rank of the voxel covariance matrix was 31 >> with the first 3 eigenvalues account for approximately 95% of the >> variance. Interestingly, the spatial distribution of the first three >> principal components exhibited the same bias as the sensor data. When >> I reconstructed the source data/omitting/the first 3 components, the >> control-patient contrast localized to a collection of canonical DMN >> nodes. >> >> It seems extremely odd to me that removing so much of the information >> present in the original data returns a reasonable source-level >> contrast while the majority of variance is accounted for by what is >> clearly bias. I cannot complete this analysis and submit the results >> unless I can isolate the problem and correct it so I can run the >> analysis without reducing the rank of the source data. If anyone can >> speculate on possible reasons for this problem and/or potential >> solutions I would be grateful. >> >> Thank you, >> >> Alexander >> >> Alexander Nakhnikian, Ph.D. >> Research Investigator >> VA Boston Healthcare System >> Instructor in Psychiatry, Harvard Medical School >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > From bioeng.yoosofzadeh at gmail.com Tue Oct 16 22:12:06 2018 From: bioeng.yoosofzadeh at gmail.com (Vahab Yousofzadeh) Date: Tue, 16 Oct 2018 15:12:06 -0500 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: Hi Kate, if it is a continuous data (before epoching), you can treat bad segments as an artifact (e.g. eog or muscle, etc) and do something like this, % artifact_EOG = [100 500]; % in sample cfg = []; cfg.artfctdef.eog.artifact = artifact_EOG; cfg.artfctdef.reject = 'value'; % cfg.artfctdef.value = 0; % replacing values with nan or 0 data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is the output from ft_preprocessing. % inspecting cleaned data cfg = []; cfg.continuous = 'yes'; cfg.viewmode = 'vertical'; % all channels seperate cfg.blocksize = 5; % view the continous data in 30-s blocks ft_databrowser(cfg, data_continuous_eog_clean); if the data is epoched, simply use ft_redefinetrial. Best, Vahab From alessandro.orticoni at gmail.com Tue Oct 16 22:56:16 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Tue, 16 Oct 2018 22:56:16 +0200 Subject: [FieldTrip] ft_preprocessing filter order Message-ID: Dear all, I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. Thanks a lot, Alessandro Orticoni -------------- next part -------------- An HTML attachment was scrubbed... URL: From abela.eugenio at gmail.com Tue Oct 16 23:57:12 2018 From: abela.eugenio at gmail.com (Eugenio Abela) Date: Tue, 16 Oct 2018 22:57:12 +0100 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi Alessandro, any defaults are set in the low-level functions that ft_preprocessing calls. So, for the lowpass filter, go have a look at the help of ft_preproc_lowpassfilter.m, it says: % FT_PREPROC_LOWPASSFILTER applies a low-pass filter to the data and thereby % removes all high frequency components in the data % % Use as % [filt] = ft_preproc_lowpassfilter(dat, Fsample, Flp, N, type, dir, instabilityfix) % where % dat data matrix (Nchans X Ntime) % Fsample sampling frequency in Hz % Flp filter frequency % N optional filter order, default is 6 (but) or dependent upon % frequency band and data length (fir/firls) etc... The default for the Butterworth low-pass filter is thus 6 (for the bandpass it’s 4). You can set your preferred order when calling ft_preprocessing.m e.g. via cfg.lpfiltord = 4 or whatever makes sense. Hope that helps Eugenio On 16 Oct 2018, at 21:56, Alessandro Orticoni wrote: Dear all, I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. Thanks a lot, Alessandro Orticoni _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From dlozanosoldevilla at gmail.com Wed Oct 17 00:15:43 2018 From: dlozanosoldevilla at gmail.com (Diego Lozano-Soldevilla) Date: Wed, 17 Oct 2018 00:15:43 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi Alessandro, As explained in the help of ft_preprocessing, the default filter order can be found in the low level functions: fieldtrip/preproc/ft_preproc_bandpassfilter.m fieldtrip/preproc/ft_preproc_bandstopfilter.m fieldtrip/preproc/ft_preproc_highpassfilter.m fieldtrip/preproc/ft_preproc_lowpassfilter.m The filter order means something very different for the Butterworth (i.e. amount of samples used for the input-output recursion) https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 or for the Finite Impulse Response (FIR) filter (i.e. length of the filter kernel). https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 For the ones interested to know more about what that number does for different filters, please check this example script: http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics I hope that helps, Diego On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < alessandro.orticoni at gmail.com> wrote: > Dear all, > > I would like to ask you just a question: which is the default order of the > filters implemented by ft_preprocessing? I cannot find it anywhere. > > Thanks a lot, > Alessandro Orticoni > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.orticoni at gmail.com Wed Oct 17 00:54:24 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Wed, 17 Oct 2018 00:54:24 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: Hi, Yes, thanks both! You have been very helpful. Best, Alessandro Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla < dlozanosoldevilla at gmail.com> ha scritto: > Hi Alessandro, > > As explained in the help > of > ft_preprocessing, the default filter order can be found in the low level > functions: > fieldtrip/preproc/ft_preproc_bandpassfilter.m > fieldtrip/preproc/ft_preproc_bandstopfilter.m > fieldtrip/preproc/ft_preproc_highpassfilter.m > fieldtrip/preproc/ft_preproc_lowpassfilter.m > > The filter order means something very different for the Butterworth (i.e. > amount of samples used for the input-output recursion) > > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 > > or for the Finite Impulse Response (FIR) filter (i.e. length of the filter > kernel). > > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 > > For the ones interested to know more about what that number does for > different filters, please check this example script: > > http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics > > I hope that helps, > > Diego > > > On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < > alessandro.orticoni at gmail.com> wrote: > >> Dear all, >> >> I would like to ask you just a question: which is the default order of >> the filters implemented by ft_preprocessing? I cannot find it anywhere. >> >> Thanks a lot, >> Alessandro Orticoni >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From k.kessler at aston.ac.uk Wed Oct 17 11:25:06 2018 From: k.kessler at aston.ac.uk (Kessler, Klaus) Date: Wed, 17 Oct 2018 09:25:06 +0000 Subject: [FieldTrip] Lectureship (faculty post) at Aston University Message-ID: Dear Fieldtrippers We would be grateful if you would disseminate or consider yourself applying for our faculty post at Aston University. We are particularly keen to strengthen our MEG group, but are open to other method backgrounds. For further details please follow the link. Many thanks Klaus https://jobs.aston.ac.uk/Vacancy.aspx?ref=R180444 Klaus Kessler (Professor of Cognitive Neuroscience) http://www.aston.ac.uk/lhs/staff/az-index/prof-klaus-kessler/ Aston Brain Centre (ABC), Aston Laboratory for Immersive Virtual Environments (ALIVE) School of Life and Health Sciences Aston University Aston Triangle Birmingham, B4 7ET Phone: +44 (0)121 204 3187 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Wed Oct 17 12:01:46 2018 From: katemsto at gmail.com (K S) Date: Wed, 17 Oct 2018 12:01:46 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: Hi Vahab, Thanks for the response. The data is already epoched so I tried ft_redefine trial as you suggested. I think the problem is that the segments marked as artefact are also marked with triggers. I therefore need some way of saying: "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried it also with ft_rejectartifact but I'm not sure how to get it to recognise the 'Bad Interval' marking. Any ideas? Many thanks, Kate On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < bioeng.yoosofzadeh at gmail.com> wrote: > Hi Kate, > > if it is a continuous data (before epoching), you can treat bad > segments as an artifact (e.g. eog or muscle, etc) and do something > like this, > > % artifact_EOG = [100 500]; % in sample > > cfg = []; > cfg.artfctdef.eog.artifact = artifact_EOG; > cfg.artfctdef.reject = 'value'; > % cfg.artfctdef.value = 0; % replacing values with nan or 0 > data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is > the output from ft_preprocessing. > > % inspecting cleaned data > cfg = []; > cfg.continuous = 'yes'; > cfg.viewmode = 'vertical'; % all channels seperate > cfg.blocksize = 5; % view the continous data in 30-s blocks > ft_databrowser(cfg, data_continuous_eog_clean); > > if the data is epoched, simply use ft_redefinetrial. > > Best, > Vahab > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Wed Oct 17 12:48:11 2018 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Wed, 17 Oct 2018 12:48:11 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: References: Message-ID: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> Hi Alessandro If you would want to apply them in a different order, just call ft_preprocessing multiple times, once for every filter you want to apply. best Robert > On 17 Oct 2018, at 00:54, Alessandro Orticoni wrote: > > Hi, > > Yes, thanks both! You have been very helpful. > > Best, > Alessandro > > Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla > ha scritto: > Hi Alessandro, > > As explained in the help of ft_preprocessing, the default filter order can be found in the low level functions: > fieldtrip/preproc/ft_preproc_bandpassfilter.m > fieldtrip/preproc/ft_preproc_bandstopfilter.m > fieldtrip/preproc/ft_preproc_highpassfilter.m > fieldtrip/preproc/ft_preproc_lowpassfilter.m > > The filter order means something very different for the Butterworth (i.e. amount of samples used for the input-output recursion) > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 > > or for the Finite Impulse Response (FIR) filter (i.e. length of the filter kernel). > https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 > > For the ones interested to know more about what that number does for different filters, please check this example script: > http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics > > I hope that helps, > > Diego > > > On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni > wrote: > Dear all, > > I would like to ask you just a question: which is the default order of the filters implemented by ft_preprocessing? I cannot find it anywhere. > > Thanks a lot, > Alessandro Orticoni > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From dmatthes at cbs.mpg.de Wed Oct 17 14:48:29 2018 From: dmatthes at cbs.mpg.de (Daniel Matthes) Date: Wed, 17 Oct 2018 14:48:29 +0200 (CEST) Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: References: Message-ID: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> Hi Kate, I did it in the following way: First, I did a regular import by ignoring the 'Bad Interval' markers. % ------------------------------------------------------------------------- % Data import % ------------------------------------------------------------------------- cfg = []; cfg.dataset = headerfile; cfg.trialfun = 'ft_trialfun_brainvision_segmented'; cfg.stimformat = 'S %d'; cfg.showcallinfo = 'no'; cfg = ft_definetrial(cfg); data = ft_preprocessing(cfg); After that step I have all trials in my data structure, also the trials which have bad intervals. In the second step I did this with the data: % ------------------------------------------------------------------------- % Estimate artifacts % ------------------------------------------------------------------------- events = data.cfg.event; % extract all events from the data structure artifact = zeros(length(events), 2); % allocate memory for the artifact array j = 1; for i=1:1:length(events) if(strcmp(events(i).type, 'Bad Interval')) % search for bad interval events artifact(j,1)=events(i).sample; % create artifact matrix artifact(j,2)=events(i).sample + events(i).duration - 1; j = j +1; end end artifact = artifact(1:j-1, :); % prune the artifact array to its actual size % ------------------------------------------------------------------------- % Revise data % ------------------------------------------------------------------------- cfg = []; cfg.event = events; cfg.artfctdef.reject = 'complete'; cfg.artfctdef.feedback = 'no'; cfg.artfctdef.xxx.artifact = artifact; cfg.showcallinfo = 'no'; data = ft_rejectartifact(cfg, data); These line are removing all trials with bad intervals completely from the data structure. But if you set the option cfg.artfctdef.reject to another value i.e. 'partial', you can also remove only the bad parts of certain trials I wrote this code some time ago, today I would replace the for-cycle with some more effective code. But in general it should work. All the best, Daniel ----- Original Message ----- From: "K S" To: "bioeng yoosofzadeh" Cc: fieldtrip at science.ru.nl Sent: Wednesday, October 17, 2018 12:01:46 PM Subject: Re: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' Hi Vahab, Thanks for the response. The data is already epoched so I tried ft_redefine trial as you suggested. I think the problem is that the segments marked as artefact are also marked with triggers. I therefore need some way of saying: "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried it also with ft_rejectartifact but I'm not sure how to get it to recognise the 'Bad Interval' marking. Any ideas? Many thanks, Kate On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < bioeng.yoosofzadeh at gmail.com > wrote: Hi Kate, if it is a continuous data (before epoching), you can treat bad segments as an artifact (e.g. eog or muscle, etc) and do something like this, % artifact_EOG = [100 500]; % in sample cfg = []; cfg.artfctdef.eog.artifact = artifact_EOG; cfg.artfctdef.reject = 'value'; % cfg.artfctdef.value = 0; % replacing values with nan or 0 data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is the output from ft_preprocessing. % inspecting cleaned data cfg = []; cfg.continuous = 'yes'; cfg.viewmode = 'vertical'; % all channels seperate cfg.blocksize = 5; % view the continous data in 30-s blocks ft_databrowser(cfg, data_continuous_eog_clean); if the data is epoched, simply use ft_redefinetrial. Best, Vahab -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From katemsto at gmail.com Wed Oct 17 17:59:52 2018 From: katemsto at gmail.com (Kate Stone) Date: Wed, 17 Oct 2018 17:59:52 +0200 Subject: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' In-Reply-To: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> References: <1939387851.102904.1539780509389.JavaMail.zimbra@cbs.mpg.de> Message-ID: Brilliant! Thanks Daniel, this worked perfectly. On Wed., 17 Oct. 2018, 15:35 Daniel Matthes, wrote: > Hi Kate, > > I did it in the following way: > > First, I did a regular import by ignoring the 'Bad Interval' markers. > > % ------------------------------------------------------------------------- > % Data import > % ------------------------------------------------------------------------- > > cfg = []; > cfg.dataset = headerfile; > cfg.trialfun = 'ft_trialfun_brainvision_segmented'; > cfg.stimformat = 'S %d'; > cfg.showcallinfo = 'no'; > > cfg = ft_definetrial(cfg); > data = ft_preprocessing(cfg); > > After that step I have all trials in my data structure, also the trials > which have bad intervals. > > In the second step I did this with the data: > > % ------------------------------------------------------------------------- > % Estimate artifacts > % ------------------------------------------------------------------------- > events = data.cfg.event; > % extract all events from the data structure > artifact = zeros(length(events), 2); > % allocate memory for the artifact array > j = 1; > > for i=1:1:length(events) > if(strcmp(events(i).type, 'Bad Interval')) > % search for bad interval events > artifact(j,1)=events(i).sample; > % create artifact matrix > artifact(j,2)=events(i).sample + events(i).duration - 1; > j = j +1; > end > end > > artifact = artifact(1:j-1, :); > % prune the artifact array to its actual size > > % ------------------------------------------------------------------------- > % Revise data > % ------------------------------------------------------------------------- > cfg = []; > cfg.event = events; > cfg.artfctdef.reject = 'complete'; > cfg.artfctdef.feedback = 'no'; > cfg.artfctdef.xxx.artifact = artifact; > cfg.showcallinfo = 'no'; > > data = ft_rejectartifact(cfg, data); > > These line are removing all trials with bad intervals completely from the > data structure. But if you set the option cfg.artfctdef.reject to another > value i.e. 'partial', you can also remove only the bad parts of certain > trials > > I wrote this code some time ago, today I would replace the for-cycle with > some more effective code. But in general it should work. > > All the best, > Daniel > > ----- Original Message ----- > From: "K S" > To: "bioeng yoosofzadeh" > Cc: fieldtrip at science.ru.nl > Sent: Wednesday, October 17, 2018 12:01:46 PM > Subject: Re: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals' > > Hi Vahab, > > Thanks for the response. > > The data is already epoched so I tried ft_redefine trial as you suggested. > I think the problem is that the segments marked as artefact are also marked > with triggers. I therefore need some way of saying: > > "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'" > > I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried > it also with ft_rejectartifact but I'm not sure how to get it to recognise > the 'Bad Interval' marking. > > Any ideas? > > Many thanks, > Kate > > > On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh < > bioeng.yoosofzadeh at gmail.com > wrote: > > > Hi Kate, > > if it is a continuous data (before epoching), you can treat bad > segments as an artifact (e.g. eog or muscle, etc) and do something > like this, > > % artifact_EOG = [100 500]; % in sample > > cfg = []; > cfg.artfctdef.eog.artifact = artifact_EOG; > cfg.artfctdef.reject = 'value'; > % cfg.artfctdef.value = 0; % replacing values with nan or 0 > data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is > the output from ft_preprocessing. > > % inspecting cleaned data > cfg = []; > cfg.continuous = 'yes'; > cfg.viewmode = 'vertical'; % all channels seperate > cfg.blocksize = 5; % view the continous data in 30-s blocks > ft_databrowser(cfg, data_continuous_eog_clean); > > if the data is epoched, simply use ft_redefinetrial. > > Best, > Vahab > > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From alessandro.orticoni at gmail.com Wed Oct 17 18:43:50 2018 From: alessandro.orticoni at gmail.com (Alessandro Orticoni) Date: Wed, 17 Oct 2018 18:43:50 +0200 Subject: [FieldTrip] ft_preprocessing filter order In-Reply-To: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> References: <2AB542EE-3C1D-492E-A635-DCE10D6AE843@donders.ru.nl> Message-ID: Hi Robert, Thanks a lot. Best, Alessandro Il giorno mer 17 ott 2018 alle ore 13:09 Robert Oostenveld < r.oostenveld at donders.ru.nl> ha scritto: > Hi Alessandro > > If you would want to apply them in a different order, just call > ft_preprocessing multiple times, once for every filter you want to apply. > > best > Robert > > > On 17 Oct 2018, at 00:54, Alessandro Orticoni < > alessandro.orticoni at gmail.com> wrote: > > Hi, > > Yes, thanks both! You have been very helpful. > > Best, > Alessandro > > Il giorno mer 17 ott 2018 alle ore 00:17 Diego Lozano-Soldevilla < > dlozanosoldevilla at gmail.com> ha scritto: > >> Hi Alessandro, >> >> As explained in the help >> of >> ft_preprocessing, the default filter order can be found in the low level >> functions: >> fieldtrip/preproc/ft_preproc_bandpassfilter.m >> fieldtrip/preproc/ft_preproc_bandstopfilter.m >> fieldtrip/preproc/ft_preproc_highpassfilter.m >> fieldtrip/preproc/ft_preproc_lowpassfilter.m >> >> The filter order means something very different for the Butterworth (i.e. >> amount of samples used for the input-output recursion) >> >> https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L151 >> >> or for the Finite Impulse Response (FIR) filter (i.e. length of the >> filter kernel). >> >> https://github.com/fieldtrip/fieldtrip/blob/master/preproc/ft_preproc_bandpassfilter.m#L235 >> >> For the ones interested to know more about what that number does for >> different filters, please check this example script: >> >> http://www.fieldtriptoolbox.org/example/determine_the_filter_characteristics >> >> I hope that helps, >> >> Diego >> >> >> On Tue, 16 Oct 2018 at 23:13, Alessandro Orticoni < >> alessandro.orticoni at gmail.com> wrote: >> >>> Dear all, >>> >>> I would like to ask you just a question: which is the default order of >>> the filters implemented by ft_preprocessing? I cannot find it anywhere. >>> >>> Thanks a lot, >>> Alessandro Orticoni >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >>> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From sauppe.s at gmail.com Thu Oct 18 12:49:00 2018 From: sauppe.s at gmail.com (Sebastian Sauppe) Date: Thu, 18 Oct 2018 12:49:00 +0200 Subject: [FieldTrip] Interpolate outlier power values within trials Message-ID: <52E7BDAC-086E-4606-BB0E-060C21807A96@gmail.com> Dear FieldTrip list members, I have EEG data that I transform to power values with ft_freqanalysis. Is there a way to identify for each trial (= each frequency time series within each trial) outlier values that are much higher and lower than the other values in that frequency for that trial and then interpolate them? Regards, Sebastian ----------- Dr. Sebastian Sauppe Department of Comparative Linguistics, University of Zurich Homepage: https://sites.google.com/site/sauppes/ Twitter: @SebastianSauppe Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe ORCID ID: http://orcid.org/0000-0001-8670-8197 -------------- next part -------------- An HTML attachment was scrubbed... URL: From danielkrauel96 at gmail.com Fri Oct 19 11:03:06 2018 From: danielkrauel96 at gmail.com (Daniel Krauel) Date: Fri, 19 Oct 2018 11:03:06 +0200 Subject: [FieldTrip] Fieldtrip Problem ft_default Message-ID: Subject: MCG Dear community, My name is Daniel Krauel and I am working for the UKSH in Kiel Germany. Currently I am analysing data of a MCG measurement. I am currently at the fieldtrip version from 27.05.2017 I used fieldtrip to read and extract the datas and also for plotting of sensor maps.It worked perfectly fine till one start. I couldn`t use ft_defaults, only executing 2 lines add path and ft default to get the error message below. addpath('C:\Users\Daniel\Documents\MATLAB\Till_2\fieldtrip-20170527'); ft_defaults; Undefined function or variable 'ft_platform_supports'. Error in ft_defaults>checkMultipleToolbox (line 280) if ~ft_platform_supports('which-all') Error in ft_defaults (line 109) checkMultipleToolbox('FieldTrip', 'ft_defaults.m'); Can someone explain me how I fix this problem? It looks so easy because I only execute two lines but I can`t fix it. I tried 4 different versions of fieldtrip and I also reinstalled matlab and resets all settings. In Addition the error message changes a little if I am using an other version of fieldtrip. Yours sincerly, Daniel Krauel -------------- next part -------------- An HTML attachment was scrubbed... URL: From gopalar.ccf at gmail.com Fri Oct 19 19:34:35 2018 From: gopalar.ccf at gmail.com (Raghavan Gopalakrishnan) Date: Fri, 19 Oct 2018 13:34:35 -0400 Subject: [FieldTrip] single subject coherence statistics In-Reply-To: <01d601d467d0$a397a570$eac6f050$@gmail.com> References: <01d601d467d0$a397a570$eac6f050$@gmail.com> Message-ID: <01e501d467d2$013c2920$03b47b60$@gmail.com> Dear all, There has been a lot of discussion on the topic of finding statistical significance between two conditions within the same subject, but there still seems to be some lack of clarity and issues. As I understand, The first step is to compute frequencies using ft_freqanalysis, with keep trials as ‘yes’ and output=’fourier’ cfg = []; cfg.output = 'fourier'; cfg.method = 'mtmfft'; cfg.foilim = [5 100]; cfg.tapsmofrq = 5; cfg.keeptrials = 'yes'; cfg.trials = find(data_redef.trialinfo(:,1)==trialopt); freq = ft_freqanalysis(cfg, data_redef); freq = struct with fields: label: {36×1 cell} dimord: 'rpttap_chan_freq' freq: [1×191 double] fourierspctrm: [1444×36×191 double] cumsumcnt: [76×1 double] cumtapcnt: [76×1 double] trialinfo: [76×5 double] cfg: [1×1 struct] The second step is to compute statistics using cfg.statistic = ‘indepsamplesZcoh’ Below is the example code cfg1 = []; cfg1.method = 'montecarlo'; cfg1.statistic = 'indepsamplesZcoh'; cfg1.correctm = 'cluster'; cfg1.clusteralpha = 0.05; cfg1.minnbchan = 2; cfg1.neighbours = neighbours; % neighbours computed separately before cfg1.tail = 0; % -1, 1 or 0 (default = 0); one-sided or two-sided test cfg1.clustertail = 0; cfg1.alpha = 0.025; % alpha level of the permutation test cfg1.numrandomization = 500; % number of draws from the permutation distribution cfg1.parameter = 'fourierspctrm'; design = zeros(1,size(freq1.fourierspctrm,1) + size(freq2.fourierspctrm,1)); design(1,1:size(freq1.fourierspctrm,1)) = 1; design(1,(size(freq1.fourierspctrm,1)+1):(size(freq1.fourierspctrm,1) + size(freq2.fourierspctrm,1)))= 2; cfg1.design = design'; cfg1.ivar = 1; cfg1.channelcmb = {'FC1' 'REMG'}; % coherence between two channels cfg1.computecritval = 'yes'; stat=ft_freqstatistics(cfg1, freq1, freq2); % freq1 and freq2 are two conditions from same subject But the second step gives an error because of dimension issues with the data Assignment has more non-singleton rhs dimensions than non-singleton subscripts Error in ft_statistics_montecarlo (line 319) statrand(:,i) = dum.stat; Error in ft_freqstatistics (line 193) [stat, cfg] = statmethod(cfg, dat, design); Error in Griptask_RT_coherence_analysis (line 140) stat=ft_freqstatistics(cfg1, freq1, freq2); There are couple of previous posts (below) that reported the same kind of error, but there is no solution yet https://mailman.science.ru.nl/pipermail/fieldtrip/2013-July/006821.html https://mailman.science.ru.nl/pipermail/fieldtrip/2010-June/002900.html In the below post, Jan-Mathijs says not to use ‘cluster’ for correctm option. However, if we don’t use then how to reproduce the effects reported in “Nonparametric statistical testing of coherence differences” paper? https://mailman.science.ru.nl/pipermail/fieldtrip/2010-April/002830.html Is this error due to a bug? Or am I doing any mistake? I appreciate developers addressing this issue. Thanks, Raghavan -------------- next part -------------- An HTML attachment was scrubbed... URL: From shaegens at gmail.com Mon Oct 22 23:19:34 2018 From: shaegens at gmail.com (Saskia Haegens) Date: Mon, 22 Oct 2018 17:19:34 -0400 Subject: [FieldTrip] postdoc opportunity at donders Message-ID: Applications are invited for a postdoc position in my lab, studying oscillations using MEG. For details please see: https://www.ru.nl/english/working-at/jobopportunities/details/details-vacature/?recid=601751 -- Saskia Haegens, PhD haegenslab.com -------------- next part -------------- An HTML attachment was scrubbed... URL: From conny.quaedflieg at maastrichtuniversity.nl Tue Oct 23 00:18:57 2018 From: conny.quaedflieg at maastrichtuniversity.nl (Quaedflieg, Conny (PSYCHOLOGY)) Date: Mon, 22 Oct 2018 22:18:57 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis between Groups - GA, plotting and statistics Message-ID: <4ac933a569fb432fae71404f91a37f0e@UM-MAIL3218.unimaas.nl> Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Tue Oct 23 14:04:15 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 23 Oct 2018 14:04:15 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs Message-ID: Hi all, I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. Thanks in advance, Kate -------------- next part -------------- An HTML attachment was scrubbed... URL: From m.manahova at gmail.com Tue Oct 23 14:55:30 2018 From: m.manahova at gmail.com (Mariya Manahova) Date: Tue, 23 Oct 2018 14:55:30 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, You mention that you're using indepsamplesT, so are you doing a test between groups (comparing two different groups) or within participants (comparing the same participants in two conditions)? If it's the latter, then I'd suggest using depsamplesT. I am pasting below a good way to define your design matrix. This works well for a within-participant comparison. You'll need to adapt it if yours is indeed between groups. See the comments for the explanation. Nsub = 29; cfg.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)]; cfg.design(2,1:2*Nsub) = [1:Nsub 1:Nsub]; cfg.ivar = 1; % the 1st row in cfg.design contains the independent variable cfg.uvar = 2; % the 2nd row in cfg.design contains the subject number A possible problem is if you've computed grand averages and haven't used cfg.keepindividual = 'yes'. If that's the case, I'd suggest keeping the individual participants' data when calling ft_timelockgrandaverage. And then using the correct design matrix. I hope this helps! Let us know if it still doesn't work. All the best, Marisha On Tue, Oct 23, 2018 at 2:35 PM Kate Stone wrote: > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP > data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using > indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't > apply as I am using grand averages and the reference for > ft_timelockstatistics doesn't mention the design matrix, although it is > definitely required. I have tried design = [1,2], but this doesn't seem to > give sensible results. > > Thanks in advance, > Kate > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Tue Oct 23 14:59:26 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Tue, 23 Oct 2018 14:59:26 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, You can't do permutation statistics if you've already consumed the degrees of freedom you want to do statistics across (i.e., subjects in this case). Did you use cfg.keepindividual = 'yes' in the call to ft_timelockgrandaverage? If so, then the design matrix should be specified exactly as in the tutorial; just as if you had been using the individual subjects' data structures. If not, then indeed you only have a grand-average left, and no permutation stats can be performed. Cheers, Eelke On Tue, 23 Oct 2018 at 14:04, Kate Stone wrote: > > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. > > Thanks in advance, > Kate > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From katemsto at gmail.com Tue Oct 23 17:21:22 2018 From: katemsto at gmail.com (Kate Stone) Date: Tue, 23 Oct 2018 17:21:22 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi again, Further to the below, what I would actually prefer to do is use the depsamplesT test on my data structure containing averages over trials for each subject (i.e. the output of ft_timelockanalysis). But every time I try, I get the message "length of the design matrix (2) doesn't equal the number of observations (694)" - no matter what the dimensions of the design matrix are. I've set up the matrix exactly as specified in the tutorial (relevant to my data of course). Any idea what this error is about? Thanks again, Kate On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > Hi all, > > I've been following the tutorial here to do a time-locked perm test on ERP > data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock > > I have computed grand averages for my two conditions and am using > indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). > > But what should the design matrix be? The example in the tutorial doesn't > apply as I am using grand averages and the reference for > ft_timelockstatistics doesn't mention the design matrix, although it is > definitely required. I have tried design = [1,2], but this doesn't seem to > give sensible results. > > Thanks in advance, > Kate > > > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Wed Oct 24 09:39:39 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Wed, 24 Oct 2018 09:39:39 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, It sounds like what you're dealing with is a single group of participants, each of whom is measured under two conditions. You are correct in stating that depsamplesT is the way to go here (as Marisha also suggested). This is, I believe, exactly the case described in this section of the "gentle" stats tutorial: http://www.fieldtriptoolbox.org/tutorial/eventrelatedstatistics#permutation_test_based_on_cluster_statistics and this section of the more "in depth" tutorial: http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock#within-subjects_experiments . See those tutorials (and Marisha's email) for more info on how to set up your design matrix in this case. Cheers, Eelke On Tue, 23 Oct 2018 at 17:21, Kate Stone wrote: > > Hi again, > > Further to the below, what I would actually prefer to do is use the depsamplesT test on my data structure containing averages over trials for each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) doesn't equal the number of observations (694)" - no matter what the dimensions of the design matrix are. I've set up the matrix exactly as specified in the tutorial (relevant to my data of course). Any idea what this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: >> >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results. >> >> Thanks in advance, >> Kate >> >> > > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From m.manahova at gmail.com Wed Oct 24 09:41:43 2018 From: m.manahova at gmail.com (Mariya Manahova) Date: Wed, 24 Oct 2018 09:41:43 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hi Kate, Clearly, there's a problem with the way you're defining your design matrix. Can you tell me what cfg.design looks like when you define it? With the code I sent you, the design matrix is a 2x58 matrix (the number of participants is 29). There are two rows, one for the independent variable (condition 1 or 2) and one for the participant number. The first row consists of 29 1's and 29 2's because I have two conditions (1 and 2), and this denotes that each participant is on the one hand in condition 1 and on the other hand in condition 2. The second row is 1:29 and then again 1:29, referring to the participant number per condition. Does this make sense? In this case, the length of my design matrix is 58 because I have 2 observations (1 for each condition) per participant. Why do you end up having 694? Can you paste the output of your cfg.design? Here's mine: [image: image.png] All the best, Marisha On Tue, Oct 23, 2018 at 6:35 PM Kate Stone wrote: > Hi again, > > Further to the below, what I would actually prefer to do is use the > depsamplesT test on my data structure containing averages over trials for > each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) > doesn't equal the number of observations (694)" - no matter what the > dimensions of the design matrix are. I've set up the matrix exactly as > specified in the tutorial (relevant to my data of course). Any idea what > this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on >> ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using >> indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't >> apply as I am using grand averages and the reference for >> ft_timelockstatistics doesn't mention the design matrix, although it is >> definitely required. I have tried design = [1,2], but this doesn't seem to >> give sensible results. >> >> Thanks in advance, >> Kate >> >> >> > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 15551 bytes Desc: not available URL: From jan.schoffelen at donders.ru.nl Wed Oct 24 10:07:26 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 24 Oct 2018 08:07:26 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis between Groups - GA, plotting and statistics References: Message-ID: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) > wrote: Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From katemsto at gmail.com Wed Oct 24 10:49:10 2018 From: katemsto at gmail.com (Kate Stone) Date: Wed, 24 Oct 2018 10:49:10 +0200 Subject: [FieldTrip] Permutation test design matrix for grand-averaged ERPs In-Reply-To: References: Message-ID: Hello, one last email just in case anyone is following my series of very silly questions or ever searches the following warnings when trying to run ft_timelockstatistics: Warning: timelock structure contains field with and without repetitions Error: the length of the design matrix does not match the number of observations in the data In my case, it was because, in the previous step where I averaged ERPs over trials using ft_timelockanalysis, I had set cfg.keeptrials = 'yes' . It should be left at the default 'no' (for a within-subjects design at least), otherwise ft_timelockstatistics will go looking for observations in the wrong spot (trial) and will discard the ones it really needs (avg, var, dof). Thanks and apologies for over-posting, Kate On Tue, Oct 23, 2018 at 5:21 PM Kate Stone wrote: > Hi again, > > Further to the below, what I would actually prefer to do is use the > depsamplesT test on my data structure containing averages over trials for > each subject (i.e. the output of ft_timelockanalysis). > > But every time I try, I get the message "length of the design matrix (2) > doesn't equal the number of observations (694)" - no matter what the > dimensions of the design matrix are. I've set up the matrix exactly as > specified in the tutorial (relevant to my data of course). Any idea what > this error is about? > > Thanks again, > Kate > > On Tue, Oct 23, 2018 at 2:04 PM Kate Stone wrote: > >> Hi all, >> >> I've been following the tutorial here to do a time-locked perm test on >> ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock >> >> I have computed grand averages for my two conditions and am using >> indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2). >> >> But what should the design matrix be? The example in the tutorial doesn't >> apply as I am using grand averages and the reference for >> ft_timelockstatistics doesn't mention the design matrix, although it is >> definitely required. I have tried design = [1,2], but this doesn't seem to >> give sensible results. >> >> Thanks in advance, >> Kate >> >> >> > > -- > Kate Stone > PhD candidate > Vasishth Lab | Department of Linguistics > Potsdam University, 14467 Potsdam, Germany > https://auskate.github.io > -- Kate Stone PhD candidate Vasishth Lab | Department of Linguistics Potsdam University, 14467 Potsdam, Germany https://auskate.github.io -------------- next part -------------- An HTML attachment was scrubbed... URL: From Joachim.Gross at glasgow.ac.uk Wed Oct 24 11:03:17 2018 From: Joachim.Gross at glasgow.ac.uk (Joachim Gross) Date: Wed, 24 Oct 2018 09:03:17 +0000 Subject: [FieldTrip] PhD student position in Muenster, Germany Message-ID: <4C93D541-3D77-4398-8160-A0FE9C3F152F@glasgow.ac.uk> The University Hospital of Münster is one of the leading hospitals in Germany. Such a position cannot be achieved by size and medical successes alone. The individual commitment counts above all. We need your commitment so that even with little things we can achieve great things for our patients. There are many possibilities open for you so that you may develop with them. The Institute for Biomagnetism and Biosignalanalysis at University of Muenster invites applications for a position as a PhD Student (gn*) Ref.: 03170 Part-Time with 65% (Germany salary grade: E 13 TV-L, 65%) (*gn=gender neutral) The position starts on 1.1.2019 and is available for 3 years in the research group of Prof. Dr. Joachim Gross. The successful candidate will coordinate, perform and publish research using MEG to study brain oscillations. We offer a stimulating environment in a successful team with high-level experience in MEG research. Successful candidates will benefit from personal mentoring, weekly seminars and general training and knowledge dissemination within the Institute for Biomagnetism and Biosignalanalysis (IBB). The position offers opportunity for further academic qualifications (PhD). The successful applicants will join a dynamic team of both experienced and junior researchers with many national and international collaborations and a sustained record of high-quality research output. We are searching for trained and highly motivated scientists ideally having experience in electrophysiology, cognitive neuroimaging or similar disciplines. The research projects require acquisition of MEG data and analysis of neuronal oscillations and their relationship to behaviour. Therefore, experience with MEG/EEG, programming and spectral analysis is highly desirable. Requirements: * Degree in psychology, medicine, physics, or a related discipline relevant for neuroscience * Experience with cognitive neuroscience research ideally with MEG or EEG * Experience with running cognitive neuroscience studies * Programming skills (matlab, python or similar) * Knowledge of general statistics for data analysis * Very good English language skills * Strong commitment, flexibility, independence and team work For more information please contact Prof. Dr. Joachim Gross, University of Muenster, Institute for Biomagnetism and Biosignalanalysis, 48149 Münster, Germany, Email: Joachim.Gross(at)­wwu(dot)­de Please send your application (including the above reference number) with all relevant information (CV, cover letter) until 11.11.2018 to Personalgewinnung des Universitätsklinikums Münster, Bewerbermanagement, Domagkstraße 5, 48149 Münster or via e-mail (PDF-file, max. 5 MB) to Bewerbung(at)­ukmuenster(dot)­de. Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference. -------------- next part -------------- An HTML attachment was scrubbed... URL: From Joachim.Gross at glasgow.ac.uk Wed Oct 24 11:03:56 2018 From: Joachim.Gross at glasgow.ac.uk (Joachim Gross) Date: Wed, 24 Oct 2018 09:03:56 +0000 Subject: [FieldTrip] PostDoc Position in Muenster, Germany Message-ID: <0EC39993-381F-49E2-B52F-FE1CD36F4058@glasgow.ac.uk> The University Hospital of Münster is one of the leading hospitals in Germany. Such a position cannot be achieved by size and medical successes alone. The individual commitment counts above all. We need your commitment so that even with little things we can achieve great things for our patients. There are many possibilities open for you so that you may develop with them. The Institute for Biomagnetism and Biosignalanalysis at University of Muenster invites applications for a Research Associate/Postdoctoral Scientist (gn*) Ref.: 03171 Full-Time with 38,5 (hours/week) (Germany salary grade: E 13 TV-L, 100%) (*gn=gender neutral) The position starts on 1.1.2019 and is available for 3 years in the research group of Prof. Dr. Joachim Gross. The successful candidate will coordinate, perform and publish research using MEG to study brain oscillations. We offer a stimulating environment in a successful team with high-level experience in MEG research. Successful candidates will benefit from personal mentoring, weekly seminars and general training and knowledge dissemination within the Institute for Biomagnetism and Biosignalanalysis (IBB). The position offers opportunity for further academic qualifications (Habilitation). The successful applicants will join a dynamic team of both experienced and junior researchers with many national and international collaborations and a sustained record of high-quality research output. We are searching for trained and highly motivated scientists ideally having experience in electrophysiology, cognitive neuroimaging or similar disciplines. The research projects require acquisition of MEG data and analysis of neuronal oscillations and their relationship to behaviour. Therefore, experience with MEG/EEG, programming and spectral analysis is highly desirable. Requirements: * PhD in psychology, medicine, physics, or a related discipline relevant for neuroscience * Experience with cognitive neuroscience research ideally with MEG or EEG * Publications in peer-reviewed journals * Experience with running cognitive neuroscience studies * Programming skills (matlab, python or similar) * Knowledge of general statistics for data analysis * Very good English language skills * Strong commitment, flexibility, independence and team work For more information please contact Prof. Dr. Joachim Gross, University of Muenster, Institute for Biomagnetism and Biosignalanalysis, 48149 Münster, Germany, Email: Joachim.Gross(at)­wwu(dot)­de Please send your application (including the above reference number) with all relevant information (CV, cover letter) until 11.11.2018 to Personalgewinnung des Universitätsklinikums Münster, Bewerbermanagement, Domagkstraße 5, 48149 Münster or via e-mail (PDF-file, max. 5 MB) to Bewerbung(at)­ukmuenster(dot)­de. Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference. -------------- next part -------------- An HTML attachment was scrubbed... URL: From agathalenartowicz at gmail.com Wed Oct 24 20:37:00 2018 From: agathalenartowicz at gmail.com (Agatha Lenartowicz) Date: Wed, 24 Oct 2018 11:37:00 -0700 Subject: [FieldTrip] postdoc opportunity - alpha oscillations/concurrent EEG-fMRI Message-ID: We’d like to share a *post-doctoral opening* at the Semel Institute for Neuroscience and Behavior at University of California, Los Angeles (Loo/Lenartowicz labs). The position is part of a funded 5-year project to map, using *concurrent EEG and fMRI,* the *brain circuitry of alpha-range oscillations and their impairments in ADHD.* The position requires familiarity with MRI and/or EEG data, knowledge of UNIX, Matlab (and/or Python), and is ideally suited for candidates with a strong background in neuroimaging, signal processing & electrical/biomedical engineering, physics or some combination. Excellent communication skills, initiative, ability to anticipate changes and develop solutions, and attention to detail are imperative. The position offers a vibrant working environment, as well as ample opportunity to participate in collaborative and independent research. For inquiries please contact Agatha Lenartowicz ( alenarto at g.ucla.edu). -------------- next part -------------- An HTML attachment was scrubbed... URL: From cornelia.quaedflieg at uni-hamburg.de Wed Oct 24 23:42:54 2018 From: cornelia.quaedflieg at uni-hamburg.de (Conny Quaedflieg) Date: Wed, 24 Oct 2018 23:42:54 +0200 Subject: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics In-Reply-To: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> References: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> Message-ID: <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> Dear Jan-Mathijs, Thank you for your quick reply. @1R Indeed ft_connectivityanalysis on individual dipole positions (PCC). Cfg.refindx’ seems not to do anything, are there other options to run a seed-based connectivity analysis? @R2 We indeed performed source-reconstruction for each subject on a subject-specific grid, that maps onto a template grid in spatially normalized space. I constructed a GA of the individual data and would like to plot these on a standard cortical sheet / brain surface. Best Conny Van: Schoffelen, J.M. (Jan Mathijs) Verzonden: woensdag 24 oktober 2018 10:16 Aan: FieldTrip discussion list Onderwerp: Re: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to  http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) wrote:   Dear Fieldtripers,   I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx.    Though, the analysis looks exactly the same with and without the refindx specified.   I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long.   Best   Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164   _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Oct 25 08:49:44 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 25 Oct 2018 06:49:44 +0000 Subject: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics In-Reply-To: <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> References: <7D46190D-63A1-4C8C-9FD6-29E97010DFDF@donders.ru.nl> <20181024214254.84EE1B0EEF@mailhost.uni-hamburg.de> Message-ID: <0FD19B0D-E3D7-4A46-99B7-19840CC3B488@donders.ru.nl> Hi Conny, Again, could you please spill more details? -what is the connectivity metric you want to compute? -what do you mean with ‘cfg.refindx seems not to do anything’? -what have you done in terms of diagnostics yourself? have you looked into the code with breakpoints etc.? Jan-Mathijs On 24 Oct 2018, at 23:42, Conny Quaedflieg > wrote: Dear Jan-Mathijs, Thank you for your quick reply. @1R Indeed ft_connectivityanalysis on individual dipole positions (PCC). Cfg.refindx’ seems not to do anything, are there other options to run a seed-based connectivity analysis? @R2 We indeed performed source-reconstruction for each subject on a subject-specific grid, that maps onto a template grid in spatially normalized space. I constructed a GA of the individual data and would like to plot these on a standard cortical sheet / brain surface. Best Conny Van: Schoffelen, J.M. (Jan Mathijs) Verzonden: woensdag 24 oktober 2018 10:16 Aan: FieldTrip discussion list Onderwerp: Re: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics Hi Conny, You are a bit short on the details, so it is hard to give to-the-point feedback. @1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions? @2 this works best if your individual subject source models can be easily compared, e.g. according to http://www.fieldtriptoolbox.org/tutorial/sourcemodel#performing_group_analysis_on_3-dimensional_source-reconstructed_data This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory. Best wishes, Jan-Mathijs On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) > wrote: Dear Fieldtripers, I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas). Based on the help function this should be possible using cfg.refindx. Though, the analysis looks exactly the same with and without the refindx specified. I would be really grateful with help on the following 1. Ideas how to run a seed based source analysis 2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it? I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long. Best Conny Quaedflieg, PhD Assistant Professor Department of Clinical Psychological Science Faculty of Psychology and Neuroscience UNS 40, Room A3731a 043-883164 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From gerhard.jocham at uni-duesseldorf.de Thu Oct 25 10:23:42 2018 From: gerhard.jocham at uni-duesseldorf.de (Gerhard Jocham) Date: Thu, 25 Oct 2018 10:23:42 +0200 Subject: [FieldTrip] =?utf-8?q?Three_Postdoc_and_one_PhD_student_position?= =?utf-8?q?=2C_Heinrich_Heine_University_D=C3=BCsseldorf?= Message-ID: <9F20E20B-DFEB-4458-AA99-D8509F0556C0@uni-duesseldorf.de> Two postdoctoral positions (up to five years) and one PhD student position (four years) on an ERC-funded project, and one postdoctoral position (three years) on a DFG-funded project are available at the Heinrich Heine University Düsseldorf. Please check the links for detailed information and for how to apply: http://www.cns-jocham.de/01_advert_postdocs_erc.pdf http://www.cns-jocham.de/03_advert_postdoc_dfg_heise.pdf http://www.cns-jocham.de/02_advert_phd_erc.pdf The projects focus on how decision variables are represented in cortical dynamics (recorded with MEG), and how these representations are shaped by neuromodulatory systems. We are seeking candidates with a strong interest in decision making. Applicants for the postdoc positions should have a PhD in psychology, neuroscience, or related field. Demonstrable experience with either MEG or EEG and good programming skills (e.g. Matlab, Python) are essential. Applicants for the PhD position should have an MSc (or equivalent degree) in psychology, neuroscience, or related field, and sound knowledge of statistics. The ideal candidate should also possess programming skills (e.g., Matlab, Python) and have prior experience with MEG/EEG analysis. Kind regards Gerhard Jocham ================================= Prof. Dr. Gerhard Jocham Biological Psychology of Decision Making Institute of Experimental Psychology Heinrich Heine University Düsseldorf Universitätsstraße 1 40225 Düsseldorf, Germany +49 (0) 211 81 12468 -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Thu Oct 25 12:45:03 2018 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Thu, 25 Oct 2018 12:45:03 +0200 Subject: [FieldTrip] Quick eloreta question In-Reply-To: References: Message-ID: <499624E4-5D51-4042-B1A9-883FCE337164@donders.ru.nl> Hi Arno, Let me CC this to the mailing list, as I think that it might be of iterest to others. You should preferably be calling eloreta through ft_sourceanalysis. There you can see on line 1016 how it is called for ERP/ERF data with the data covariance (and elsewhere with the cross-spectrum). The phase information is certainly not ignored: an obvious one that you need is the exact out-of-phase (i.e. 180 degree rotation) of the positive and negative channels that see the same dipole. So taking the “abs” is not appropriate. But there is sometimes a reason to take “real” and ignore “imag”: For beamforming - where we scan with a single dipole - we do know that the dipole can only be in phase or exactly out-of-phase, which means that the complex part of the CSD cannot be attibuted to the source of interest. That is implemented with the ‘realfilter’ option in beamformer_dics (default=no, consistent with the publication) and beamformer_pcc (default=yes, because it works slightly better). Since we are not doing a scalar beamformer in most cases, but a vector beamformer (which can rotate in 2 or 3 dimensions), it is not obvious under which conditions realfilter works the best. A strongly rotating source has out-of-phase CSD coponents between the different dipole orientations, so in that case realfilter=no should be better able to reconstruct it. But in our data we (anecdotically) tend to get slightly better SNR at the source level for realfilter=yes. For frequency domain eloreta you are estimating many source simultaneously, where the sources can have any phase relation to each other, so there is a priori no point in only taking the real part of the CSD. Hence it is also not implemented. However, if you were to hypothesize that the underlying source is very one-dimensional (as with an ICA) and not rotating, the same reasoning would apply as for the beamformer and you would expect slightly better performance ignoring the imaginary part of the CSD in the inversion. best Robert PS the getting_started page on “loreta” refers to the specific Loreta software, not the inverse methods. Furthermore, it is >10 years old and probably outdated. > On 24 Oct 2018, at 19:49, Arnaud Delorme wrote: > > HI Robert, > > Another quick follow up, still on cross-spectrum and Loreta. > > When I use ft_freqanalysis, I get a complex crossspectrum (dataset with 80 trials). > However, my understanding is that you only use the absolute value of the crossspectrum for source localization (phase information is ignored), and that the crossspectrum should be computed on a single trial basis then averaged the absolute value should be averaged accross trials. It is unclear to me how you can obtain a complex estimate on multiple trials (are you averaging the complex cross-spectrum values across trials - I have done some more test and it seems that this is what you are doing). Attached is a cross-spectrum calculated using this method (custom code, left) versus the absolute value of the cross-spectrum returned by ft_freqanalysis (right) on the same data. > > When I do take the absolute value before doing the average of the cross-spectrum, the eLoreta solution is also more focal. > > Cheers, > > Arno > > > >> On Oct 24, 2018, at 9:41 AM, Arnaud Delorme > wrote: >> >> Hi Robert, >> >> I have a quick eLoreta question. In Fieldtrip ft_sourcelocalize, it seems that eLoreta requires the cross-spectrum. I have tried without (or used NaN) but the function is not functioning properly in that case. This means that eLoreta cannot be applied to ERPs, is that correct? What about ICA component scalp topographies (in that case I can weight the cross-spectrum using the channel inverse weight matrix - the cross-spectrum matrix would be proportional to the product of the column in the inverse weight matrix corresponding to the component by its transpose). For spectral decomposition, assuming the spectrum is in the diagonal of the cross-spectrum, is the spectrum field even used at all (I was not able to find information about that). >> >> Aso my intuition is that performing statistics (as outlined on this page http://www.fieldtriptoolbox.org/getting_started/loreta ) at the voxel level does not make sense if the statistics at the electrode level is not significant. >> >> Thank you, >> >> Arno > -------------- next part -------------- An HTML attachment was scrubbed... URL: From rdm146 at newark.rutgers.edu Thu Oct 25 23:05:46 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Thu, 25 Oct 2018 21:05:46 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Message-ID: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi -------------- next part -------------- An HTML attachment was scrubbed... URL: From manuela.costa at ctb.upm.es Fri Oct 26 18:50:41 2018 From: manuela.costa at ctb.upm.es (Manuela Costa) Date: Fri, 26 Oct 2018 18:50:41 +0200 Subject: [FieldTrip] Across regions PAC Message-ID: Dear community, I performed phase low amplitude high coupling within a region (A) following fieldtrip tutorial. http://www.fieldtriptoolbox.org/example/crossfreq/phalow_amphigh Now I would like to see wether low frequency in my region (A) modulate the amplitude of high frequency in region (B). Which is the correct way to perform this analysis? Best regards, Manuela -------------- next part -------------- An HTML attachment was scrubbed... URL: From gopalar.ccf at gmail.com Fri Oct 26 20:02:16 2018 From: gopalar.ccf at gmail.com (Raghavan Gopalakrishnan) Date: Fri, 26 Oct 2018 14:02:16 -0400 Subject: [FieldTrip] Across regions PAC Message-ID: <049501d46d56$08895520$199bff60$@gmail.com> Hi Manuela I suggest you use Brainstorm to perform Phase-amplitude coupling analysis (PAC). Brainstorm has advanced PAC algorithms. Now you can import fieldtrip structure to Brainstorm easily. https://neuroimage.usc.edu/brainstorm/Tutorials/TutPac https://neuroimage.usc.edu/brainstorm/Tutorials/Resting Thanks, Raghavan Dear community, I performed phase low amplitude high coupling within a region (A) following fieldtrip tutorial. http://www.fieldtriptoolbox.org/example/crossfreq/phalow_amphigh Now I would like to see wether low frequency in my region (A) modulate the amplitude of high frequency in region (B). Which is the correct way to perform this analysis? Best regards, Manuela -------------- next part -------------- An HTML attachment was scrubbed... URL: From carsten.wolters at uni-muenster.de Mon Oct 29 12:31:25 2018 From: carsten.wolters at uni-muenster.de (Carsten Wolters) Date: Mon, 29 Oct 2018 12:31:25 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: Message-ID: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR    Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: > > Dear Fieldtrippers > > > I have applied the FEM simbio head modeling pipeline implemented > in Fieldtrip to my EEG data. My understanding is that this pipeline > assumes isotropic conductivities for 5 head compartments (as specified > by cfg.conductivity in ft_prepare_headmodel). After reading some > papers (e.g. Vorwerk et al 2014 > https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like > incorporating white matter conductivity anisotropy has a relatively > small albeit significant effect on the source solution. I am > interested in comparing FEM results when treating white matter as > anisotropic. My questions are as follows: > > > 1. Is there a way to implement the FEM simbio head model whilst > treating WM as anisotropic within Fieldtrip? If so, how would one > do this (or are there any resources available that demonstrate this)? > 2. From previous papers and some simbio documentation > (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) > it seems like diffusion MRI data is required to calculate the WM > conductivity for each individual subject. I only have T1 and T2 > scans for my subjects. So would it be possible to use WM > anisotropic information obtained from some kind of diffusion > MRI group average/atlas instead (accepting some loss in > subject-level precision)? If so, does such a group average/atlas > exist? > > > Any help would be greatly appreciated! > > > Thanks > > Ravi > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From maria.hakonen at gmail.com Mon Oct 29 13:33:15 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Mon, 29 Oct 2018 14:33:15 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? Message-ID: Dear FieldTrip experts, I have run ft_mergealign across subjects to align the head positions. However, the residual variance between the original and the realigned data seems to be high: original -> template RV 21232.46 % original -> original RV 36.96 % original -> template -> original RV 9579.95 % Could someone please let me know what would be the largest acceptable change in the residual variance, and what should I do if the residual variance is too high? Does the increase in residual variance mean that there is a large shift in the head position? I have used ft_mergealign as follows: template = list of subjects (i.e. I want to calculate an average head position over the subjects) grad = data.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); vol = ft_headmodel_localspheres(hs,grad); cfg = []; cfg.template = template; cfg.inwardshift = 1.0; cfg.vol = vol; data_aligned = ft_megrealign(cfg, data); Best, Maria -------------- next part -------------- An HTML attachment was scrubbed... URL: From j.vorw01 at gmail.com Mon Oct 29 15:14:16 2018 From: j.vorw01 at gmail.com (Johannes Vorwerk) Date: Mon, 29 Oct 2018 15:14:16 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: Message-ID: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes > Am 29.10.2018 um 12:31 schrieb Carsten Wolters : > > Dear Ravi, > > 1) You can use the pure SimBio-code from > https://www.mrt.uni-jena.de/simbio/index.php/Main_Page > to treat WM anisotropy. > While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, > this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented > Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: > "Depending on your matlab skills and your available time, I could help you to give it a > try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." > > 2) We recommend > http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf > on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main > bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details > in the cortices are individual. We always measure T1, T2 and DTI from each subject > and I personally do not have experience with such a group-level anisotropy compared > to the individual one. Might be interesting to hear from others what they think!? > > BR > Carsten > > > > Am 25.10.18 um 23:05 schrieb Ravi Mill: >> Dear Fieldtrippers >> >> I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040 ), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: >> >> Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? >> From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples ) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? >> >> Any help would be greatly appreciated! >> >> Thanks >> Ravi >> >> >> >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From victor.rg.gib at gmail.com Tue Oct 30 10:43:08 2018 From: victor.rg.gib at gmail.com (Victor RG) Date: Tue, 30 Oct 2018 10:43:08 +0100 Subject: [FieldTrip] Problem using ft_sensrorealign with Yokogawa MEG Message-ID: HI Fieldtrip experts! I'm trying to align a headmodel (the one from example "Subject1") with my MEG sensors (Yokogawa system). I'm trying to do that interactively, in order to construct a Leadfied matrix as accurate as possible. To do that, I am testing this code: *cfg = [];* *cfg.method = 'interactive';* *cfg.headshape = vol.bnd(1);* *cfg.senstype = 'meggrad';* *grad_aligned = ft_sensorrealign(cfg, grad); % Im using ft_sensorrealign cause I think is the MEG version of ft_electroderealign* The variables employed consist of: *>> grad = * struct with fields: - balance: [1×1 struct] - chanori: [160×3 double] - chanpos: [160×3 double] - chantype: {160×1 cell} - chanunit: {160×1 cell} - coilori: [320×3 double] - coilpos: [320×3 double] - label: {160×1 cell} - tra: [160×320 double] - type: 'yokogawa160' - unit: 'cm' - fid: [1×1 struct] *>> vol.bnd(1) =* struct with fields: - pos: [1000×3 double] - tri: [1996×3 double] - coordsys: 'ctf' I have looked at the tutorial for EEG sensors realignment, and I have copied the procedure, since there is not a specific tutorial for MEG sensors. I thought it would be the same, but when executing it I obtain the following errors: *>> Undefined function 'fixpos' for input arguments of type 'struct'.* *Error in ft_sensorrealign (line 255)* *headshape = fixpos(cfg.headshape);* *Error in generating_leadfield (line 63)* *grad_aligned = ft_sensorrealign(cfg, grad);* Does anybody know how to do that, or how to do an interactive realignment with MEG sensors? Thanks in advance. Víctor. Víctor Rodríguez González Grupo de Ingeniería Biomédica, ETSIT. Universidad de Valladolid, España. -------------- next part -------------- An HTML attachment was scrubbed... URL: From hesham.elshafei at inserm.fr Tue Oct 30 17:05:08 2018 From: hesham.elshafei at inserm.fr (Hesham ElShafei) Date: Tue, 30 Oct 2018 17:05:08 +0100 Subject: [FieldTrip] Phase Information For PCC Beamformer Message-ID: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> Hello Fieldtrippers! So I am trying to do some whole brain connectivity analysis according to this tutorial: http://www.fieldtriptoolbox.org/tutorial/networkanalysis All is going fine :) I just would like to understand how the phase information is obtained using these options in ft_freqanalysis cfg.method = 'mtmfft'; cfg.output = 'fourier'; In other words , how is time incorporated in the computed phase values? In other other words, these phase values represent the signal at which time point? hope I was clear enough Cheers! Hesham From ignasi.sols at nyu.edu Tue Oct 30 19:38:13 2018 From: ignasi.sols at nyu.edu (Ignasi Sols) Date: Tue, 30 Oct 2018 14:38:13 -0400 Subject: [FieldTrip] Brain-shift compensation Error Message-ID: Dear all, I'm following the method developed by Stolk et al (2018) to localize the electrodes of ECoG data. I'm getting this error on step 23 (Project the electrode grids to the surface hull of the implanted hemisphere) and I can't solve it. Could anyone help me with this? Thanks, Ignasi *using electrodes specified in the configuration* *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: 22155045* *creating electrode pairs based on electrode positions* *Error using fmincon (line 241)* *You must provide a non-empty starting* *point.* *Error in warp_dykstra2012 (line 156)* *coord_snapped = fmincon(efun, coord0,* *[], [], [], [], [], [], cfun,* *options);* *Error in ft_electroderealign (line* *406)* * norm.elecpos =* warp_dykstra2012(cfg, elec, headshape); -- Ignasi Sols Postdoctoral Fellow Department of Psychology New York University -------------- next part -------------- An HTML attachment was scrubbed... URL: From a.stolk8 at gmail.com Wed Oct 31 07:23:37 2018 From: a.stolk8 at gmail.com (Arjen Stolk) Date: Tue, 30 Oct 2018 23:23:37 -0700 Subject: [FieldTrip] Brain-shift compensation Error In-Reply-To: References: Message-ID: Hi Ignasi, One can only guess based on that error msg alone. You might want to put a debug marker at line 156, check whether coord0 is truly empty, and then try to trace back to what's causing it to be empty (e.g., an empty elecpos field in your elec structure?). Arjen On Tue, Oct 30, 2018 at 12:04 PM Ignasi Sols wrote: > Dear all, > I'm following the method developed by Stolk et al (2018) to localize the > electrodes of ECoG data. > I'm getting this error on step 23 (Project the electrode grids to the > surface hull of the implanted hemisphere) and I can't solve it. Could > anyone help me with this? > > Thanks, > Ignasi > > > *using electrodes specified in the configuration* > *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: > 22155045* > *creating electrode pairs based on electrode positions* > *Error using fmincon (line 241)* > *You must provide a non-empty starting* > *point.* > *Error in warp_dykstra2012 (line 156)* > *coord_snapped = fmincon(efun, coord0,* > *[], [], [], [], [], [], cfun,* > *options);* > *Error in ft_electroderealign (line* > *406)* > * norm.elecpos =* > warp_dykstra2012(cfg, elec, > headshape); > > -- > Ignasi Sols > Postdoctoral Fellow > Department of Psychology > New York University > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From assaf.harel at wright.edu Wed Oct 31 17:43:59 2018 From: assaf.harel at wright.edu (Harel, Assaf) Date: Wed, 31 Oct 2018 16:43:59 +0000 Subject: [FieldTrip] 20th International Symposium on Aviation Psychology Call for Proposals : New Proposal Submission Deadline Message-ID: <95AD2984-3DA8-4F45-9664-8BE71009B580@wright.edu> 20th International Symposium on Aviation Psychology Call for Proposals The 20th ISAP will be held in Dayton, Ohio, U.S.A., May 7-10, 2019 (Tuesday – Friday). Proposal Submission is Live! New Proposal Submission Deadline: November 9, 2018 Proposals are sought for posters, papers, symposium and panel sessions, and workshops. Any topic related to the field of aviation psychology is welcomed. Topics on human performance problems and opportunities within aviation systems, and design solutions that best utilize human capabilities for creating safe and efficient aviation systems are all appropriate. Any basic or applied research domain that generalizes from or to the aviation domain will be considered. Students are especially encouraged to participate in the The Stanley Nelson Roscoe Best Student Paper Competition. Visit http://aviation-psychology.org for more information. Pamela Tsang and Michael Vidulich (Symposium Co-Chairs) Contact isap2019 at isap.wright.edu for any questions. -------------- next part -------------- An HTML attachment was scrubbed... URL: