[FieldTrip] fieldtrip Digest, Vol 124, Issue 7
Andrade Rey René
rene.andrade at edu.uah.es
Sat Mar 6 13:09:08 CET 2021
Thanks for all the help to all the team. But I have a further question.
Error using ft_sourceparcellate (line 109)
the source positions are not consistent with the parcellation, please use FT_SOURCEINTERPOLATE
Is this related to my data versus tutorial?
> On 6 Mar 2021, at 12:00, fieldtrip-request at science.ru.nl wrote:
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> Today's Topics:
>
> 1. Re: Loading atlas_MMP1.0_4k.mat error not found &&
> ft_sourceanalysis SVD NaN Inf (Andrade Rey René)
> 2. Re: Loading atlas_MMP1.0_4k.mat error not found &&
> ft_sourceanalysis SVD NaN Inf (Tzvetan Popov)
> 3. Plot classifier weights' topography (Barnett, Benjy)
> 4. Re: Plot classifier weights' topography
> (Schoffelen, J.M. (Jan Mathijs))
> 5. Maskparameter in ft_singleplotER
> (Christina Vanden Bosch der Nederlanden)
> 6. Re: source-level cluster analysis? (Ladanyi, Eniko)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 5 Mar 2021 11:13:03 +0000
> From: Andrade Rey René <rene.andrade at edu.uah.es>
> To: "fieldtrip at science.ru.nl" <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] Loading atlas_MMP1.0_4k.mat error not found
> && ft_sourceanalysis SVD NaN Inf
> Message-ID: <8CA3CA44-D43F-471A-B43C-5407AAC71FC8 at edu.uah.es>
> Content-Type: text/plain; charset="utf-8"
>
> I have seen fro EEG I should use dkatlas. Although that would actually give me the same error because I don’t know where it is located.
>
> On 5 Mar 2021, at 11:21, Andrade Rey René <rene.andrade at edu.uah.es<mailto:rene.andrade at edu.uah.es>> wrote:
>
> Dear community:
> I m trying to run the tutorial of connectivity EEG analysis modified from MEG. And I receive this error message.
>
>
>
> Error using load
> Unable to read file 'atlas_MMP1.0_4k.mat'. No such file or directory.
>
> Error in fieldtrip_coherence (line 231)
> load atlas_MMP1.0_4k.mat;
>
>
> I could finally reduce the source model to 3d8 because 3d10 gives an error of NaN or Inf.
>
> Error using svd
> Input to SVD must not contain NaN or Inf.
>
> Error in ft_inverse_pcc>pinv (line 352)
> [U,S,V] = svd(A,0);
>
> Error in ft_inverse_pcc (line 253)
> filt = pinv(lfa' * invCmeg * lfa) * lfa' * invCmeg;
>
> Error in ft_sourceanalysis (line 775)
> dip(i) = ft_inverse_pcc(sourcemodel, sens, headmodel, avg, squeeze_Cf,
> methodopt{:}, leadfieldopt{:}, 'refdip', cfg.refdip, 'refchan', refchanindx,
> 'supdip', cfg.supdip, 'supchan', supchanindx);
>
> Error in fieldtrip_coherence (line 108)
> source = ft_sourceanalysis(cfg, freq);
>
>
> Best,
> Rene Andrade.
>
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> ------------------------------
>
> Message: 2
> Date: Fri, 5 Mar 2021 12:51:39 +0100
> From: Tzvetan Popov <tzvetan.popov at uni-konstanz.de>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] Loading atlas_MMP1.0_4k.mat error not found
> && ft_sourceanalysis SVD NaN Inf
> Message-ID: <D6864DD1-CECA-4A20-878B-555A4AE58963 at uni-konstanz.de>
> Content-Type: text/plain; charset="utf-8"
>
> Dear Rene,
>
> The objects you are looking for are located here: ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/networkanalysis_eeg/ <ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/networkanalysis_eeg/>
> Good luck
> tzvetan
>
>
>> On 5 Mar 2021, at 12:13, Andrade Rey René <rene.andrade at edu.uah.es> wrote:
>>
>> I have seen fro EEG I should use dkatlas. Although that would actually give me the same error because I don’t know where it is located.
>>
>>> On 5 Mar 2021, at 11:21, Andrade Rey René <rene.andrade at edu.uah.es <mailto:rene.andrade at edu.uah.es>> wrote:
>>>
>>> Dear community:
>>> I m trying to run the tutorial of connectivity EEG analysis modified from MEG. And I receive this error message.
>>>
>>>
>>>
>>> Error using load
>>> Unable to read file 'atlas_MMP1.0_4k.mat'. No such file or directory.
>>>
>>> Error in fieldtrip_coherence (line 231)
>>> load atlas_MMP1.0_4k.mat;
>>>
>>>
>>> I could finally reduce the source model to 3d8 because 3d10 gives an error of NaN or Inf.
>>>
>>> Error using svd
>>> Input to SVD must not contain NaN or Inf.
>>>
>>> Error in ft_inverse_pcc>pinv (line 352)
>>> [U,S,V] = svd(A,0);
>>>
>>> Error in ft_inverse_pcc (line 253)
>>> filt = pinv(lfa' * invCmeg * lfa) * lfa' * invCmeg;
>>>
>>> Error in ft_sourceanalysis (line 775)
>>> dip(i) = ft_inverse_pcc(sourcemodel, sens, headmodel, avg, squeeze_Cf,
>>> methodopt{:}, leadfieldopt{:}, 'refdip', cfg.refdip, 'refchan', refchanindx,
>>> 'supdip', cfg.supdip, 'supchan', supchanindx);
>>>
>>> Error in fieldtrip_coherence (line 108)
>>> source = ft_sourceanalysis(cfg, freq);
>>>
>>>
>>> Best,
>>> Rene Andrade.
>>
>> _______________________________________________
>> fieldtrip mailing list
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> https://doi.org/10.1371/journal.pcbi.1002202
>
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> ------------------------------
>
> Message: 3
> Date: Fri, 5 Mar 2021 14:18:30 +0000
> From: "Barnett, Benjy" <benjy.barnett.20 at ucl.ac.uk>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: [FieldTrip] Plot classifier weights' topography
> Message-ID: <A00D241C-A96D-4916-BCE7-8FEF829D13DE at ucl.ac.uk>
> Content-Type: text/plain; charset="utf-8"
>
> Hi!
>
> I am trying to plot the topography of my LDA weights, but am having some trouble. I am using my own script to calculate the weights rather than any packages (e.g. DMLT, or MVPA-light), so the tutorials on the fieldtrip site haven’t been able to help me. So far, I’ve simply tried adding the weights (a 306x1 length vector) as a field to my data structure. And trying this code:
>
>
> data.weights = weights;
> cfg = [];
> cfg.xlim ='maxmin';
> cfg.zlim = 'maxmin';
> cfg.layout = 'neuromag306all.lay';
> cfg.channel = 'MEG';
> cfg.parameter = 'weights';
> figure; ft_topoplotER(cfg,data); colorbar
>
>
>
> Unsurprisingly, this doesn’t work. It results in the error 'field “weights" not present in data’. I assume because the weights field is not interpretable by the fieldtrip code. I guessed the lack of a dimord field for the weights might be underlying its lack of interpretability, but playing around with that has not worked either. I’ve tried adding a ‘weightsdimord’ field with the [306x1] dimensions, but this also - unsurprisingly - didn’t work.
>
> Is there a way around this? Will I need to use lower level functions than ft_topoplotER? If so, can anyone advise on how I might implement this?
>
> Many thanks for your time,
> Benjy
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> ------------------------------
>
> Message: 4
> Date: Fri, 5 Mar 2021 15:02:41 +0000
> From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] Plot classifier weights' topography
> Message-ID: <B1236372-0CA3-4940-A640-F47984E17E83 at donders.ru.nl>
> Content-Type: text/plain; charset="utf-8"
>
> Dear Benjy,
>
> It looks as if you are almost there.
>
> That is, I don’t know what your data structure looks like, but if you create it sufficiently lean, and internally consistent, it should work.
> Something like:
>
> data = [];
> data.weights = weights;
> data.label = <list-of-labels>;
> data.time = 0; % just give it a value
> data.dimord = ‘chan_time’;
>
> Alternatively, you can use the lower-level function ft_plot_topo (it’s in fieldtrip/plotting): this function requires also ‘low-level’ input arguments, i.e. two vectors of x and y coordinates of the sensor positions, the vector with the to-be-plotted values, and some additional options for aesthetics. I think the help documentation of this function explains it well enough.
>
> Two points of caution:
> 1) with the 306 mixed gradiometer/magnetometer system it probably does not make sense to visualize a topogrophy of the gradiometers and magnetometers mixed, and also, using the gradiometers only it probably will probably not result in a nice topography.
> 2) it could be that it is more informative to look at the ‘forward mixing weights’, rather than the unmixing weights that drop out of your classifier. An exceptionally good read about this issue is provided in: https://doi.org/10.1016/j.neuroimage.2013.10.067
>
> Best wishes,
> Jan-Mathijs
>
>
> On 5 Mar 2021, at 15:18, Barnett, Benjy <benjy.barnett.20 at ucl.ac.uk<mailto:benjy.barnett.20 at ucl.ac.uk>> wrote:
>
> Hi!
>
> I am trying to plot the topography of my LDA weights, but am having some trouble. I am using my own script to calculate the weights rather than any packages (e.g. DMLT, or MVPA-light), so the tutorials on the fieldtrip site haven’t been able to help me. So far, I’ve simply tried adding the weights (a 306x1 length vector) as a field to my data structure. And trying this code:
>
>
> data.weights = weights;
> cfg = [];
> cfg.xlim ='maxmin';
> cfg.zlim = 'maxmin';
> cfg.layout = 'neuromag306all.lay';
> cfg.channel = 'MEG';
> cfg.parameter = 'weights';
> figure; ft_topoplotER(cfg,data); colorbar
>
> Unsurprisingly, this doesn’t work. It results in the error 'field “weights" not present in data’. I assume because the weights field is not interpretable by the fieldtrip code. I guessed the lack of a dimord field for the weights might be underlying its lack of interpretability, but playing around with that has not worked either. I’ve tried adding a ‘weightsdimord’ field with the [306x1] dimensions, but this also - unsurprisingly - didn’t work.
>
> Is there a way around this? Will I need to use lower level functions than ft_topoplotER? If so, can anyone advise on how I might implement this?
>
> Many thanks for your time,
> Benjy
> _______________________________________________
> fieldtrip mailing list
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> https://doi.org/10.1371/journal.pcbi.1002202
>
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> ------------------------------
>
> Message: 5
> Date: Fri, 5 Mar 2021 12:59:38 -0500
> From: Christina Vanden Bosch der Nederlanden <christinavb at gmail.com>
> To: fieldtrip at science.ru.nl
> Subject: [FieldTrip] Maskparameter in ft_singleplotER
> Message-ID:
> <CAG8arbYTSoEsUrSKLb1g48paDRTNXH=MEZsr+FdOD7qWbgXaQw at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hello!
>
> I have been plotting my coherence data using Matlab's built-in plot
> functions, but I'd like to take advantage of fieldtrip's
> cfg.maskparameters for graphing significant regions of coherence in my
> data. I want to plot the average coherence across subjects and then make
> the thickness of the line represent significant regions. I'm trying to
> "trick" fieldtrip into thinking that my datatype is freq, but it won't let
> me plot just the average (and I read that maskparameter is omitted when
> trying to average over repetitions). So how do I trick fieldtrip into
> plotting my data as an average across participants, with std error bars AND
> the mask parameter for sig regions of coherence (x-axis: freq, y-axis:
> coherence).
>
> data =
> struct with fields:
>
> powspctrm: [33×790 double] %this is actually participants_freq
> avg: [1×790 double] %avg across all participants for all freqs
>
> stderror: [1×790 double] %stderror across all participants for all freqs
> dimord: 'chan_freq'
> freq: [1×790 double]
> sigreg: [1×790 logical] %0's for nonsig 1's for sig
> label: 'E25' %just put in a random chan label to trick ft into
> thinking this was a 'chan_freq' datatype resulting from ft_freqanalysis
>
>
> cfg = [ ];
> cfg.parameter = 'avg';
> cfg.maskparameter = 'sigreg';
> cfg.maskstyle = 'thickness'
> cfg.title = 'All conditions, sig coherence';
> ft_singleplotER(cfg, data)
>
> --
> Christina M. Vanden Bosch der Nederlanden
> Postdoctoral Fellow
> Brain and Mind Institute
> Western University
> christinavb at gmail.com
> Website: https://sites.google.com/view/christinavbdn
> ᐧ
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> ------------------------------
>
> Message: 6
> Date: Sat, 6 Mar 2021 02:59:57 +0000
> From: "Ladanyi, Eniko" <eniko.ladanyi at vumc.org>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] source-level cluster analysis?
> Message-ID:
> <CH2PR12MB482443C8F35FDF498B20AB6B9D959 at CH2PR12MB4824.namprd12.prod.outlook.com>
>
> Content-Type: text/plain; charset="windows-1252"
>
> Thank you, Paolo! This is very helpful!
>
> Best,
>
> Eniko
>
>
> *********************
>
> Eniko Ladanyi, Ph.D.
>
> Postdoctoral Fellow
>
> Vanderbilt University Medical Center
>
> Music Cognition Lab
>
> https://enikoladanyi.com/
>
> ________________________________
> From: fieldtrip <fieldtrip-bounces at science.ru.nl> on behalf of Paolo Belardinelli <paolo.belardinelli at gmail.com>
> Sent: Thursday, March 4, 2021 2:02 PM
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] source-level cluster analysis?
>
> Hi Eniko.
>
> You might check this out:
>
> https://www.fieldtriptoolbox.org/example/source_statistics/<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.fieldtriptoolbox.org%2Fexample%2Fsource_statistics%2F&data=04%7C01%7Ceniko.ladanyi%40vumc.org%7C7ca941d103a64bf0927108d8df4894c0%7Cef57503014244ed8b83c12c533d879ab%7C0%7C1%7C637504850130270558%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=L5FRsfjl29oIrmwRFat9KKi39qRYTXgQ4%2F3HvZrIEuU%3D&reserved=0>
>
> I would add that in my experience a strong significance at sensor does not imply a significance at source.
>
> Best wishes,
>
> Paolo
>
> On Thu, Mar 4, 2021 at 8:13 PM Ladanyi, Eniko <eniko.ladanyi at vumc.org<mailto:eniko.ladanyi at vumc.org>> wrote:
>
> Hi all,
>
>
>
> I am analyzing an MEG dataset in which I only have access to source-level data. I ran cluster-based permutation tests on the data with pre-defined ROI-s but I was wondering if it would make sense not to pre-define ROI-s but search for spatial clusters with a cluster-based permutation test. I’m not familiar enough with MEG, so I can’t decide if the features of source-level data would allow me to run the cluster analysis the same way described here for sensor-level data: https://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq/<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.fieldtriptoolbox.org%2Ftutorial%2Fcluster_permutation_freq%2F&data=04%7C01%7Ceniko.ladanyi%40vumc.org%7C7ca941d103a64bf0927108d8df4894c0%7Cef57503014244ed8b83c12c533d879ab%7C0%7C1%7C637504850130280553%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=Jm36KzjU6sa3kvQApqvd%2FZXW2iKK%2FN6Fz1jaIOiQyfQ%3D&reserved=0>.
>
>
>
> Would cluster-based permutation test work as well with source-level data as with sensor-level data? If so, what kind of layout file should I use? Positions of sensors/electrodes can be defined with two coordinates but sources can be only defined with three coordinates.
>
>
>
> Thanks so much in advance for any advice!
>
>
>
> Best,
>
>
>
> Eniko
>
>
>
> *********************
>
> Eniko Ladanyi, Ph.D.
>
> Postdoctoral Fellow
>
> Vanderbilt University Medical Center
>
> Music Cognition Lab
>
> https://enikoladanyi.com/<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fenikoladanyi.com%2F&data=04%7C01%7Ceniko.ladanyi%40vumc.org%7C7ca941d103a64bf0927108d8df4894c0%7Cef57503014244ed8b83c12c533d879ab%7C0%7C1%7C637504850130280553%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=vgFBBWzYjFNEmvaPvhr3ThOH7QMyNn%2BLNaKT0zGyfBA%3D&reserved=0>
>
> _______________________________________________
> fieldtrip mailing list
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmailman.science.ru.nl%2Fmailman%2Flistinfo%2Ffieldtrip&data=04%7C01%7Ceniko.ladanyi%40vumc.org%7C7ca941d103a64bf0927108d8df4894c0%7Cef57503014244ed8b83c12c533d879ab%7C0%7C1%7C637504850130280553%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=p34mi%2Fh51RSbznI9DtGV8v0C4376LrkpUn77ltdT1vI%3D&reserved=0>
> https://doi.org/10.1371/journal.pcbi.1002202<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1371%2Fjournal.pcbi.1002202&data=04%7C01%7Ceniko.ladanyi%40vumc.org%7C7ca941d103a64bf0927108d8df4894c0%7Cef57503014244ed8b83c12c533d879ab%7C0%7C1%7C637504850130290546%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=1M4iU53dM6S%2Bc7wF2LIadAAOD17breor9WGiVcZe%2FV8%3D&reserved=0>
>
>
> --
> --
> [Università di Trento]<https://nam12.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.unitn.it%2F&data=04%7C01%7Ceniko.ladanyi%40vumc.org%7C7ca941d103a64bf0927108d8df4894c0%7Cef57503014244ed8b83c12c533d879ab%7C0%7C1%7C637504850130290546%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ET39V8iWv5i2qGVrVc44U9N7EWy%2FVQvk4A0OGbu1Jq0%3D&reserved=0>
>
> Paolo Belardinelli, PhD
>
> Center for Mind/Brain Sciences - CIMeC<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.cimec.unitn.it%2Fen&data=04%7C01%7Ceniko.ladanyi%40vumc.org%7C7ca941d103a64bf0927108d8df4894c0%7Cef57503014244ed8b83c12c533d879ab%7C0%7C1%7C637504850130300545%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=QPKw15ZT65cCabIDjy%2FUDm3eeuRYHJl4KFD9H43E5po%3D&reserved=0>
>
> University of Trento
> Via delle Regole, 101, 38123 Mattarello-Trento (TN), Italy
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