[FieldTrip] fieldtrip Digest, Vol 9, Issue 30
Anna Lambrechts
anna.lambrechts at gmail.com
Wed Aug 31 13:40:33 CEST 2011
Hello,
I have written yesterday about troubles while running ft_timelockstatistics.
As it happens now, this seems to be a compatibility issue between matlab
2011 and the current fieldtrip version (which you can solve temporarily by
using older versions of both).
Best wishes,
Anna.
2011/8/30 <fieldtrip-request at donders.ru.nl>
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> Today's Topics:
>
> 1. Call for Papers :: NIPS 2011 Workshop on Interpretable
> Decoding of Higher Cognitive States from Neural Data (Irina Simanova)
> 2. Issues with neighbours in ft_timelockstatistics (Anna Lambrechts)
> 3. Re: assessing significance in using ft_timelockanalysis
> results (Kanal Eliezer)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 30 Aug 2011 14:22:31 +0200
> From: Irina Simanova <irina.simanova at mpi.nl>
> To: Email discussion list for the FieldTrip project
> <fieldtrip at donders.ru.nl>
> Subject: [FieldTrip] Call for Papers :: NIPS 2011 Workshop on
> Interpretable Decoding of Higher Cognitive States from Neural Data
> Message-ID: <2E7CF2CC-C7AF-4937-935E-D0337E8445C8 at mpi.nl>
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>
> Interpretable Decoding of Higher Cognitive States from Neural Data
>
> NIPS 2011 Workshop, Dec 16 or 17, 2011, Granada, Spain
>
> (Please feel free to distribute the CFP to all the interested persons
> and groups.)
>
> Overview
>
> Over recent years, machine learning methods have become a crucial
> analytical tool in cognitive neuroscience (see reviews by Formisano et
> al., 2008; Pereira et al., 2009). Decoding techniques have
> dramatically increased the sensitivity of experiments, and so also the
> subtlety of cognitive questions that can be asked. At the same time
> the mental phenomena being studied are moving beyond lower-level
> perceptual and motor processes which are directly grounded in external
> measurable realities.
>
> Decoding higher cognition and interpreting the learned behaviour of
> the classifiers used pose unique challenges, as these psychological
> states are complex, fast-changing and often ill-defined. Contemporary
> machine learning methods deal well with the small numbers of cases,
> and high numbers of co-linear dimensions typical of neural data, and
> are generally optimized to maximize classification performance, rather
> than to enable meaningful interpretation of the features they learn
> from. And indeed recent work has succeeded to decode psychological
> phenomena including visual object recognition (e.g. Kriegeskorte et
> al., 2008; Connolly et al., 2011), perceptual interpretation of sounds
> (Staeren et al., 2009), lexical semantics (Mitchell et al., 2008;
> Siminova et al., 2010; Devereux et al., 2010; Murphy et al., 2011),
> decision making during game playing (Xiang et al., 2009) and the
> process of mental arithmetic (Anderson et al., 2008). But for the
> cognitive scientists who use these methods, the primary question is
> often not "how much" but rather "how" and "why" the patterns of neural
> activity identified by a machine learning algorithm encode particular
> cognitive processes.
>
> The aim of this workshop is therefore to 1) discuss the achievements
> and problems of the decoding of high-level cognitive states, and 2)
> explore the use of machine learning methodologies and other
> computational models that enable such cognitive interpretation of
> neural recordings of different modalities. Advances in this field
> require close collaboration between machine learning experts,
> neuroscientists and cognitive scientists. Thus, this workshop is
> highly interdisciplinary and will aim to attract submissions also from
> outside the existing NIPS community. By stimulating discussions among
> experts in the different fields, the workshop seeks to generate novel
> insights and new directions for research.
>
> Topics of interest
>
> The field requires techniques that are capable of taking advantage of
> spatially distributed patterns in the brain, that are separated in
> space but coordinated in their activity. Methods should also be
> sensitive to the fine-grained temporal patterns of multiple processes
> - which may proceed in a serial fashion, overlapping or in parallel
> with each-other, or in multiple passes with bidirectional information
> flows. Different recording modalities have distinctive advantages:
> fMRI provides very fine millimetre-level localisation in the brain but
> poor temporal resolution, while EEG and MEG have millisecond temporal
> resolution at the cost of spatial resolution. Ideally machine learning
> methods would be able to meaningfully combine complementary
> information from these different neuroimaging techniques (see e.g. De
> Martino et al., 2010). Moreover, as the processes underlying higher
> cognition are so complex, methods should be able to disentangle even
> tightly linked and confounded subprocesses. Finally, general use
> algorithms that could induce latent dimensions from neural data, and
> so reveal the "hidden" psychological states, would be a dramatic
> advance on current hypothesis-driven analytical paradigms. Originality
> of approach is encouraged and submissions on any related
> methodological approach are welcomed, such as:
>
> - Interpreting spatial and temporal location of selected features and
> their weights
> - Discovering "hidden" or "latent" cognitive representations
> - Disentangling confounded processes and representations
> - Comparing or combining data from recording modalities (e.g. fMRI,
> EEG, structural MRI, DTI, MEG, NIRS, EcOG, single cell recordings)
> - Fuzzy and partial classifications
> - Unaligned or incommensurate feature spaces and data representation
>
> As noted above, the complexity of higher cognition poses challenges.
> To take language comprehension as an example, speech is received at
> 3-4 words; acoustic, semantic and syntactic processing can occur in
> parallel; and the form of underlying representations (sentence
> structures, conceptual descriptions) remains controversial. We welcome
> submissions dealing with any high-level cognitive functions that
> exhibit similar complexity, for instance:
>
> - Knowledge representation and concepts
> - Language and communication
> - Understanding visual and auditory experience
> - Memory and learning
> - Reasoning and problem solving
> - Decision making and executive control
>
> Submissions
>
> Authors are invited to submit full papers on original, unpublished
> work in the topic area of this workshop via the NIPS 2011 submission
> site at https://cmt.research.microsoft.com/NIPS2011/Default.aspx.
> Submissions should be formatted using the NIPS 2011 stylefiles, with
> blind review and not exceeding 8 pages plus an extra page for
> references. Author and submission information can be found at
> http://nips.cc/PaperInformation/AuthorSubmissionInstructions
> . The stylefiles are available at
> http://nips.cc/PaperInformation/StyleFiles
> . Each submission will be reviewed at least by two members of the
> programme committee. Accepted papers will be published in the workshop
> proceedings. Dual submissions to the main NIPS 2011 conference and
> this workshop are allowed; if you submit to the main session, indicate
> this when you submit to the workshop. If your paper is accepted for
> the main session, you should withdraw your paper from the workshop
> upon notification by the main session.
>
> Important Dates
>
> - Aug 30, 2011: Call for papers
> - Sep 23, 2011: Deadline for submission of workshop papers
> - Oct 15, 2011: Notification of acceptance
> - Oct 31, 2011: Camera-ready papers due
> - Dec 16 or 17, 2011: Workshop date
>
> Links
>
> - NIPS 2011 website: http://nips.cc/Conferences/2011/
> - Workshop website: https://sites.google.com/site/decodehighcogstate
> - Call for Papers: https://sites.google.com/site/decodehighcogstate/cfp/
>
> Kind regards,
> The Workshop Organizers,
> Kai-min Kevin Chang, Anna Korhonen, Irina Simanova, Brian Murphy
>
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> ------------------------------
>
> Message: 2
> Date: Tue, 30 Aug 2011 17:46:52 +0200
> From: Anna Lambrechts <anna.lambrechts at gmail.com>
> To: fieldtrip at donders.ru.nl
> Subject: [FieldTrip] Issues with neighbours in ft_timelockstatistics
> Message-ID:
> <CALdyJ7HB+uaRs+mKa5s6ZxtZTbis-nNFz3EP7YQjZfamV2E3-g at mail.gmail.com
> >
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear fieldtrip community,
>
> Until about two weeks ago I was using a batch to process group comparison
> statistics on MEG data using ft_timelockstatistics. A week later, the same
> batch was not working anymore, even on the same data.
>
> The error I get is the following:
>
> *??? Improper index matrix reference.
>
> Error in ==> clusterstat>makechanneighbstructmat at 520
> [seld] = match_str(cfg.channel, cfg.neighbours(chan).label);
>
> Error in ==> clusterstat at 60
> channeighbstructmat = makechanneighbstructmat(cfg);
>
> Error in ==> statistics_montecarlo at 320
> [stat, cfg] = clusterstat(cfg, statrand, statobs,'issource',issource);
>
> Error in ==> statistics_wrapper at 290
> [stat, cfg] = statmethod(cfg, dat, design, 'issource',issource);
>
> Error in ==> ft_timelockstatistics at 124
> [stat, cfg] = statistics_wrapper(cfg, varargin{:});
>
> Error in ==> myfunction_stats at 59
> gpstat = ft_timelockstatistics(cfg, data1, data2);
>
> *It seems the error concerns the 'neighbours.mat' file reading, even though
> I am not sure about it. When I delete the two last lines in this file (to
> match the size of matrix wanted) the error disappears, but the processing
> is
> incomplete and false (or at least it does not match the results I had
> before).
>
> Has anyone encounter the same issue?
> Did you do any update recently that might explain this problem, and do you
> have any idea how to remedy to it?
>
> Best wishes,
> Anna.
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> ------------------------------
>
> Message: 3
> Date: Tue, 30 Aug 2011 11:58:34 -0400
> From: Kanal Eliezer <ekanal at cmu.edu>
> To: Email discussion list for the FieldTrip project
> <fieldtrip at donders.ru.nl>
> Subject: Re: [FieldTrip] assessing significance in using
> ft_timelockanalysis results
> Message-ID: <8CC6BA25-9A03-4630-B940-486BE2B9EE92 at cmu.edu>
> Content-Type: text/plain; charset=us-ascii
>
> Hello Eric -
>
> Thanks for the response. It looks like the fixed vs random analysis is
> exactly what I'm referring to. From what I understood, it looks like the
> difference really only shows up in the variance of the resultant
> distribution; with a random, the variance also takes into account the
> betwee-subjects variance. Is there a way to specify whether I want to do a
> fixed or random effects analysis in FieldTrip when I'm running
> ft_timelockgrandaverage or ft_freqgrandaverage? Thanks!
>
> Elli
>
> p.s. - In case anyone else is trying to figure this out, chapter 12 of
> Friston's book "Statistical Parametric Mapping" does an excellent job
> explaining the difference between fixed and random analyses, as well as how
> to implement it algorithmically.
>
>
>
> On Aug 26, 2011, at 3:00 PM, Eric Maris wrote:
>
> > Hi Kanal,
> >
> >> The event related statistics tutorial
> >> (http://fieldtrip.fcdonders.nl/tutorial/eventrelatedstatistics) talks
> > about
> >> assessing significance parametrically by running t-tests on pooled
> >> timelockanalysis data. My question is, does the fact that the averages
> > were
> >> created from N trials make a difference? If I'm condition A has twelve
> >> averages and condition B has another twelve, and each average contains
> 70
> >> trials, is there a way to "inform" the statistical test that the power
> in
> > this
> >> dataset is greater than 24? Is this only possible if I run the t-test
> > comparing
> >> each set of 840 (70*12) trials?
> >>
> >> I'm also curious whether this is possible with non-parametric analyses,
> as
> >> well. Thanks -
> >
> > In an analysis over subjects (called random-effects analysis in the fMRI
> > literature), "informing" the statistical test about the number of trials
> per
> > condition only makes sense if this number is different for the two
> > conditions. I propose that you have a look the fMRI papers that deal with
> > the issue of fixed-versus-random effect analyses. The conceptual issues
> > involved are the same in fMRI and electrophysiology.
> >
> >
> > Best,
> >
> > Eric Maris
> >
> >
> >
> >
> >>
> >> Elli Kanal
> >>
> >>
> >> --------------------
> >> Eliezer Kanal, Ph.D.
> >> Postdoctoral Fellow
> >> Center for the Neural Basis of Cognition
> >> Carnegie Mellon University
> >> 4400 Fifth Ave, Suite 110A
> >> Pittsburgh PA 15213
> >> P: 412-268-4115
> >> F: 412-268-5060
> >>
> >>
> >> _______________________________________________
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> >
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>
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