[FieldTrip] TRENTOOL pipeline help
Patricia Wollstadt
Patricia.Wollstadt at gmx.de
Tue Sep 9 15:51:33 CEST 2014
I'm glad that cleared things up a little. Sorry about the confusion, we
will try to include warnings for deprecated function in the next release
of TRENTOOL.
You don't need to run TEprepare before running TEgroup_prepare. You just
run the four functions
- TEgroup_prepare
- InteractionDelayReconstruction_calculate
- InteractionDelayReconstruction_analyze
- TEgroup_stats
in that order.
The graph analysis is an optional step that can be run on the individual
outputs of InteractionDelayReconstruction_analyze. The function will
partially correct the output for multivariate effects. Since you are
looking at one connection only, you don't need to include the graph
analysis step into your pipeline. (I also sent you an example script
directly, because I think it is not a good idea to send attachments via
the mailing list.)
Best, Patricia
On 09/09/2014 03:19 PM, Max Cantor wrote:
> This is immensely helpful, thank you. I was very confused about why
> some versions of the pipeline I saw were using group calculate and
> others were using interaction delay reconstruction and what that
> meant, and I think I have a more clear idea of what the different
> steps of the pipeline are doing. There are still a few things I'm a
> bit confused about though in terms of the pipeline. For instance,
> whether or not I need to do TEprepare before group prepare, and if I
> need to do graph analysis (which I'm not sure I fully understand but
> also haven't looked deeply into) before group stats.
>
> If you don't mind me taking you up on your offer, I think seeing your
> example script might help clarify some of these issues.
>
> Thank you!
>
> On Tue, Sep 9, 2014 at 8:16 AM, Patricia Wollstadt
> <Patricia.Wollstadt at gmx.de <mailto:Patricia.Wollstadt at gmx.de>> wrote:
>
> Hello Max,
>
> I added a few comments to the questions regarding individual
> parameters below. To address the general problem of TRENTOOL
> telling you, that there are not enough sample points in your data:
> From what I can see in your script, you probably don't have enough
> data points in each time series to robustly estimate TE. You
> analyze 800 ms of data sampled at 300 Hz, which gives you 240
> samples per time series. Can you maybe avoid downsampling to 300
> Hz and downsample to 600 Hz instead? Or could you analyze a longer
> time window of interest?
> Note that you also 'lose' data to embedding and the interaction
> delay: The first point that can be used for TE estimation is at
> max. embedding length + max. interaction delay in samples. For
> example: max. embedding length = dim * tau_factor * ACT = 10 * 0.4
> * 5 = 20 samples plus the max interaction delay of 30 ms = 9
> samples. In this example, you would be left with 240 - 29 samples
> for TE estimation per trial. There is also the possibility to
> estimate time resolved TE/TE for shorter time windows of interest
> (see section 4.4 in the manual); however, this method requires the
> use of a GPU for TE estimation.
>
> I would further recommend to use the new pipeline for group
> statistics described in the manual in section 4.5 (the function
> 'TEgroup_calculate' is deprecated). The new pipeline allows you to
> reconstruct the interaction delay and uses the following functions
> (see also comments in the script):
>
> TEgroup_prepare -> prepares all data sets (all subjects/all
> conditions) for group analysis (this means finding common
> embedding parameters such that estimates are not biased between
> groups)
> InteractionDelayReconstruction_calculate -> estimates TE for
> individual data sets and all assumed interaction delays u
> InteractionDelayReconstruction_analyze -> reconstructs the
> interaction delay by selecting the u that maximizes TE for each
> channel
> TEgroup_stats -> calculate group statistics using a
> permutation test
>
> I can send you an example script for group TE analysis using this
> pipeline to get you started. I hope this helps you to get the
> group analysis running. Just write again if you're having trouble
> setting up the pipeline or something is not clear about the
> parameters/my comments.
>
> Best,
> Patricia
>
>
>
>
> On 09/04/2014 08:30 PM, Max Cantor wrote:
>> Hi fieldtrippers,
>>
>> I know trentool is not produced by the Donders Institute, so I'm
>> not 100% sure if it is appropriate to ask questions about it
>> here, but to the best of my knowledge they do not have a mailing
>> list and I saw a few trentool questions in the archives, so I'm
>> going to assume it's ok...
>>
>> In any case, below is my current pipeline (slightly modified for
>> comprehensibility):
>>
>> (notes in bold are comments/questions made in this email, not
>> present in the pipeline. Sorry in advance for the long post! Any
>> help would be greatly appreciated as I'm a bit over my head on
>> this but I think I'm close!)
>>
>> *****
>>
>> % Prepare group TE data
>>
>> cfgP = [];
>> cfgP.Path2TSTOOL = *TSTOOLPATH*
>> cfgP.TEcalctype = 'VW_ds';
>> cfgP.channel = {'ctfdip_LAC' 'ctfdip_RAC'};
>>
>> *I'm trying to find the transfer entropy between the left and
>> right auditory cortices in my experiment. The input is virtual
>> sensor data that was produced using SAM in fieldtrip on real MEG
>> data. *
>>
>> % specify u to be scanned
>>
>> cfgP.predicttime_u = 30;
>> cfgP.toi = [-0.4 0.4];
>>
>> *For clarification, the predicttime_u is in seconds but the toi
>> is in milliseconds. If I understand correctly, the predicttime_u
>> must fit within the toi, but beyond that are there any benefits
>> to it being earlier or later?* PW: The predictiontime_u is in
>> milliseconds and the toi is in seconds. The prediction time is
>> the assumed interaction delay between your two sources and should
>> fit within your toi. In general it is preferable to use the
>> method for interaction delay reconstruction for TE estimation,
>> because it allows you to reconstruct the actual delay between
>> your source and target times series. A non-optimal u/interaction
>> delay may cause an underestimation of TE, so it is recommended to
>> use the pipeline for interaction delay reconstruction whenever
>> estimating TE for unknown delays.
>> If you use the methods for interaction delay reconstruction
>> 'predicttime_u' is replaced by
>> cfgTEP.predicttimemin_u % minimum u to be scanned
>> cfgTEP.predicttimemax_u % maximum u to be scanned
>> cfgTEP.predicttimestepsize % time steps between u to be scanned
>> A large range for u values to be scanned increases computing time
>> a lot, so it is best to limit the u range to values that are
>> physiologically plausible.
>>
>> % ACT (Autocorrelation Time) estimation and constraints
>>
>> cfgP.maxlag = 150;
>> cfgP.actthrvalue = 7.5;
>> cfgP.minnrtrials = 5;
>>
>> *My understanding is maxlag should be 1/2 the sampling rate, so
>> since the data are downsampled to 300hz, it should be 150. I know
>> that the sample rate and filters are used to determine the
>> actthrvalue, but I don't actually know the calculation. 7.5 was a
>> rough guess just to test the pipeline. I'm also uncertain of what
>> minnrtrials should be.* PW: You can set the actthrvalue based on
>> the filtering you did prior to TE analysis. If you for example
>> highpass filtered at 10 Hz, you shouldn't find an ACT higher than
>> 30 samples, because you filtered out any components of the signal
>> slower than 10 Hz/30 samples (given your sampling frequency of
>> 300 Hz). So in this scenario the actthrvalue would be 30.
>> A good value for cfgP.minnrtrials is 12 (a minimum number of
>> trials is needed to realize the permutation test for estimated
>> TE values).
>>
>> % Optimization
>>
>> cfgP.optimizemethod = 'ragwitz';
>> cfgP.ragdim = 4:8;
>> cfgP.ragtaurange = [0.2 0.4];
>> cfgP.ragtausteps = 15;
>> cfgP.repPred = 100;
>> *
>> *
>> *I am completely at a loss for this. I've done some reading into
>> transfer entropy, mutual information, etc., cited in trentool,
>> but I'm yet to understand how exactly this optimization works and
>> what the configuration should be, given my data and experimental
>> intentions.* PW: The Ragwitz criterion tries to find optimal
>> embedding parameters dim and tau for the data. To do that, the
>> method iteratively takes all possible combinations of dim and tau
>> values that are provided in cfgP.ragdim and
>> cfgP.ragtaurange/.ragtausteps and tests how well these
>> combinations embed the data. To test an embedding, the method
>> builds the embedding vectors from the data; it then tests for
>> each point how well the next point in time can be predicted from
>> the reference point's nearest neighbours. So for each embedded
>> point, the method searches for the nearest neighbours and
>> calculates the average of those nearest neighbours. The
>> difference between the averaged/predicted point and the actual
>> next point is the error of the local predictor. The Ragwitz
>> criterion will then return the parameter combination for which
>> this error over all points is minimal.
>> The parameters set the following: 'ragdim' are dimensions to be
>> tested by the method (I would reccomend to start with 2:10),
>> 'ragtaurange' together with 'ragtausteps' specifies the tau
>> values to be tested (TRENTOOL will build a vector from 0.2 to 0.4
>> in 15 steps). Note, that the values here are factors that are
>> later multiplied with the ACT to obtain the actual tau. 'repPred'
>> is the number of points that will be used for the local
>> prediction, i.e. the Ragwitz criterion will test the local
>> prediction and calculate the error for the first 100 points in
>> your time series. The two parameters 'flagNei' ans 'sizeNei'
>> below specify the type of neighbour search conducted by the
>> Ragwitz criterion: 'flagNei' tells the method to either conduct a
>> kNN or range search; 'sizeNei' specifies the number of neighbours
>> or the radius to be searched by a range search.
>>
>>
>> % Kernel-based TE estimation
>>
>> cfgP.flagNei = 'Mass';
>> cfgP.sizeNei = 4; % Default
>>
>> cfgP.ensemblemethod = 'no';
>> cfgP.outputpath = *OUTPUT PATH*;
>>
>> if ~exist(*Path for TEprepare data object*)
>> load VSdat;
>> TE_Wrd = {};
>> for i = 1:nConds
>> for j = 1:Nsub
>> TE_Wrd{i}{j} = TEprepare(cfgP, VSdat{i}{j});
>> end
>> end
>> clear VSdat;
>> save('TE_Wrd', 'TE_Wrd');
>> end
>> *
>> *
>> *The configuration and virtual sensor data, organized in a 3 x 15
>> cell of structures (condition by subject) are the input. The
>> TEprepare substructure is added to each individual condition x
>> subject .mat files' data structure which are stored on disk
>> independently.*
>>
>> % Use object_to_mat_conversion.m to replace individual condition
>> x subject virtual sensor data
>> % .mat files with their TE_Wrd equivalent
>> *
>> *
>> *I'm using a separate script to make some manipulations to the
>> objects from disk; this will all eventually be integrated into
>> the main pipeline*.*TRENTOOL seems to handle data output very
>> differently from fieldtrip and I've had trouble thinking through
>> the most logical way to handle the data so it's a bit haphazard
>> right now.*
>>
>> load cond080sub01.mat
>>
>> cfgG = [];
>> cfgG.dim = cond080sub01.TEprepare.optdim;
>> cfgG.tau = cond080sub01.TEprepare.opttau;
>>
>> if isfield(cond080sub01, 'TEprepare')
>> TEgroup_prepare(cfgG, fileCell);
>> else
>> error('Need to run TEprepare before TEgroup_prepare');
>> end
>> *
>> *
>> *For clarification, fileCell is a cell with the name of each
>> condition x subject .mat file, which as I said before is
>> collectively the same as the 3 x 15 VSdat structure (condition x
>> subject).*
>>
>> % Replace .mat files with '_for_TEgroup_calculate' version in
>> % object_to_mat_conversion.m
>>
>> % TE Group Calculate
>>
>> load cond080sub01.mat
>> if isfield(cond080sub01, 'TEgroupprepare')
>> for i = 1:length(fileCell)
>> TEgroup_calculate(fileCell{i});
>> end
>> else
>> error('Need to run TEgroup_prepare before TEgroup_calculate');
>> end
>> *
>> *
>> *At this step I get the following error:
>>
>> Error using transferentropy (line 337)
>> \nTRENTOOL ERROR: not enough data points left after embedding
>>
>> Error in TEgroup_calculate (line 133)
>> [TEresult] = transferentropy(cfg,data);*
>>
>> % TE Group Stats
>>
>> cfgGSTAT = [];
>> cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];
>> cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];
>>
>> cfgGSTAT.uvar = 1;
>> cfgGSTAT.ivar = 2;
>> cfgGSTAT.fileidout = 'test_groupstats';
>>
>> TEgroup_stats(cfgGSTAT, fileCell);
>>
>> *Given the error above, I am yet to get to this step, but it does
>> not seem fundamentally different from normal fieldtrip stats.*
>>
>> *****
>>
>> In case my notes were not clear or you skipped to the bottom, *my
>> primary concern is whether the error I'm getting in
>> TEgroup_calculate is a pipeline issue* (I noticed the example
>> pipeline in trentool, the manual, and published methods articles
>> all seem to have slightly or significantly different pipeline
>> compositions), *or if the error is* due to ACT, ragwitz
>> optimization, or some other faulty parameterization *on my part
>> due to a lack of understanding of how transfer entropy works on a
>> more theoretical/mathematical level*. If the latter is the case,
>> is there any relatively straightforward way to conceptualize
>> this, or is this something where I'm just going to have to keep
>> reading and rereading until it eventually makes sense? I've
>> already done quite a bit of that and it hasn't pierced my thick
>> skull yet but I'm sure it will eventually!
>>
>> Thank you so much,
>>
>> Max Cantor
>>
>>
>> --
>> Max Cantor
>> Lab Manager
>> Computational Neurolinguistics Lab
>> University of Michigan
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> --
> ------------------------------------------------------------
>
> Patricia Wollstadt, PhD Student
>
> MEG Unit, Brain Imaging Center
>
> Goethe University, Frankfurt, Germany
>
> Heinrich Hoffmann Strasse 10, Haus 93 B
>
> D - 60528 Frankfurt am Main
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
>
> --
> Max Cantor
> Lab Manager
> Computational Neurolinguistics Lab
> University of Michigan
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
--
------------------------------------------------------------
Patricia Wollstadt, PhD Student
MEG Unit, Brain Imaging Center
Goethe University, Frankfurt, Germany
Heinrich Hoffmann Strasse 10, Haus 93 B
D - 60528 Frankfurt am Main
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