[FieldTrip] TRENTOOL pipeline help

Patricia Wollstadt Patricia.Wollstadt at gmx.de
Tue Sep 9 14:16:20 CEST 2014

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.


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.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
> 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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