[FieldTrip] TRENTOOL pipeline help
Max Cantor
mcantor at umich.edu
Tue Sep 9 16:39:18 CEST 2014
Looking at the sample script, it should be reasonably straightforward for
me to adapt my pipeline, and between the ragwitz tutorial and your notes I
should be able to develop a better understanding of what the pipeline is
doing in a more sophisticated way. Again, thank you so much! I will try to
incorporate this new information as soon as possible, and if I have any
other issues I'll follow up, but hopefully this should solve most of my
problems.
On Tue, Sep 9, 2014 at 9:51 AM, Patricia Wollstadt <
Patricia.Wollstadt at gmx.de> wrote:
> 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> 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 listfieldtrip at donders.ru.nlhttp://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
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
>
> --
> Max Cantor
> Lab Manager
> Computational Neurolinguistics Lab
> University of Michigan
>
>
> _______________________________________________
> fieldtrip mailing listfieldtrip at donders.ru.nlhttp://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
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
--
Max Cantor
Lab Manager
Computational Neurolinguistics Lab
University of Michigan
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