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added current TRENTOOL version (3.3.2)
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pwollstadt committed Mar 19, 2015
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97 changes: 97 additions & 0 deletions ChangeLog_TRENTOOL_v3.3.1.txt
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%% TRENTOOL 3.3.1 Release Notes - 18/12/2014 - www.trentool.de
%
%
% Main features:
% - speed up for interaction delay reconstruction
% - speed ups for group and single subject analysis
% - bug fixes in group analysis functions
% - TSTool functions are now integrated into TRENTOOL
% - deprecated functions now issue a warning
%
% The toolbox now contains four example analysis folders in
% 'exampledata/' with analysis and plotting scripts.
%

InteractionDelayReconstruction_calculate.m
PW: TEprepare is now called only once for each data set, using the
max. requested value for u (previously it was called for every u, which doesn't make a difference for the
embedding parameter optimization)
PW: The interaction delay is now reconstructed by only calculating the
TE for all values for u and then testing only the value that maximizes
TE for statistical significance

TEsurrogatestats.m, TEsurrogatestats_ensemble.m
PW: minor changes to fit the new work flow

InteractionDelayReconstruction_analyze.m
PW: new criterion for opt u selection to fit the new work flow

InteractionDelayReconstruction_plotting.m
PW: changed to fit the new work flow

TEplot2D.m
PW: new option that allows the plotting of the graph analysis output
(plots flagged edges in red)
PW: plotting the head contour is now optional

TEC_dsvalues.m, TEC_dsvalues_noSourceEmb.m
PW: TSTool functions are now called for all search points at once,
i.e. the function doesn't loop over neighbour searches per point -
this saves a lot of computational time
PW: bug fixes

TEgetACT.m
PW: function that returns the ACT for the data, helps to determine
parameters for embedding optimization

TEarch.m
PW: Adds the appropriate subfolder of the tstool_functions folder
(according to architecture and OS) - all functions calling TSTool
functions now no longer need to cd into the TSTool folder
(TEprepare.m, transferentropy.m, TEragwitz.m)

TEgroup_prepare.m
PW: TEprepare is now called only once for each data set, using the
max. requested u
PW: Parameter optimization is aborted if for any subject the max.
requested dim (ragdim) is found to be optimal for embedding; the max.
ragdim is then used for all data sets

TEprepare.m
PW: Function now accepts scalar values for both ragtau and ragdim
PW: The path to TSTool is no longer a necessary parameter

TEsurrogatestats_binomstats.m
PW: added three additional outputs (occurrences, bino_cdf,
occurr_thresh)
PW: bug fix - cfg.alpha wasn't used
PW: bug fix - 0 occurences of a link caused the function to crash

TRENTOOL2BrainNet.m
PW: bug fix in the edge coloring

TEsurrogatestats_ensemble.m
PW: For debugging the neighbour counts can be written to disc

private/transferentropy.m
PW: bug fix in the parfor loop (needed for running TRENTOOL with the parallel toolbox)
PW: changes to accomodate the new work flow for the reconstruction of interaction delays

private/TEcmc.m
PW: bug fix - threhsold for FDR correction was not calculated
correctly

private/TEbacktracking.m
PW: changed the output format for alternative paths

private/TEflagedges.m
PW: simplified the code and added documentation

private/TEfindDelay.m
PW: optimizes the interaction delay by calculating all TE values w/o
doing surrogate testing

The following functions are considered deprecated:
TEconditionstatssingle.m -> use TEgroup_prepare/TEgroup_stats instead
TEgroup_calculate.m -> use TEgroup_prepare/TEgroup_stats instead

189 changes: 189 additions & 0 deletions GPLv3_license.txt
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8 changes: 8 additions & 0 deletions GPU_howto.txt
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Code that runs on CUDA devices needs to be compiled specifically for the user's device. Compilation is done by evoking the function 'install.m' from Matlab. 'install.m' will compile two mex files

- fnearneigh_gpu.mexa64
- rangesearch_all_gpu.mexa64

from the corresponding *.cpp files using the library libgpuKnnLibrary.a. libgpuKnnLibrary.a contains uncompiled CUDA code for all devices to date (as of May 2013).
(Make sure, both mex files are within the TRENTOOL folder before starting any calculations.)

284 changes: 284 additions & 0 deletions InteractionDelayReconstruction_analyze.m
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function TGA=InteractionDelayReconstruction_analyze(cfg,data)

% analyzes and compacts the output of InteractionDelayReconstruction_calculate
%
% the output of InteractionDelayReconstruction_calculate which serves as an input here
% is a cell array of results from separate TEsurrogatestats processes --
% each TEsurrogatestats has been called with an individual prediction
% time u one, and the output is collected as one result of TEsurrogatestats
% per cell (see below for details)
% The ouput of InteractionDelayReconstruction_analyze looks like the output of
% TEpermstats with a column for the optimal interaction
% delay (predicttime_u) added as a seventh column
%
% You can call this function directly as follows:
% dataout=TEprepare(cfg, data)
%
% * DEPENDENCIES
%
% * INPUT PARAMETERS
%
% data = output of InteractionDelayReconstruction_calculate. Cell array with as many
% cells as predictime_u's that were scanned in InteractionDelayReconstruction_calculate.
% Each cell contains the output of a call to TEsurrogatestats (TEpermtest and TEresult)
% (see below for details)
%
% TEpermtest
% .TEpermvalues = matrix with size:
% (channelpair,value)
% The last dimension "value" includes:
% 1 - p_values of the statistic within the
% distribution given by the permutations
% 2 - 1 (0), if the statistics is significant at
% the prescribed alpha level (or not)
% 3 - 1 (0), if the statistics is significant
% after correction for multiple comparisons
% (or not)
% 4 - 1 (0), mean difference or tvalue of mean
% difference depending on cfg.permstatstype
% 5 - 1 (0), if instantaneous mixing (volume
% conduction) exists (or not)
% .TEmat = matrix containing raw TE values for each
% channel combination (channelcombi x trial for
% TE estimation on CPU or channelcombi x 1 for
% TE estimation using the ensemble method/GPU
% estimation)
% .dimord = dimensions of TEpermvalues
% .cfg = configuration file used to calculate TE and
% permtest
% .sgncmb = labels of channel combinations (source ->
% target)
% .numpermutation = number of permutations
% .ACT = structure including
% .act = ACT matrix (channelcombi x 2 x trial)
% .nr2cmc = number of tests to correct for multiple
% comparisons
% .TEprepare = results of the function TEprepare from the
% data
%
% AND
%
% TEresult (= Output structure of the function tranferentropy)
% .TEmat = resultmatrix including transfer entropy(TE)
% values (channelpairs x u x trial)
% .MImat = resultmatrix including mutual information (MI)
% values (channelpairs x u x trial)
% .dimord = 'channelpair_u_trial'; the dimensions of TEmat
% and MImat
% .cfg = configuration file used to calculate TE
% .trials = trial numbers selected from raw dataset
% .act = ACT matrix (channelcombi x 2 x trial)
% .sgncmb = labels of channel combinations (source -> target)
% .TEprepare = results of the function TEprepare from the
% data
% if instantaneous mixing is found in the data, then another field will
% be added:
% .instantaneousmixing = matrix (channel x u) which indicates were
% the instantaneous mixings were found (1) or not (0).%
%
% cfg a configuration structure with the fields
%
% .select_opt_u = selects the way the optimal u is determined
% optiomns are:
%
% 'min_p' - optimal predictiontime u is the one
% with the largest statistical distance (smallest
% randomization p-value) to surrogate data.
% This option might be problematic with
% respect to later testing of existence of
% a link if not used on independent data first.
%
% 'max_TE' - optimal predictiontime u is the
% one with the highest TE raw value (mean TE value
% over trials). This is used as default when
% determining the optimal information transfer
% delay.
%
% 'max_TEdiff' - optimal predictiontime u is the
% one with the largest difference in the test
% statistic between data and surrogates.
% This option might be problematic if different
% predictiontimes u lead to vastly different
% embedding via the optimization in the ragwitz
% criterion.
%
% 'product_evidence' - optimal predictiontime u is
% the one which maximes the product (1-p)*TEdiff.
% is is a statistically weighted measure of
% TEdifferences between data and surrogates
% (experimental feature)
%
% .select_opt_u_pos= 'shortest' select the shortest u if multiple u's
% optimize the target quantity (minimum p,
% maximum TE difference); 'longest' select the
% longest u that optimizes the target quantity
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation;
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY;
% version2.0
% (C) Michael Wibral, Raul Vicente and Michael Lindner 2012

% CHANGELOG
% Nicu Pampu:bugfix for the nan index (initial was without value when using function max) now set to 1
% 13-11-03 PW: add u-reconstruction for ensemble method
% 13-27-06 PW: field .groupprepare is added to the ouptput if
% TEprepare_group was run on the data previous to TE analysis
% 13-12-11 PW: field .TEmat containing the raw TE values for each channel
% combination is added to the ouptput, raw TE values are needed for the
% group statistics
% 27-11-14 PW: added option 'max_TE' for opt u selection

NumOfUs = length(data); % the number of different prediction times u scanned in previous call to InteractionDelayReconstruction_calculate
NumSgnCmbs = size(data{1}.sgncmb,1); % the number of signal combinations in the input, taken from the first cell

% collect data into an efficient structure
TGA = data{1}; % dummy copy, output structure

% create a container to collect indivicual TEpermvalues into a 3-dim array
TGA.TEpermvaluesTmp = nan([NumSgnCmbs NumOfUs]);

% preallocate the output array
TGA.TEpermvalues = zeros([NumSgnCmbs 5]);

TGA.sgncmb=data{1}.sgncmb;
% remove misleading old information
%TGA=rmfield(TGA.cfg,'predicttime_u');

TEDiffMat = nan(NumSgnCmbs,NumOfUs); % called TEDiffmat, because it contains TE values in the form of differences against surrogates
TERawmat = nan(NumSgnCmbs,NumOfUs); % collect all raw TE values, used for 'max_TE'
uvec = nan(NumOfUs,1);
TEmat = nan(size(data{1}.TEmat)); % remember the raw TE values, needed for group statistics


% collect TEpermvalues tables for all u's into one temporary data structure
for uu=1:NumOfUs
%TGA.TEpermvaluesTmp(:,:,uu) = data{uu}.TEpermvalues;
uvec(uu) = data{uu}.cfg.predicttime_u(1);
%TEDiffMat(:,uu) = data{uu}.TEpermvalues(:,4);
TERawmat(:,uu) = mean(data{uu}.TEmat,2);
end

minp = nan(NumSgnCmbs,1);
IdxMinP = nan(NumSgnCmbs,1);
OptUTmp = nan(NumSgnCmbs,1);

% loop over channel combinations
for cc=1:NumSgnCmbs

if strcmp(cfg.select_opt_u,'min_p') % look for the u with the smallest p-value

% find the index of the optimal u
minp(cc) = min(squeeze(TGA.TEpermvaluesTmp(cc,1,:)));
IdxMinPTmp = find(TGA.TEpermvaluesTmp(cc,1,:)==minp(cc));

if strcmp(cfg.select_opt_u_pos,'shortest')
IdxMinP(cc) = IdxMinPTmp(1);
elseif strcmp(cfg.select_opt_u_pos,'longest')
IdxMinP(cc) = IdxMinPTmp(end);
end

% get data according to the index of the optimal u
if ~isnan( IdxMinP(cc) )
TGA.TEpermvalues(cc,:) = TGA.TEpermvaluesTmp(cc,:,IdxMinP(cc)); % get line from TEpermvalues
OptUTmp(cc) = data{IdxMinP(cc)}.cfg.predicttime_u; % get the u
TEmat(cc,:) = data{IdxMinP(cc)}.TEmat(cc,:); % get raw TE values
else % if all TEpermvalues have been nans then also set the final value to nan
TGA.TEpermvalues(cc,:) = [1 0 0 NaN 1 0]; % p, sig(uncor), sig (corr), TE, volcond, delay
OptUTmp(cc) = 0;
TEmat(cc,:) = zeros(size(data{1}.TEmat(cc,:)));
end

elseif strcmp(cfg.select_opt_u,'max_TEdiff') % look for the u with the largest TE difference

% find the index of the optimal u
maxTE = max(squeeze(TGA.TEpermvaluesTmp(cc,4,:))); % find the maximum TE difference over all u's
IdxMaxTETmp = find(TGA.TEpermvaluesTmp(cc,4,:)==maxTE);

if isnan(maxTE) % if the index is a nan, we set it manually,
IdxMaxTE = 1; % otherwhise MATLAB will produce an error
else
if strcmp(cfg.select_opt_u_pos,'shortest')
IdxMaxTE = IdxMaxTETmp(1);
elseif strcmp(cfg.select_opt_u_pos,'longest')
IdxMaxTE = IdxMaxTETmp(end);
end
end

% get data according to the index of the optimal u
TGA.TEpermvalues(cc,:) = TGA.TEpermvaluesTmp(cc,:,IdxMaxTE); % get line from TEpermvalues
OptUTmp(cc) = data{IdxMaxTE}.cfg.predicttime_u; % get the u
TEmat(cc,:) = data{IdxMaxTE}.TEmat(cc,:); % get raw TE values

elseif strcmp(cfg.select_opt_u,'product_evidence') % get a evidence weighted TEdifference metric

% find the index of the optimal u
Product = (1-squeeze(TGA.TEpermvaluesTmp(cc,1,:))) .* squeeze(TGA.TEpermvaluesTmp(cc,4,:));
maxProduct = max(Product);
IdxMaxProductTmp = find(Product==maxProduct);

if isnan(maxProduct) % if the index is a nan, we set it manually,
IdxMaxProduct = NaN; % otherwhise MATLAB will produce an error
else
if strcmp(cfg.select_opt_u_pos,'shortest')
IdxMaxProduct = IdxMaxProductTmp(1);
elseif strcmp(cfg.select_opt_u_pos,'longest')
IdxMaxProduct = IdxMaxProductTmp(end);
end
end

% get data according to the index of the optimal u
if ~isnan( IdxMaxProduct )
TGA.TEpermvalues(cc,:) = TGA.TEpermvaluesTmp(cc,:,IdxMaxProduct); % get line from TEpermvalues
OptUTmp(cc) = data{IdxMaxProduct}.cfg.predicttime_u; % get the u
TEmat(cc,:) = data{IdxMaxProduct}.TEmat(cc,:); % get raw TE values
else % if all TEpermvalues have been nans then also set the final value to nan
TGA.TEpermvalues(cc,:) = [1 0 0 NaN 1]; % p, sig(uncor), sig (corr), TE, volcond, delay
OptUTmp(cc) = 0;
TEmat(cc,:) = zeros(size(data{1}.TEmat(cc,:)));
end

elseif strcmp(cfg.select_opt_u,'max_TE') % use the max. raw TE value to select the opt u
% find the index of the optimal u
[maxTE, IdxMaxTETmp] = max(TERawmat(cc,:));

if isnan(maxTE) % if the index is a nan, we set it manually,
IdxMaxTE = 1; % otherwhise MATLAB will produce an error
else
if strcmp(cfg.select_opt_u_pos,'shortest')
IdxMaxTE = IdxMaxTETmp(1);
elseif strcmp(cfg.select_opt_u_pos,'longest')
IdxMaxTE = IdxMaxTETmp(end);
end
end

% get data according to the index of the optimal u
%TGA.TEpermvalues(cc,:) = TGA.TEpermvaluesTmp(cc,:,IdxMaxTE); % get line from TEpermvalues
OptUTmp(cc) = data{IdxMaxTE}.cfg.predicttime_u(1); % get the u
TEmat(cc,:) = data{IdxMaxTE}.TEmat(cc,:); % get raw TE values

end
end

% insert the vector of optimal u-value and remove volume conduction (volume conduction will result in u = 0 ms).
if strcmp(data{1}.cfg.ensemblemethod,'yes')

% do not correct for volume conduction, this is handled by the
% mandatory use of the Faes-method
TGA.TEpermvalues(:,6)=(OptUTmp);

else
VolCondIndicator = (ones(size(squeeze(TGA.TEpermvalues(:,5))))-squeeze(TGA.TEpermvalues(:,5))); % 0 for volume conduction, 1 for OK
TGA.TEpermvalues(:,6) = (OptUTmp).*VolCondIndicator;
end;

% add additional parameters to the output
TGA.TEmat = TEmat;
TGA.uvec = uvec;
%TGA.TEDiffMat = TEDiffMat;
if isfield(data{1},'groupprepare')
TGA.groupprepare = data{1}.groupprepare;
end
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