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pop_roi_connect.m
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pop_roi_connect.m
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% pop_roi_connect - call roi_connect to connectivity between ROIs
%
% Usage:
% EEG = pop_roi_connect(EEG, 'key', 'val', ...);
%
% Inputs:
% EEG - EEGLAB dataset containing ROI activity
%
% Optional inputs:
% 'morder' - [integer] Order of autoregressive model. Default is 20.
% 'nepochs' - [integer] number of data epoch. This is useful when
% comparing conditions. if not enough epochs can be extracted
% an error is returned. If there are too many, the first ones
% are selected (selecting the first epochs ensure they are mostly
% contiguous and that the correlation between them is similar
% accross conditions).
% 'naccu' - [integer] Number of accumulation for stats. Default is 0.
% 'methods' - [cell] Cell of strings corresponding to methods.
% 'CS' : Cross spectrum
% 'aCOH' : Coherence
% 'cCOH' : (Complex-valued) Coherency
% 'iCOH' : Absolute value of the imaginary part of Coherency
% 'GC' : Granger Causality
% 'TRGC' : Time-reversed Granger Causality
% 'wPLI' : Weighted Phase Lag Index
% 'PDC' : Partial directed coherence
% 'TRPDC' : Time-reversed partial directed coherence
% 'DTF' : Directed transfer entropy
% 'TRDTF' : Time-reversed directed transfer entropy
% 'MIM' : Multivariate Interaction Measure for each ROI
% 'MIC' : Maximized Imaginary Coherency for each ROI
% 'PAC' : Phase-amplitude coupling between ROIs (see the 'fcomb' and 'bs_outopts' input parameters)
% 'TDE' : Time-delay estimation between two selected ROIs (see the 'tde_regions' and 'tde_freqbands' input parameters)
% 'snippet' - ['on'|off] Option to compute connectivity over snippets. Default is 'off'.
% 'firstsnippet' - ['on'|off] Only use the first snippet (useful for fast computation). Default is 'off'.
% 'snip_length' - ['on'|'off'] Length of the snippets. Default is 60 seconds.
% 'errornosnippet' - ['on'|'off'] Error if snippet too short. Default 'on'.
% 'fcsave_format' - ['mean_snips'|'all_snips'] Option to save mean over snippets
% (shape: 101,68,68) or all snippets (shape: n_snips,101,68,68). Default is 'mean_snips.'
% 'freqresolution' - [integer] Desired frequency resolution (in number of frequencies).
% If specified, the signal is zero padded accordingly. Default is 0 (means no padding).
% 'fcomb' - [struct] Frequency combination for which PAC is computed (in Hz). Must have fields 'low' and
% 'high' with fcomb.low < fcomb.high. For example, fcomb.low = 10 and fcomb.high = 50 if single
% frequencies are used. fcomb.low = [4 8] and fcomb.high = [48 50] if frequency bands are used
% (might take a long time to compute so use with caution). Default is {} (this will cause an error when PAC is selected).
% 'bs_outopts' - [integer] Option which bispectral tensors should be stored in EEG.roi.PAC. Default is 1.
% 1 - store all tensors: b_orig, b_anti, b_orig_norm, b_anti_norm
% 2 - only store: b_orig, b_anti
% 3 - only store: b_orig_norm, b_anti_norm
% 4 - only store: b_orig, b_orig_norm
% 5 - only store: b_anti, b_anti_norm
% 'roi_selection' - [cell array of integers] Cell array of ROI indices {1, 2, 3, ...} indicating for which regions (ROIs) connectivity should be computed.
% Default is empty (in this case, connectivity will be computed for all ROIs).
% 'tde_method' - [integer] TDE method, must be between 1:4, open bispectral_TD_est.m for details. Default is 1.
% 'tde_regions' - [seed target] Array containing the seed and target region for time-delay estimation. Regions need to be passed as region *indices*,
% e.g., [2 10] will compute time-delays from region 2 -> 10 and 10 -> 2, corresponding to the information flow in both directions separately.
% Default is [] (will throw an error).
% 'tde_freqbands' - [f1 f2] Array containing the frequency band for which bispectral time-delays will be estimated. Default is [] (broadband).
% 'conn_stats' - ['on'|'off'] Run statistics on connectivity metrics. Default is 'off'.
% 'nshuf' - [integer] number of shuffles for statistical significance testing. The first shuffle is the true value. Default is 1001.
% 'freqrange' - [min max] frequency range in Hz. This is used to compute and plot p-values. Default is to plot broadband power.
% 'poolsize' - [integer] Number of workers in the parallel pool (check parpool documentation) for parallel computing
%
% Output:
% EEG - EEGLAB dataset with field 'roi' containing connectivity info.
%
% Note: Optional inputs to roi_connectivity_process() are also accepted.
%
% Author: Arnaud Delorme, UCSD, 2019
%
% Example
% p = fileparts(which('eeglab')); % path
% EEG = pop_roi_connect(EEG, 'headmodel', ...
% EEG.dipfit.hdmfile, 'elec2mni', EEG.dipfit.coord_transform, ...
% 'sourcemodel', fullfile(p, 'functions', 'supportfiles', ...
% 'head_modelColin27_5003_Standard-10-5-Cap339.mat'), 'sourcemodel2mni', ...
% [0 -26.6046230000 -46 0.1234625600 0 -1.5707963000 1000 1000 1000]);
%
% Use pop_roi_connectivity(EEG) to conectivity
% Copyright (C) Arnaud Delorme, [email protected]
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
%
% 2. Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
% THE POSSIBILITY OF SUCH DAMAGE.
% TO DO - Arno
% - Centralize reading head mesh and Atlas (there might be a function in
% Fieldtrip to do that) ft_read_volume ft_read_mesh
% - Make compatible with all Fieldtrip and FSL Atlases
% - Downsampling of Atlas - check bug submitted to Fieldtrip
% - Plot inside(blue) vs outside(red) voxels for source volume
function [EEG,com] = pop_roi_connect(EEG, varargin)
com = '';
if nargin < 1
help pop_roi_connect;
return
end
if ~isfield(EEG(1), 'roi') || ~isfield(EEG(1).roi, 'source_roi_data')
error('Cannot find ROI data - ROI data first');
end
if nargin < 2
rowg = [0.1 0.6 1 0.2];
% uigeom = { 1 1 rowg rowg 1 rowg rowg [0.1 0.6 0.9 0.3] 1 rowg 1 [0.5 1 0.35 0.5] [0.5 1 0.35 0.5] [0.5 1 0.35 0.5] [1] [0.9 1.2 1] };
uigeom = { [1] [1.2 1] [1.2 1] [1.2 1] [1.2 1] [1.2 1] [1.2 1] [1.2 1] [1.2 1] [1] [0.2 1 0.35 0.8] [0.2 1 0.35 0.8] [0.2 1 0.35 0.8] [0.2 1 0.35 0.8]};
uilist = { { 'style' 'text' 'string' 'Select connectivity measures' 'fontweight' 'bold' } ...
{ 'style' 'checkbox' 'string' 'Cross-spectrum (CS)' 'tag' 'cs' 'value' 1 } {} ...
{'style' 'checkbox' 'string' '(Complex-valued) Coherency (cCOH)' 'tag' 'ccoh' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Coherence (aCOH)' 'tag' 'acoh' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Imaginary Coherency (iCOH)' 'tag' 'icoh' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Weighted Phase Lag Index (wPLI)' 'tag' 'wpli' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Granger Causality (GC)' 'tag' 'gc' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Time-reversed GC' 'tag' 'trgc' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Partial Directed Coherence (PDC)' 'tag' 'pdc' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Time-reversed PDC' 'tag' 'trpdc' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Directed Transfer Entropy (DTF)' 'tag' 'dtf' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Time-reversed DTF' 'tag' 'trdtf' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Multivariate Interaction Measure (MIM)' 'tag' 'mim' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Maximized Imaginary Coherency (MIC)' 'tag' 'mic' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Phase-Amplitude Coupling (PAC)' 'tag' 'pac' 'value' 0 } ...
{ 'style' 'checkbox' 'string' 'Time Delay Estimation (TDE)' 'tag' 'tde' 'value' 0 } ...
{} ...
{} { 'style' 'text' 'string' 'Autoregressive model order' } { 'style' 'edit' 'string' '20' 'tag' 'morder' } {} ...
{} { 'style' 'text' 'string' 'Bootstrap if any (n)' } { 'style' 'edit' 'string' '' 'tag' 'naccu2' } {} ...
{} { 'style' 'text' 'string' 'Frequency combination in Hz (for PAC) [f1 f2]' } { 'style' 'edit' 'string' '' 'tag' 'fcomb' } {} ...
{} { 'style' 'text' 'string' 'Region selection by index (for TDE) [region1 region2]' } { 'style' 'edit' 'string' '' 'tag' 'tde_regions' } {} };
...
[result,~,~,out] = inputgui('geometry', uigeom, 'uilist', uilist, 'helpcom', 'pophelp(''pop_roi_connect'')', 'title', 'pop_roiconnect - connectivity');
if isempty(result), return, end
% check we have the same naccu
methods = {};
if out.cs, methods = [ methods { 'CS' } ]; end
% if out.coh, methods = [ methods { 'COH' } ]; end
if out.ccoh, methods = [ methods { 'cCOH' } ]; end
if out.acoh, methods = [ methods { 'aCOH' } ]; end
if out.icoh, methods = [ methods { 'iCOH' } ]; end
if out.gc , methods = [ methods { 'GC' } ]; end
if out.trgc, methods = [ methods { 'TRGC' } ]; end
if out.wpli, methods = [ methods { 'wPLI' } ]; end
if out.pdc , methods = [ methods { 'PDC' } ]; end
if out.trpdc, methods = [ methods { 'TRPDC' } ]; end
if out.dtf , methods = [ methods { 'DTF' } ]; end
if out.trdtf, methods = [ methods { 'TRDTF' } ]; end
if out.mim , methods = [ methods { 'MIM' } ]; end
if out.mic, methods = [ methods { 'MIC' } ]; end
if out.pac, methods = [ methods { 'PAC' } ]; end
if out.tde, methods = [ methods { 'TDE' } ]; end
options = { ...
'morder' str2num(out.morder) ...
'naccu' str2num(out.naccu2) ...
'methods' methods ...
'tde_regions' eval( [ '[' out.tde_regions ']' ] )};
if ~isempty(eval( [ '[' out.fcomb ']' ] ))
out_fcomb = eval( [ '[' out.fcomb ']' ] );
fcomb.low = out_fcomb(1);
fcomb.high = out_fcomb(2);
options = [options {'fcomb' fcomb}];
end
else
options = varargin;
end
% decode input parameters
% -----------------------
g = finputcheck(options, ...
{ 'morder' 'integer' { } 20;
'naccu' 'integer' { } 0;
'methods' 'cell' { } { };
'snippet' 'string' { 'on', 'off' } 'off';
'firstsnippet' 'string' { 'on', 'off' } 'off';
'errornosnippet' 'string' { 'on', 'off' } 'off';
'nepochs' 'real' {} [];
'snip_length' 'integer' { } 60;
'fcsave_format' 'string' { 'mean_snips', 'all_snips'} 'mean_snips';
'freqresolution' 'integer' { } 0;
'fcomb' 'struct' { } struct;
'bs_outopts' 'integer' { } 1;
'roi_selection' 'cell' { } { };
'tde_method' 'integer' { 1:4 } 1;
'tde_regions' 'integer' { } [ ];
'tde_freqbands' 'integer' { } [ ];
'conn_stats' 'string' { } 'off'; ...
'nshuf' 'integer' { } 1001; ...
'poolsize' 'integer' { } 1}, 'pop_roi_connect');
if ischar(g), error(g); end
% process multiple datasets
% -------------------------
if length(EEG) > 1
if nargin < 2
[ EEG, com ] = eeg_eval( 'pop_roi_connect', EEG, 'warning', 'off', 'params', options );
else
[ EEG, com ] = eeg_eval( 'pop_roi_connect', EEG, 'params', options );
end
return
end
tmpMethods = setdiff(g.methods, { 'CS' 'COH' 'cCOH' 'aCOH' 'iCOH' 'GC' 'TRGC' 'wPLI' 'PDC' 'TRPDC' 'DTF' 'TRDTF' 'MIM' 'MIC' 'PAC' 'TDE'});
if ~isempty(tmpMethods)
error('Unknown methods %s', vararg2str(tmpMethods))
end
% compute connectivity over snippets
if strcmpi(g.snippet, 'on') && strcmpi(g.conn_stats, 'off')
% n_conn_metrics = length(g.methods);
snippet_length = g.snip_length; % seconds
trials = size(EEG.roi.source_roi_data,3);
pnts = size(EEG.roi.source_roi_data,2);
snip_eps = snippet_length/(pnts/EEG.roi.srate); % snip length/epoch length (how many trials for each snippet)
nsnips = floor(trials/snip_eps);
if nsnips < 1
if strcmpi(g.errornosnippet, 'on')
error('Snippet length cannot exceed data length.\n')
else
fprintf(2, 'Snippet length cannot exceed data length, using the whole data\n')
nsnips = 1;
end
end
diff = (trials * pnts/EEG.roi.srate) - (nsnips * pnts/EEG.roi.srate * snip_eps);
if diff ~= 0
warning(strcat(int2str(diff), ' seconds are thrown away.'));
end
if strcmpi(g.firstsnippet, 'on')
nsnips = 1;
end
% check if Parallel Processing Toolbox is available and licensed
if license('test', 'Distrib_Computing_Toolbox') && ~isempty(ver('parallel'))
if isfield(g, 'poolsize') && isnumeric(g.poolsize) && g.poolsize > 0
% check if there's already an existing parallel pool
currentPool = gcp('nocreate');
if isempty(currentPool)
parpool(g.poolsize);
end
end
else
disp('Parallel Processing Toolbox is not installed or licensed.');
end
tmplist1 = setdiff(g.methods, {'PAC'}); % list of fc metrics without PAC
tmplist2 = intersect(g.methods, {'PAC'});
% store each connectivity metric for each snippet in separate structure
fc_matrices_snips = cell(nsnips, length(tmplist1));
if ~isempty(tmplist2)
switch g.bs_outopts % number of PAC metrics (check documentation)
case 1
bs_matrices_snips = cell(nsnips, 4);
fns = cell(nsnips, 4);
otherwise
bs_matrices_snips = cell(nsnips, 2);
fns = cell(nsnips, 2);
end
end
source_roi_data_save = EEG.roi.source_roi_data;
parfor isnip = 1:nsnips
% for isnip = 1:nsnips
EEG1 = EEG;
begSnip = (isnip-1)* snip_eps + 1;
endSnip = min((isnip-1)* snip_eps + snip_eps, size(source_roi_data_save,3));
roi_snip = source_roi_data_save(:,:, begSnip:endSnip ); % cut source data into snippets
EEG1.roi.source_roi_data = single(roi_snip);
EEG1 = roi_connect(EEG1, 'morder', g.morder, 'naccu', g.naccu, 'methods', g.methods,'freqresolution', g.freqresolution, 'roi_selection', g.roi_selection); % compute connectivity over one snippet
if ~isempty(intersect(g.methods, {'PAC'}))
EEG1 = roi_pac(EEG1, g.fcomb, g.bs_outopts, g.roi_selection);
end
if ~isempty(tmplist1)
tmp_fc_matrices = cell(1, length(tmplist1));
for fc = 1:length(tmplist1)
fc_name = g.methods{fc};
fc_matrix = EEG1.roi.(fc_name);
tmp_fc_matrices{fc} = fc_matrix;
end
fc_matrices_snips(isnip, :) = tmp_fc_matrices;
end
if ~isempty(tmplist2)
tmp_fns = fieldnames(EEG1.roi.PAC);
tmp_bs_matrices = cell(1, length(tmp_fns));
for bs = 1:length(tmp_fns)
bs_matrix = EEG1.roi.PAC.(tmp_fns{bs});
tmp_bs_matrices{bs} = bs_matrix;
end
bs_matrices_snips(isnip, :) = tmp_bs_matrices;
fns(isnip, :) = tmp_fns;
end
end
% shut down current parallel pool only if the toolbox is available
if license('test', 'Distrib_Computing_Toolbox') && ~isempty(ver('parallel'))
poolobj = gcp('nocreate');
if ~isempty(poolobj)
delete(poolobj);
end
end
% compute mean over connectivity of each snippet
if ~isempty(tmplist1)
for fc = 1:length(tmplist1)
fc_name = g.methods{fc};
[first_dim, second_dim, third_dim] = size(fc_matrices_snips{1,fc});
conn_cell = fc_matrices_snips(:, fc); % store all matrices of one metric in a cell
mat = cell2mat(conn_cell);
reshaped = reshape(mat, first_dim, nsnips, second_dim, third_dim);
reshaped = squeeze(permute(reshaped, [2, 1, 3, 4]));
if strcmpi(g.fcsave_format, 'all_snips')
EEG.roi.(fc_name) = reshaped;
else
if nsnips > 1
mean_conn = squeeze(mean(reshaped, 1));
else
mean_conn = reshaped;
end
EEG.roi.(fc_name) = mean_conn; % store mean connectivity in EEG struct
end
end
end
if ~isempty(tmplist2)
fns = fns(1, :);
for bs = 1:length(fns)
[second_dim, third_dim] = size(bs_matrices_snips{1, bs});
conn_cell = bs_matrices_snips(:, bs); % store all matrices of one metric in a cell
mat = cell2mat(conn_cell);
reshaped = reshape(mat, second_dim, nsnips, third_dim);
reshaped = squeeze(permute(reshaped, [2, 1, 3]));
if strcmpi(g.fcsave_format, 'all_snips')
EEG.roi.PAC.(fns{bs}) = reshaped;
else
if nsnips > 1
mean_conn = squeeze(mean(reshaped, 1));
else
mean_conn = reshaped;
end
EEG.roi.PAC.(fns{bs}) = mean_conn; % store mean connectivity in EEG struct
end
end
end
end
if strcmpi(g.snippet, 'off') && strcmpi(g.conn_stats, 'off')
EEG = roi_connect(EEG, 'morder', g.morder, 'naccu', g.naccu, 'methods', g.methods,'freqresolution', g.freqresolution, 'roi_selection', g.roi_selection);
if strcmpi(g.snippet, 'off') && ~isempty(intersect(g.methods, {'PAC'}))
EEG = roi_pac(EEG, g.fcomb, g.bs_outopts, g.roi_selection);
end
if strcmpi(g.snippet, 'off') && ~isempty(intersect(g.methods, {'TDE'}))
EEG = roi_tde(EEG, g.tde_method, g.tde_regions, g.tde_freqbands);
end
end
% TO-DO: add snippet option for stats mode
if strcmpi(g.conn_stats, 'on')
EEG = roi_connstats(EEG, 'methods', g.methods, 'nshuf', g.nshuf, 'roi_selection', g.roi_selection, 'freqresolution', g.freqresolution, 'poolsize', g.poolsize, 'fcomb', g.fcomb);
end
if nargout > 1
com = sprintf( 'EEG = pop_roi_connect(EEG, %s);', vararg2str( options ));
end