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roi_activity.m
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roi_activity.m
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% roi_activity - call roi_connectivity_process to compute
% connectivity between ROIs
% Usage:
% EEG = roi_activity(EEG, 'key', 'val', ...);
%
% Inputs:
% EEG - EEGLAB dataset
%
% Required inputs:
% 'leadfield' - [string or struct] leadfield structure (Fieldtrip)
% or file containing Fieldtrip or Brainstrom leadfield
% matrix.
% 'sourcemodel' - [string] source model file also containing Atlas info.
%
% Optional inputs:
% 'model' - ['eLoretaFieldtrip'|'lcmvFieldtrip'|'eLoreta'|'lcmv'] distributed
% source localization method. Default is 'lcvm'
% (Beamforming) from ROIconnect. 'eLoretaFieldtrip' and
% 'lcmvFieldtrip' are alternative implementations in
% Fieldtrip that should return similar results.
% 'sourcemodel2mni' - [9x float] homogeneous transformation matrix to convert
% sourcemodel to MNI space.
% 'sourcemodelatlas' - [string] name of Atlas to use (must be contained
% in Atlas field of the sourcemodel file.
% 'nPCA' - [interger] Number of PCA component for each ROI. Each ROI
% is made of many voxel. Instead of averaging their activity,
% this function takes the x first PCA components, then use
% these to compute connectivity (default is 3)
% 'naccu' - [interger] For bootstrap, number of accumulation. Default is
% none.
% 'eloretareg' - [float] regularization term for eLoreta. Default is 0.05.
% 'roiactivity' - ['on'|'off'] compute ROI activity. Default is on. If
% you just need voxel activity, you can set this option to
% ' off'.
% 'chansel' - [cell array of string] channel selection. Default is all.
% 'exportvoxact' - ['on'|'off'] export voxel activity in EEG.roi.source_voxel_data
% These array are huge, so the default is 'off'.
% 'fooof' - ['on'|'off'] enable FOOOF analysis (this method can be used to parameterize neural power spectra and is described here: https://fooof-tools.github.io/fooof/). Default is 'off'.
% 'fooof_frange' - [ ] FOOOF fitting range. Default is [1 30] like in the MATLAB example:
% https://github.com/fooof-tools/fooof_mat/blob/main/examples/fooof_example_one_spectrum.m.
% 'freqresolution' - [integer] Desired frequency resolution (in number of frequencies). If
% specified, the signal is zero padded accordingly.
% Default is 0 (means no padding).
% 'lowmemory' - ['on'|'off'] Option to run the code with low memory, though, it might take significantly longer to complete. When turned on, the estimation of voxel-wise spectral power
% will require less memory.
%
% Output:
% EEG - EEGLAB dataset with field 'roi' containing connectivity info.
% source_voxel_data - voxel data activity (voxels x times x trials).
% Usually several Gb in size.
%
% Author: Stefan Haufe and Arnaud Delorme
%
% Example: call pop_roi_activity instead because it will
% compute the leadfield matrix automatically using Dipfit information.
% 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.
function [EEG, source_voxel_data] = roi_activity(EEG, varargin)
if nargin < 2
help roi_activity;
return
end
% decode input parameters
% -----------------------
g = finputcheck(varargin, { ...
'leadfield' {'struct' 'string'} {{} {}} '';
'headmodel' 'string' { } ''; % sometimes useful when loading volume to see which voxels are inside/outside
'sourcemodel' 'string' { } '';
'sourcemodel2mni' 'real' { } [];
'sourcemodelatlas' 'string' { } '';
'modelparams' 'cell' { } { 0.05 };
'model' 'string' { 'eLoretaFieldtrip' 'lcmvFieldtrip' 'eLoreta' 'lcmv' } 'lcmv';
'nPCA' 'integer' { } 3;
'downsample' 'integer' { } 1;
'chansel' {'integer' 'cell'} { {} {} } {};
'roiactivity' 'string' { 'on' 'off' } 'on';
'channelpower' 'string' { 'on' 'off' } 'off';
'exportvoxact' 'string' { 'on' 'off' } 'off';
'fooof' 'string' { 'on' 'off'} 'off';
'fooof_frange' '' {} [1 30];
'freqresolution' 'integer' {} 0;
'lowmemory' 'string' { 'on' 'off'} 'off';
'outputdir' 'string' { } '' }, 'roi_activity');
if ischar(g), error(g); end
if isempty(g.leadfield), error('Leadfield is mandatory parameter'); end
% Creating result folder
if ~isempty(g.outputdir)
mkdir(fullfile( g.outputdir, 'data'));
end
% Cortex mesh or volume
% ---------------------
[~,~,ext] = fileparts(g.sourcemodel);
if strcmpi(ext, '.head')
[~, grid, labels, strlabels ] = load_afni_atlas(g.sourcemodel, g.headmodel, g.sourcemodel2mni, g.downsample);
uniqueROIs = unique(labels);
nROI = length(uniqueROIs);
cortex.Atlas(1).Name = g.sourcemodelatlas;
for iROI = 1:nROI
indVertices = find(labels == uniqueROIs(iROI));
cortex.Atlas(1).Scouts(iROI).Label = strlabels{iROI};
cortex.Atlas(1).Scouts(iROI).Vertices = indVertices;
end
cortex.Vertices = grid;
else
cortex = load(g.sourcemodel);
if isfield(cortex, 'Faces')
% make brainstorm coordinate system consistent with MNI coordinates for
% plotting (in terms of axis directions)
disp('Brainstorm cortex mesh detected - transforming to MNI coordinates');
tf = traditionaldipfit(g.sourcemodel2mni);
pos = tf*[cortex.Vertices ones(size(cortex.Vertices,1),1)]';
pos = pos';
cortex.Vertices = pos(:,1:3);
elseif isfield(cortex, 'cortex') && isfield(cortex, 'atlas')
hm = cortex;
clear cortex;
% align with MNI coordinates
if ~isempty(g.sourcemodel2mni)
tf = traditionaldipfit(g.sourcemodel2mni);
pos = tf*[hm.cortex.vertices ones(size(hm.cortex.vertices,1),1)]';
pos = pos';
else
pos = hm.cortex.vertices;
end
cortex.Vertices = pos(:,1:3);
cortex.Faces = hm.cortex.faces;
% make Alejandro Atlas definition compatible with Brainstrom one
nROI = length(hm.atlas.label);
cortex.Atlas(1).Name = g.sourcemodelatlas;
for iROI = 1:nROI
indVertices = find(hm.atlas.colorTable == iROI);
cortex.Atlas(1).Scouts(iROI).Label = hm.atlas.label{iROI};
cortex.Atlas(1).Scouts(iROI).Vertices = indVertices;
end
elseif isnumeric(cortex) && mod(size(cortex,1),3) == 0 && size(cortex,2) == 6
% NFT matrix
cortextmp = cortex;
clear cortex;
cortex.Vertices = cortextmp(:,1:3);
cortex.Atlas(1).Name = g.sourcemodelatlas;
elseif ~isfield(cortex, 'Vertices')
% code below is functional to load a mesh
% However, need to align with an Atlas
% This can be achieve with Fieldtrip functions
sourcemodelOriOld = ft_read_headshape(fullfile(ftPath, 'template', 'sourcemodel', 'cortex_20484.surf.gii'));
error('Unknown mesh format')
end
end
% Select Atlas
% ------------
found = false;
for iAtlas = 1:length(cortex.Atlas)
if strcmpi(cortex.Atlas(iAtlas).Name, g.sourcemodelatlas)
cortex.Atlas = cortex.Atlas(iAtlas);
found = true;
break
end
end
if ~found
error('Atlas not found');
end
% leadfield matrix (Brainstorm or Fieldtrip)
% ------------------------------------------
if ~isstruct(g.leadfield)
leadfield = load(g.leadfield, '-mat');
else
leadfield = g.leadfield;
end
if isstruct(leadfield) && isfield(leadfield, 'roiconnectleadfield')
leadfield = leadfield.roiconnectleadfield;
elseif isstruct(leadfield) && isfield(leadfield, 'Gain')
% brainstorm
% make format compatible with Stefan's routines
leadfield = permute(reshape(leadfield.Gain, [], 3, nvox), [1 3 2]);
elseif isstruct(leadfield) && isfield(leadfield, 'leadfield')
% fieldtrip
oldLeadfield = leadfield;
leadfield.gain = reshape( [ leadfield.leadfield{:} ], [length(leadfield.label) 3 length(leadfield.leadfield)]);
leadfield.gain = permute(leadfield.gain, [1 3 2]);
leadfield = leadfield.gain;
elseif isfield(leadfield, 'LFM')
% NFT
leadfield = leadfield.LFM;
else
disp('Warning: unknown leadfield matrix format, assuming array of gain values');
end
nvox = size(cortex.Vertices, 1);
nvox2 = size(leadfield,2);
if ~isequal(nvox, nvox2)
error('There must be the same number of vertices/voxels in the leadfield and source model');
end
if isempty(g.chansel)
g.chansel = [1:EEG.nbchan];
% if isfield(EEG.dipfit, 'chansel')
% g.chansel = EEG.dipfit.chansel;
% else
% g.chansel = 1:EEG.nbchan;
% end
elseif iscell(g.chansel)
g.chansel = eeg_decodechan(EEG.chanlocs, g.chansel);
end
if ~isequal(size(leadfield,1), length(g.chansel))
error('There must be the same number of channels in the leadfield and in the list of selected channels');
end
fres = EEG.pnts/2;
% from the MVGC toolbox, compute frequencies in Hz for a
frqs = sfreqs(fres, EEG.srate);
% common average reference transform
nbchan = length(g.chansel);
H = eye(nbchan) - ones(nbchan) ./ nbchan;
% apply to data and leadfield
tmpData = reshape(H*EEG.data(g.chansel, :), nbchan, EEG.pnts, EEG.trials);
leadfield = reshape(H*leadfield(:, :), nbchan, nvox, 3);
%% source reconstruction
if strcmpi(g.model, 'eLoreta')
% eLORETA inverse projection kernel
disp('Computing eLoreta...');
P_eloreta = mkfilt_eloreta_v2(leadfield, g.modelparams{:});
% project to source space
source_voxel_data = reshape(tmpData(:, :)'*P_eloreta(:, :), EEG.pnts*EEG.trials, nvox, 3);
elseif strcmpi(g.model, 'LCMV')
C = cov(tmpData(:, :)');
if length(g.modelparams) == 1
lcmv_reg = g.modelparams{1};
end
alpha = lcmv_reg*trace(C)/length(C);
Cr = C + alpha*eye(nbchan);
[~, P_eloreta] = lcmv(Cr, leadfield, struct('alpha', 0, 'onedim', 0));
source_voxel_data = reshape(tmpData(:, :)'*P_eloreta(:, :), EEG.pnts*EEG.trials, nvox, 3);
source_voxel_data = 10^3*source_voxel_data; % the units are nA*m
else
% transform the data to continuous so we can get an estimate for each sample
EEG2 = EEG;
EEG2.data = EEG2.data(:,:);
EEG2.pnts = size(EEG2.data,2);
EEG2.trials = 1;
EEG2 = eeg_checkset(EEG2);
dataPre = eeglab2fieldtrip(EEG2, 'preprocessing', 'dipfit');
% prepare data
cfg = [];
cfg.channel = {'all', '-EOG1'};
cfg.reref = 'yes';
cfg.refchannel = {'all', '-EOG1'};
dataPre = ft_preprocessing(cfg, dataPre);
% load head model and prepare leadfield matrix
vol = load('-mat', g.headmodel);
% source reconstruction
cfg = [];
if lower(g.model(1)) == 'e'
cfg.method = 'eLoreta';
else
cfg.method = 'lcmv';
end
try
cfg.(g.sourcemethod) = struct(g.modelparams{:});
catch, end
cfg.sourcemodel = oldLeadfield;
cfg.headmodel = vol.vol;
cfg.keeptrials = 'yes';
source = ft_sourceanalysis(cfg, dataPre); % compute the source
% reformat for ROI analysis below
source_voxel_data = reshape([ source.avg.mom{:} ], 3, size(source.avg.mom{1},2), length(source.avg.mom));
source_voxel_data = permute(source_voxel_data, [2 3 1]);
end
% number of ROIs in the Desikan-Killiany Atlas
nROI = length(cortex.Atlas.Scouts);
% ROI labels
labels = {cortex.Atlas.Scouts.Label};
% keep only the first nPCA strongest components for each ROI
clear tmpData
if strcmpi(g.roiactivity, 'on')
nfreq = fres + 1;
tmpData = reshape(source_voxel_data, EEG.pnts, EEG.trials*size(source_voxel_data,2)*size(source_voxel_data,3));
source_roi_data = [];
data_pnts = EEG.pnts;
% zero padding if necessary
if g.freqresolution ~= 0
required_zeros = g.freqresolution - fres;
if required_zeros < 0
error('Desired frequency resolution cannot be lower than the actual resolution of the signal.')
end
pad = zeros(required_zeros, size(tmpData, 2));
tmpData = cat(1,pad,tmpData,pad);
frqs = sfreqs(g.freqresolution, EEG.srate);
data_pnts = size(tmpData, 1);
nfreq = data_pnts/2 + 1;
end
source_roi_power = zeros(nfreq, nROI);
% compute power using the Welch method
if strcmpi(g.lowmemory, 'off')
% default version
[tmpWelch, ftmp] = pwelch(tmpData, data_pnts, floor(data_pnts/2), data_pnts, EEG.srate); % ftmp should be equal frqs
else
% version for less memory, can be very slow
warning('The sequential spectral estimation can take a very long time. It is recommended to run the faster version on a computer/cluster with enough memory.')
tmpWelch = zeros(nfreq, size(tmpData, 2));
fprintf('Progress of %d:', size(tmpData, 2));
for ivox = 1:size(tmpData, 2)
if mod(ivox, 10000) == 0
fprintf('%d', ivox);
elseif mod(ivox, 1000) == 0
fprintf('.');
end
[tmpWelch(:, ivox), ftmp] = pwelch(tmpData(:, ivox), data_pnts, floor(data_pnts/2), data_pnts, EEG.srate); % ftmp should be equal frqs
end
fprintf('\n');
end
tmpWelch = reshape(tmpWelch, size(tmpWelch,1), EEG.trials, size(source_voxel_data,2), size(source_voxel_data,3));
tmpWelch = squeeze(mean(tmpWelch,2)); % remove trials size freqs x voxels x 3
tmpWelch = squeeze(mean(tmpWelch,3)); % remove 3rd dim size freqs x voxels
source_voxel_power = tmpWelch;
% fooof settings
if strcmpi(g.fooof, 'on')
f_range = g.fooof_frange; % freq range where 1/f should be fitted
settings = struct(); % use defaults
slope = zeros(1, nROI);
PS_corrected = zeros(size(frqs, 1), size(frqs, 2), nROI);
end
source_roi_data = zeros(size(source_voxel_data,1), g.nPCA*nROI);
fprintf('Computing ROI activity:');
for iROI = 1:nROI
if mod(iROI, 5) == 0
fprintf('.');
end
ind_roi = cortex.Atlas.Scouts(iROI).Vertices;
[~, source_roi_power_norm(iROI)] = roi_getpower(source_voxel_data, ind_roi);
source_roi_power(:,iROI) = mean(tmpWelch(:, ind_roi),2); % shape: (101, nROI)
[source_roi_data_tmp, nPCA(iROI)] = roi_getact(source_voxel_data, ind_roi, g.nPCA);
source_roi_data(:, (iROI-1)*g.nPCA+1:iROI*g.nPCA) = source_roi_data_tmp;
if strcmpi(g.fooof, 'on')
ps1 = source_roi_power(:,iROI);
fooof_result = fooof(frqs, ps1, f_range, settings, true);
offset = fooof_result.aperiodic_params(1);
slope(iROI) = fooof_result.aperiodic_params(2);
y = (-slope(iROI) .* log10(frqs)) + offset;
PS_corrected(:,:,iROI) = 10*log10(ps1)-10*y;
end
end
fprintf('\n');
% version with nPCA components
source_roi_data = permute(reshape(source_roi_data, EEG.pnts, EEG.trials, []), [3 1 2]);
% source_roi_data = permute(reshape(source_roi_data, data_pnts, EEG.trials, []), [3 1 2]); % error when fres is chosen to be 400 Hz
else
source_roi_data = [];
source_roi_power = [];
source_roi_power_norm = [];
end
disp('Done');
% Output paramters
EEG.roi.cortex = cortex;
EEG.roi.atlas = cortex.Atlas.Scouts;
if strcmpi(g.exportvoxact, 'on')
EEG.roi.source_voxel_data = source_voxel_data; % large (takes lots of RAM)
end
EEG.roi.source_voxel_power = single(source_voxel_power);
EEG.roi.source_roi_data = single(source_roi_data);
EEG.roi.source_roi_power = source_roi_power; % used for plotting
EEG.roi.source_roi_power_norm = source_roi_power_norm; % used for cross-sprectum
EEG.roi.freqs = frqs;
EEG.roi.nPCA = g.nPCA;
EEG.roi.nROI = nROI;
EEG.roi.pnts = EEG.pnts;
EEG.roi.srate = EEG.srate;
EEG.roi.atlas = cortex.Atlas;
EEG.roi.srate = EEG.srate;
EEG.roi.leadfield = g.leadfield;
EEG.roi.headmodel = g.headmodel;
EEG.roi.parameters = varargin;
if exist('P_eloreta', 'var')
EEG.roi.P_eloreta = single(P_eloreta);
end
if strcmpi(g.fooof, 'on')
EEG.roi.fooof_results.slope = slope;
EEG.roi.fooof_results.offset = offset;
EEG.roi.fooof_results.PS = ps1;
EEG.roi.fooof_results.PS_corrected = squeeze(PS_corrected);
end
% get channel power for comparison
if strcmpi(g.channelpower, 'on')
tmpdata = permute(EEG.data(g.chansel, :, :), [2 1 3]); % pnts trials channels
tmpdata = reshape(tmpdata, size(tmpdata,1), size(tmpdata,2)*size(tmpdata,3));
[tmpWelch,ftmp] = pwelch(tmpdata, data_pnts, data_pnts/2, data_pnts, data_pnts/2); % ftmp should be equal frqs
tmpWelch = reshape(tmpWelch, size(tmpWelch,1), EEG.nbchan, EEG.trials);
tmpWelch = squeeze(mean(tmpWelch,3)); % remove trials size freqs x voxels x 3
EEG.roi.channel_power = tmpWelch;
end