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ft_sourceparcellate.m
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ft_sourceparcellate.m
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function [parcel] = ft_sourceparcellate(cfg, source, parcellation)
% FT_SOURCEPARCELLATE combines the source-reconstruction parameters over the parcels, for
% example by averaging all the values in the anatomically or functionally labeled parcel.
%
% Use as
% output = ft_sourceparcellate(cfg, source, parcellation)
% where the input source is a 2D surface-based or 3-D voxel-based source grid that was for
% example obtained from FT_SOURCEANALYSIS or FT_COMPUTE_LEADFIELD. The input parcellation is
% described in detail in FT_DATATYPE_PARCELLATION (2-D) or FT_DATATYPE_SEGMENTATION (3-D) and
% can be obtained from FT_READ_ATLAS or from a custom parcellation/segmentation for your
% individual subject. The output is a channel-based representation with the combined (e.g.
% averaged) representation of the source parameters per parcel.
%
% The configuration "cfg" is a structure that can contain the following fields
% cfg.method = string, method to combine the values, see below (default = 'mean')
% cfg.parcellation = string, fieldname that contains the desired parcellation
% cfg.parameter = cell-array with strings, fields that should be parcellated (default = 'all')
%
% The values within a parcel or parcel-combination can be combined with different methods:
% 'mean' compute the mean
% 'median' compute the median (unsupported for fields that are represented in a cell-array)
% 'eig' compute the largest eigenvector
% 'min' take the minimal value
% 'max' take the maximal value
% 'maxabs' take the signed maxabs value
% 'std' take the standard deviation
%
% See also FT_SOURCEANALYSIS, FT_DATATYPE_PARCELLATION, FT_DATATYPE_SEGMENTATION
% Copyright (C) 2012-2021, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
% FieldTrip 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, either version 3 of the License, or
% (at your option) any later version.
%
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id$
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_defaults
ft_preamble init
ft_preamble debug
ft_preamble loadvar source parcellation
ft_preamble provenance source parcellation
% the ft_abort variable is set to true or false in ft_preamble_init
if ft_abort
return
end
% get the defaults
cfg.parcellation = ft_getopt(cfg, 'parcellation');
cfg.parameter = ft_getopt(cfg, 'parameter', 'all');
cfg.method = ft_getopt(cfg, 'method', 'mean'); % can be mean, min, max, svd
cfg.feedback = ft_getopt(cfg, 'feedback', 'text');
% the data can be passed as input argument or can be read from disk
hasparcellation = exist('parcellation', 'var');
if ischar(cfg.parameter)
cfg.parameter = {cfg.parameter};
end
if hasparcellation
% the parcellation is specified as separate structure
else
% the parcellation is represented in the source structure itself
parcellation = source;
end
% keep the transformation matrix
if isfield(parcellation, 'transform')
transform = parcellation.transform;
else
transform = [];
end
% ensure it is a parcellation, not a segmentation
parcellation = ft_checkdata(parcellation, 'datatype', 'parcellation', 'parcellationstyle', 'indexed', 'hasunit', 'yes');
% keep the transformation matrix
if ~isempty(transform)
parcellation.transform = transform;
end
% ensure it is a source, not a volume
source = ft_checkdata(source, 'datatype', 'source', 'insidestyle', 'logical', 'hasunit', 'yes');
% ensure that the source and the parcellation are anatomically consistent
if ~strcmp(source.unit, parcellation.unit)
ft_error('the units of the source and parcellation structure are not consistent, please use FT_SOURCEINTERPOLATE');
end
% ensure that the source and the parcellation are anatomically consistent
tolerance = 0.1 * ft_scalingfactor('mm', source.unit);
if ~isalmostequal(source.pos, parcellation.pos, 'abstol', tolerance)
ft_error('the positions of the source and parcellation structure are not consistent, please use FT_SOURCEINTERPOLATE');
end
if isempty(cfg.parcellation)
% determine the first field that can be used for the parcellation
fn = fieldnames(parcellation);
for i=1:numel(fn)
if isfield(parcellation, [fn{i} 'label'])
ft_warning('using "%s" for the parcellation', fn{i});
cfg.parcellation = fn{i};
break
end
end
end
if isempty(cfg.parcellation)
ft_error('you should specify the field containing the parcellation');
end
% determine the fields and corresponding dimords to work on
fn = fieldnames(source);
fn = setdiff(fn, {'pos', 'tri', 'dim', 'transform', 'unit', 'coordsys', 'inside', 'time', 'freq', 'cfg', 'hdr'}); % remove fields that do not represent the data
fn = fn(cellfun(@isempty, regexp(fn, 'dimord'))); % remove dimord fields
fn = fn(cellfun(@isempty, regexp(fn, 'label'))); % remove label fields
dimord = cell(size(fn));
for i=1:numel(fn)
dimord{i} = getdimord(source, fn{i});
end
if any(strcmp(cfg.parameter, 'all'))
cfg.parameter = fn;
else
[inside, i1, i2] = intersect(cfg.parameter, fn);
[outside ] = setdiff(cfg.parameter, fn);
if ~isempty(outside)
ft_warning('\nparameter "%s" cannot be parcellated', outside{:});
end
cfg.parameter = fn(i2);
fn = fn(i2);
dimord = dimord(i2);
end
% although it is technically feasible, don't parcellate the parcellation itself
sel = ~strcmp(cfg.parcellation, fn);
fn = fn(sel);
dimord = dimord(sel);
if numel(fn)==0
ft_error('there are no source parameters that can be parcellated');
end
% get the parcellation and the labels that go with it
tissue = parcellation.(cfg.parcellation);
tissuelabel = parcellation.([cfg.parcellation 'label']);
ntissue = length(tissuelabel);
if isfield(source, 'inside')
% determine the conjunction of the parcellation and the inside source points
n0 = numel(source.inside);
n1 = sum(source.inside(:));
n2 = sum(tissue(:)~=0);
fprintf('there are in total %d positions, %d positions are inside the brain, %d positions have a label\n', n0, n1, n2);
fprintf('%d of the positions inside the brain have a label\n', sum(tissue(source.inside)~=0));
fprintf('%d of the labeled positions are inside the brain\n', sum(source.inside(tissue(:)~=0)));
fprintf('%d of the positions inside the brain do not have a label\n', sum(tissue(source.inside)==0));
% discard the positions outside the brain and the positions in the brain that do not have a label
tissue(~source.inside) = 0;
end
% start preparing the output data structure
parcel = keepfields(source, {'freq','time','cumtapcnt'});
parcel.label = tissuelabel;
for i=1:numel(fn)
% parcellate each of the desired parameters
dat = source.(fn{i});
siz = getdimsiz(source, fn{i});
siz(contains(tokenize(dimord{i},'_'),'pos')) = ntissue;
if startsWith(dimord{i}, '{pos_pos}')
fprintf('creating %d*%d parcel combinations for parameter %s by taking the %s\n', numel(tissuelabel), numel(tissuelabel), fn{i}, cfg.method);
tmp = zeros(siz);
ft_progress('init', cfg.feedback, 'computing parcellation');
k = 0;
K = numel(tissuelabel)^2;
for j1=1:numel(tissuelabel)
for j2=1:numel(tissuelabel)
k = k + 1;
if ~any(tissue==j1) || ~any(tissue==j2)
ft_progress(k/K, 'skipping parcellation for %s combined with %s', tissuelabel{j1}, tissuelabel{j2});
else
ft_progress(k/K, 'computing parcellation for %s combined with %s', tissuelabel{j1}, tissuelabel{j2});
switch cfg.method
case 'mean'
tmp(j1,j2,:,:) = cellmean(dat(tissue==j1,tissue==j2));
case 'median'
tmp(j1,j2,:,:) = cellmedian(dat(tissue==j1,tissue==j2));
case 'min'
tmp(j1,j2,:,:) = cellmin(dat(tissue==j1,tissue==j2));
case 'max'
tmp(j1,j2,:,:) = cellmax(dat(tissue==j1,tissue==j2));
case 'eig'
tmp(j1,j2,:,:) = celleig(dat(tissue==j1,tissue==j2));
case 'std'
tmp(j1,j2,:,:) = cellstd(dat(tissue==j1,tissue==j2));
otherwise
ft_error('method %s not implemented for %s', cfg.method, dimord{i});
end % switch
end % if
end % for j2
end % for j1
ft_progress('close');
elseif startsWith(dimord{i}, '{pos}')
fprintf('creating %d parcels for parameter %s by taking the %s\n', numel(tissuelabel), fn{i}, cfg.method);
tmp = zeros(siz);
ft_progress('init', cfg.feedback, 'computing parcellation');
for j=1:numel(tissuelabel)
if ~any(tissue==j)
ft_progress(j/numel(tissuelabel), 'skipping parcellation for %s', tissuelabel{j});
else
ft_progress(j/numel(tissuelabel), 'computing parcellation for %s', tissuelabel{j});
switch cfg.method
case 'mean'
tmp(j,:,:) = cellmean(dat(tissue==j));
case 'median'
tmp(j,:,:) = cellmedian(dat(tissue==j));
case 'min'
tmp(j,:,:) = cellmin(dat(tissue==j));
case 'max'
tmp(j,:,:) = cellmax(dat(tissue==j));
case 'eig'
tmp(j,:,:) = celleig(dat(tissue==j));
case 'std'
tmp(j,:,:) = cellstd(dat(tissue==j));
otherwise
ft_error('method %s not implemented for %s', cfg.method, dimord{i});
end % switch
end % if
end % for
ft_progress('close');
elseif startsWith(dimord{i}, 'pos_pos')
fprintf('creating %d*%d parcel combinations for parameter %s by taking the %s\n', numel(tissuelabel), numel(tissuelabel), fn{i}, cfg.method);
siz = size(dat);
siz(1) = ntissue;
siz(2) = ntissue;
tmp = nan(siz);
ft_progress('init', cfg.feedback, 'computing parcellation');
k = 0;
K = numel(tissuelabel)^2;
for j1=1:numel(tissuelabel)
for j2=1:numel(tissuelabel)
k = k + 1;
if ~any(tissue==j1) || ~any(tissue==j2)
ft_progress(k/K, 'skipping parcellation for %s combined with %s', tissuelabel{j1}, tissuelabel{j2});
else
ft_progress(k/K, 'computing parcellation for %s combined with %s', tissuelabel{j1}, tissuelabel{j2});
switch cfg.method
case 'mean'
tmp(j1,j2,:) = arraymean2(dat(tissue==j1,tissue==j2,:));
case 'median'
tmp(j1,j2,:) = arraymedian2(dat(tissue==j1,tissue==j2,:));
case 'min'
tmp(j1,j2,:) = arraymin2(dat(tissue==j1,tissue==j2,:));
case 'max'
tmp(j1,j2,:) = arraymax2(dat(tissue==j1,tissue==j2,:));
case 'eig'
tmp(j1,j2,:) = arrayeig2(dat(tissue==j1,tissue==j2,:));
case 'maxabs'
tmp(j1,j2,:) = arraymaxabs2(dat(tissue==j1,tissue==j2,:));
case 'std'
tmp(j1,j2,:) = arraystd2(dat(tissue==j1,tissue==j2,:));
otherwise
ft_error('method %s not implemented for %s', cfg.method, dimord{i});
end % switch
end % if
end % for j2
end % for j1
ft_progress('close');
elseif startsWith(dimord{i}, 'pos')
fprintf('creating %d parcels for %s by taking the %s\n', numel(tissuelabel), fn{i}, cfg.method);
siz = size(dat);
siz(1) = ntissue;
tmp = nan(siz);
ft_progress('init', cfg.feedback, 'computing parcellation');
for j=1:numel(tissuelabel)
if ~any(tissue==j)
ft_progress(j/numel(tissuelabel), 'skipping parcellation for %s', tissuelabel{j});
else
ft_progress(j/numel(tissuelabel), 'computing parcellation for %s', tissuelabel{j});
switch cfg.method
case 'mean'
tmp(j,:) = arraymean1(dat(tissue==j,:));
case 'mean_thresholded'
cfg.mean = ft_getopt(cfg, 'mean', struct('threshold', []));
if isempty(cfg.mean.threshold)
ft_error('when cfg.method = ''mean_thresholded'', you should specify a cfg.mean.threshold');
end
if numel(cfg.mean.threshold)==size(dat,1)
% assume one threshold per vertex
threshold = cfg.mean.threshold(tissue==j,:);
else
threshold = cfg.mean.threshold;
end
tmp(j,:) = arraymean1(dat(tissue==j,:), threshold);
case 'median'
tmp(j,:) = arraymedian1(dat(tissue==j,:));
case 'min'
tmp(j,:) = arraymin1(dat(tissue==j,:));
case 'max'
tmp(j,:) = arraymax1(dat(tissue==j,:));
case 'maxabs'
tmp(j,:) = arraymaxabs1(dat(tissue==j,:));
case 'eig'
tmp(j,:) = arrayeig1(dat(tissue==j,:));
case 'std'
tmp(j,:) = arraystd1(dat(tissue==j,:));
otherwise
ft_error('method %s not implemented for %s', cfg.method, dimord{i});
end % switch
end % if
end % for
ft_progress('close');
else
ft_error('unsupported dimord %s', dimord{i})
end % if pos, pos_pos, {pos}, etc.
% update the dimord, use chan rather than pos
% this makes it look just like timelock or freq data
tok = tokenize(dimord{i}, '_');
tok(strcmp(tok, 'pos' )) = {'chan'}; % replace pos by chan
tok(strcmp(tok, '{pos}')) = {'chan'}; % replace pos by chan
tok(strcmp(tok, '{pos')) = {'chan'}; % replace pos by chan
tok(strcmp(tok, 'pos}')) = {'chan'}; % replace pos by chan
% squeeze out any singleton oris
siz = [size(tmp) 1]; % add trailing singleton to be sure
oris = contains(tok, 'ori') & siz(1:numel(tok))==1;
siz(oris) = [];
tmp = reshape(tmp, siz);
tok(oris) = [];
tmpdimord = sprintf('%s_', tok{:});
tmpdimord = tmpdimord(1:end-1); % exclude the last _
% store the results in the output structure
parcel.(fn{i}) = tmp;
parcel.([fn{i} 'dimord']) = tmpdimord;
% to avoid confusion
clear dat tmp tmpdimord j j1 j2
end % for each of the fields that should be parcellated
% a brainordinate is a brain location that is specified by either a surface vertex (node) or a volume voxel
parcel.brainordinate = keepfields(parcellation, {'pos', 'tri', 'dim', 'transform', 'unit', 'coordsys'}); % keep the information about the geometry
fn = fieldnames(parcellation);
for i=1:numel(fn)
if isfield(parcellation, [fn{i} 'label'])
% keep each of the labeled fields from the parcellation
parcel.brainordinate.( fn{i} ) = parcellation.( fn{i} );
parcel.brainordinate.([fn{i} 'label']) = parcellation.([fn{i} 'label']);
end
end
% do the general cleanup and bookkeeping at the end of the function
ft_postamble debug
ft_postamble previous source parcellation
ft_postamble provenance parcel
ft_postamble history parcel
ft_postamble savevar parcel
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTIONS to compute something over the first dimension
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y = arraymean1(x, threshold)
if nargin==1
y = mean(x,1);
else
if numel(threshold)==1
% scalar comparison is possible
elseif size(threshold,1) == size(x,1)
% assume threshold to be column vector
threshold = repmat(threshold, [1, size(x,2)]);
end
sel = sum(x>threshold,2);
if ~isempty(sel)
y = mean(x(sel>0,:),1);
else
y = nan+zeros(1,size(x,2));
end
end
function y = arraymedian1(x)
if ~isempty(x)
y = median(x,1);
else
y = nan(1,size(x,2));
end
function y = arraymin1(x)
if ~isempty(x)
y = min(x,[], 1);
else
y = nan(1,size(x,2));
end
function y = arraymax1(x)
if ~isempty(x)
y = max(x,[], 1);
else
y = nan(1,size(x,2));
end
function y = arrayeig1(x)
if ~isempty(x)
siz = size(x);
x = reshape(x, siz(1), prod(siz(2:end)));
[u, s, v] = svds(x, 1); % x = u * s * v'
y = s(1,1) * v(:,1); % retain the largest eigenvector with appropriate scaling
y = reshape(y, [siz(2:end) 1]); % size should have at least two elements
else
siz = size(x);
y = nan([siz(2:end) 1]);
end
function y = arraymaxabs1(x)
if ~isempty(x)
% take the value that is at max(abs(x))
[dum,ix] = max(abs(x), [], 1);
y = x(ix);
else
y = nan(1,size(x,2));
end
function y = arraystd1(x)
if ~isempty(x)
y = std(x,0, 1);
else
y = nan(1,size(x, 2));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTIONS to compute something over the first two dimensions
% all of these functions should be implemented the same
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y = arraymean2(x)
siz = size(x);
x = reshape(x, [siz(1)*siz(2) siz(3:end) 1]); % simplify it into a single dimension
y = arraymean1(x);
function y = arraymedian2(x)
siz = size(x);
x = reshape(x, [siz(1)*siz(2) siz(3:end) 1]); % simplify it into a single dimension
y = arraymedian1(x);
function y = arraymin2(x)
siz = size(x);
x = reshape(x, [siz(1)*siz(2) siz(3:end) 1]); % simplify it into a single dimension
y = arraymin1(x);
function y = arraymax2(x)
siz = size(x);
x = reshape(x, [siz(1)*siz(2) siz(3:end) 1]); % simplify it into a single dimension
y = arraymax1(x);
function y = arrayeig2(x)
siz = size(x);
x = reshape(x, [siz(1)*siz(2) siz(3:end) 1]); % simplify it into a single dimension
y = arrayeig1(x);
function y = arraymaxabs2(x)
siz = size(x);
x = reshape(x, [siz(1)*siz(2) siz(3:end) 1]); % simplify it into a single dimension
y = arraymaxabs1(x);
function y = arraystd2(x)
siz = size(x);
x = reshape(x, [siz(1)*siz(2) siz(3:end) 1]); % simplify it into a single dimension
y = arraystd1(x);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTIONS for doing something over all elements of a cell-array
% add a singleton dimension, concatenate into an array, and do the computatioon
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y = cellmean(x)
siz = size(x{1});
for i=1:numel(x)
x{i} = reshape(x{i}, [1 siz]);
end
x = cat(1, x{:});
y = arraymean1(x);
function y = cellmedian(x)
siz = size(x{1});
for i=1:numel(x)
x{i} = reshape(x{i}, [1 siz]);
end
x = cat(1, x{:});
y = arraymedian1(x);
function y = cellmin(x)
siz = size(x{1});
for i=1:numel(x)
x{i} = reshape(x{i}, [1 siz]);
end
x = cat(1, x{:});
y = arraymin1(x);
function y = cellmax(x)
siz = size(x{1});
for i=1:numel(x)
x{i} = reshape(x{i}, [1 siz]);
end
x = cat(1, x{:});
y = arraymax1(x);
function y = celleig(x)
siz = size(x{1});
for i=1:numel(x)
x{i} = reshape(x{i}, [1 siz]);
end
x = cat(1, x{:});
y = arrayeig1(x);
function y = cellstd(x)
siz = size(x{1});
for i=1:numel(x)
x{i} = reshape(x{i}, [1 siz]);
end
x = cat(1, x{:});
y = arraystd1(x);