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swe_cp.m
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swe_cp.m
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function swe_cp(SwE)
% This function computes covariance and beta maps for parametric analyses.
% =========================================================================
% For a parametric SwE analysis with nifti input, this function computes
% the following maps:
%
% - swe_vox_mask: The mask image for the analysis.
% - swe_vox_con_c{c#}: The contrast map for contrast {c#}
% - swe_vox_cov_b{b1#}_b{b2#}: The covariance map between betas {b1#}
% and {b2#}.
% - swe_vox_cov_g{g#}_b{b1#}_b{b2#}: The covariance map between betas
% {b1#} and {b2#} for group {g#}.
% - swe_vox_cov_g{g#}_v{v1#}_v{v2#}: The covariance map between betas
% {v1#} and {v2#} for group {g#}.
%
% For a parametric SwE analysis with GIfTI or CIfTI inputs, this function computes
% the following maps:
%
% - swe_dpx_mask: The mask image for the analysis.
% - swe_dpx_con_c{c#}: The contrast map for contrast {c#}
% - swe_dpx_cov_b{b1#}_b{b2#}: The covariance map between betas {b1#}
% and {b2#}.
% - swe_dpx_cov_g{g#}_b{b1#}_b{b2#}: The covariance map between betas
% {b1#} and {b2#} for group {g#}.
% - swe_dpx_cov_g{g#}_v{v1#}_v{v2#}: The covariance map between betas
% {v1#} and {v2#} for group {g#}.
%
% For a parametric SwE analysis with '.mat' input, this function computes
% the following analagous maps:
%
% - swe_dat_mask: The mask image for the analysis.
% - swe_dat_beta_b: The beta map.
% - swe_dat_con_c: The contrast map for each contrast.
% - swe_dat_cov_bb: The between-betas covariance map.
% - swe_dat_cov_g_bb: The groupwise between-betas covariance maps.
% - swe_dat_cov_g_vv: The visitwise between-betas covariance maps.
%
% For non-parametric SwE analyses, the function `swe_cp_WB` is called
% instead as these maps must be computed differently. See the header of
% `swe_cp_WB` for more information.
% =========================================================================
% FORMAT swe_cp(SwE)
% -------------------------------------------------------------------------
% Inputs:
% - SwE: SwE data structure
% =========================================================================
% Version Info: $Format:%ci$ $Format:%h$
%-Say hello
%--------------------------------------------------------------------------
Finter = spm('CreateIntWin','off');
set(Finter,'name','SwE estimation');
set(Finter,'vis','on')
%-Get SwE.mat[s] if necessary
%--------------------------------------------------------------------------
if nargin == 0
P = cellstr(spm_select(Inf,'^SwE\.mat$','Select SwE.mat[s]'));
for i = 1:length(P)
swd = fileparts(P{i});
load(fullfile(swd,'SwE.mat'));
SwE.swd = swd;
% detect if this is a WB analysis or a "standard analysis"
if isfield(SwE, 'WB')
swe_cp_WB(SwE);
else
swe_cp(SwE);
end
end
return
end
% If this is a WB analysis we need to use swe_cp_WB.
if isfield(SwE, 'WB')
swe_cp_WB(SwE);
return
end
%-Change to SwE.swd if specified
%--------------------------------------------------------------------------
try
cd(SwE.swd);
catch %#ok<*CTCH>
SwE.swd = pwd;
end
%-Ensure data are assigned
%--------------------------------------------------------------------------
try
SwE.xY.VY;
catch
spm('alert!','Please assign data to this design', mfilename);
spm('FigName','Stats: done',Finter); spm('Pointer','Arrow')
return
end
%-Check if we have data in a.mat format and set some variables accordingly
%--------------------------------------------------------------------------
file_ext = swe_get_file_extension(SwE.xY.P{1});
isMat = strcmpi(file_ext,'.mat');
isCifti = strcmpi(file_ext,'.dtseries.nii') || strcmpi(file_ext,'.dscalar.nii');
isOctave = exist('OCTAVE_VERSION','builtin');
if isCifti
metadata = {'ciftiTemplate', SwE.xY.P{1}};
file_data_type = 'dpx';
end
if isMat
file_data_type = 'dat';
end
if ~isMat && ~isCifti
isMeshData = spm_mesh_detect(SwE.xY.VY);
if isMeshData
file_ext = '.gii';
file_data_type = 'dpx';
g = SwE.xY.VY(1).private;
metadata = g.private.metadata;
name = {metadata.name};
if any(ismember(name,'SurfaceID'))
metadata = metadata(ismember(name,'SurfaceID'));
metadata = {metadata.name, metadata.value};
elseif isfield(g,'faces') && ~isempty(g.faces)
metadata = {'SurfaceID', SwE.xY.VY(1).fname};
else
error('SurfaceID not found in GIfTI''s metadata.');
end
if isempty(spm_file(metadata{2},'path'))
metadata{2} = fullfile(spm_file(SwE.xY.VY(1).fname,'path'),metadata{2});
end
SwE.xVol.G = metadata{2};
else
file_ext = spm_file_ext;
file_data_type = 'vox';
metadata = {};
end
end
%-Delete files from previous analyses
%--------------------------------------------------------------------------
if exist(fullfile(SwE.swd,sprintf('swe_%s_mask%s',file_data_type,file_ext)),'file') == 2
str = {'Current directory contains SwE estimation files:',...
'pwd = ',SwE.swd,...
'Existing results will be overwritten!'};
if spm_input(str,1,'bd','stop|continue',[1,0],1)
spm('FigName','Stats: done',Finter); spm('Pointer','Arrow')
return
else
warning('Overwriting old results\n\t (pwd = %s) ',SwE.swd); %#ok<WNTAG>
end
end
files = {'^swe_.{3}_mask(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_cov_b\d{2}_b\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_cov_vv(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_cov(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_con_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_beta_b\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_cov_g\d{2}_v\d{2}_v\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_cov_g\d{2}_b\d{2}_b\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_edf_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_beta_\w{1}\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_\w{1,2}stat_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_\w{1,2}stat-WB_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_\w{1,2}stat_lp\w{0,3}_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_\w{1,2}stat_lp\w{0,3}-WB_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_clustere_\w{1,2}stat_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_clustere_\w{1,2}stat_lp\w{0,3}_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_clustere_\w{1,2}stat_lp\w{0,3}-WB_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_clusternorm\d{0,1}_\w{1,2}stat_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_clusternorm\d{0,1}_\w{1,2}stat_lp\w{0,3}-WB_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_resid_y\d{2,4}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^swe_.{3}_fit_y\d{2,4}(\.dtseries)?(\.dscalar)?\..{3}$'};
for i = 1:length(files)
j = spm_select('List',SwE.swd,files{i});
for k = 1:size(j,1)
spm_unlink(deblank(j(k,:)));
end
end
%==========================================================================
% - A N A L Y S I S P R E L I M I N A R I E S
%==========================================================================
%-Initialise
%==========================================================================
fprintf('%-40s: %30s','Initialising parameters','...computing'); %-#
xX = SwE.xX;
[nScan, nBeta] = size(xX.X);
nCov_beta = (nBeta+1)*nBeta/2;
pX = pinv(xX.X); % pseudo-inverse
Hat = xX.X*(pX); % Hat matrix
iSubj = SwE.Subj.iSubj;
uSubj = unique(iSubj);
nSubj = length(uSubj);
%-residual correction
%
switch SwE.SS
case 0
corr = ones(nScan,1);
case 1
corr = sqrt(nScan/(nScan-nBeta)); % residual correction (type 1)
case 2
corr = (1-diag(Hat)).^(-0.5); % residual correction (type 2)
case 3
corr = (1-diag(Hat)).^(-1); % residual correction (type 3)
case 4
corr = cell(nSubj,1);
I_Hat = eye(nScan) - Hat;
for i = 1:nSubj
tmp = I_Hat(iSubj==uSubj(i), iSubj==uSubj(i));
tmp = (tmp + tmp')/2;
[tmpV, tmpE] = eig(tmp);
corr{i} = tmpV * diag(1./sqrt(diag(tmpE))) * tmpV';
end
clear I_Hat tmp
case 5
corr = cell(nSubj,1);
I_Hat = eye(nScan) - Hat;
for i = 1:nSubj
tmp = I_Hat(iSubj==uSubj(i), iSubj==uSubj(i));
tmp = (tmp + tmp')/2;
corr{i} = inv(tmp);
end
clear I_Hat tmp
end
%-detect if the design matrix is separable (a little bit messy, but seems to do the job)
%
iGr_dof = zeros(1,nScan);
iBeta_dof = zeros(1,nBeta);
it = 0;
while ~all(iGr_dof)
it = it + 1;
scan = find(iGr_dof==0,1);
if any(xX.X(scan,:)) % handle the case where a row is all 0s (BG - 05/08/2016; Thanks to Ged Ridgway for finding the bug)
for i = find(iGr_dof==0)
if any((xX.X(i,:) & xX.X(scan,:)))
iGr_dof(i) = it;
end
end
else
iGr_dof(scan) = it;
end
end
%need to check if the partition is correct
while 1
uGr_dof = unique(iGr_dof);
nGr_dof = length(uGr_dof);
tmp = zeros(nGr_dof,nBeta);
for i = 1:nGr_dof
tmp(i,:) = any(xX.X(iGr_dof==uGr_dof(i),:));
end
if nGr_dof==1 | all(sum(tmp,1)==1) %#ok<OR2>
break % all is ok, just stop the while
else
ind1 = find(sum(tmp,1)>1,1); % detect the first column in common
ind2 = find(tmp(:,ind1)==1); % detect the groups to be fused
for ii = ind2'
iGr_dof(iGr_dof==uGr_dof(ii)) = ind2(1); % fuse the groups
end
end
end
nSubj_dof = zeros(1,nGr_dof);
for i = 1:nGr_dof % renumber to avoid gaps in the numbering
iGr_dof(iGr_dof==uGr_dof(i)) = i;
iBeta_dof(tmp(i,:)==1) = i;
nSubj_dof(i) = length(unique(iSubj(iGr_dof==uGr_dof(i))));
end
pB_dof = zeros(1,nGr_dof);
for i=1:nBeta
tmp=1;
for ii=1:nSubj
if length(unique(xX.X(iSubj==uSubj(ii)&iGr_dof'==iBeta_dof(i),i)))>1
tmp=0;
break
end
end
if tmp == 1
pB_dof(iBeta_dof(i)) = pB_dof(iBeta_dof(i)) + 1;
end
end
%-effective dof for each subject
edof_Subj = zeros(1,nSubj);
for i = 1:nSubj
edof_Subj(i) = 1 - pB_dof(iGr_dof(iSubj==uSubj(i)))/...
nSubj_dof(iGr_dof(iSubj==uSubj(i)));
end
%-degrees of freedom estimation type
if isfield(SwE.type,'modified')
dof_type = SwE.type.modified.dof_mo;
else
dof_type = SwE.type.classic.dof_cl;
end
if dof_type == 0 % so naive estimation is used
dof_cov = zeros(1,nBeta);
for i = 1:nBeta
dof_cov(i) = nSubj_dof(iBeta_dof(i)) - ...
pB_dof(iBeta_dof(i));
end
end
%-preprocessing for the modified SwE
if isfield(SwE.type,'modified')
iVis = SwE.Vis.iVis;
iGr = SwE.Gr.iGr;
uGr = unique(iGr);
nGr = length(uGr);
% info specific for each group
uVis_g = cell(1,nGr); % unique visits for each group
nVis_g = zeros(1,nGr); % number of visits for each group
uSubj_g = cell(1,nGr); % unique visits for each group
nSubj_g = zeros(1,nGr); % number of visits for each group
for g = 1:nGr
uVis_g{g} = unique(iVis(iGr==uGr(g)));
nVis_g(g) = length(uVis_g{g});
iSubj_g = iSubj(iGr==uGr(g)); % Subject number for each subject in group for each visit
uSubj_g{g} = unique(iSubj_g); % Unique subject numbers of subjects in group
nSubj_g(g) = length(uSubj_g{g});
uSubj_g_tmp = uSubj_g{g};
for k = 1:nSubj_g(g)
% The number of visits for subject uSubj_g(k)
vis_g_subj(k) = sum(iSubj_g==uSubj_g_tmp(k));
end
max_nVis_g(g) = max(vis_g_subj);
min_nVis_g(g) = min(vis_g_subj);
clear vis_g_subj
end
nCov_vis_g = nVis_g.*(nVis_g+1)/2; % number of covariance elements to be estimated for each group
nCov_vis = sum(nCov_vis_g); % total number of covariance elements to be estimated
% Flags matrices indicating which residuals have to be used for each covariance element
Flagk = false(nCov_vis,nScan); % Flag indicating scans corresponding to visit k for each covariance element
Flagkk = false(nCov_vis,nScan); % Flag indicating scans corresponding to visit kk for each covariance element
Ind_Cov_vis_diag = nan(1,sum(nVis_g)); % index of the diagonal elements
Ind_Cov_vis_off_diag = nan(1,nCov_vis - sum(nVis_g)); % index of the off-diagonal elements
Ind_corr_diag=nan(nCov_vis,2); % index of the 2 corresponding diagonal elements
iGr_Cov_vis_g = nan(1,nCov_vis);
it = 0; it2 = 0; it3 = 0;
for g = 1:nGr
for k = 1:nVis_g(g)
for kk = k:nVis_g(g)
it = it + 1;
id = intersect(iSubj(iGr==uGr(g) & iVis==uVis_g{g}(k)),...
iSubj(iGr==uGr(g) & iVis==uVis_g{g}(kk))); % identifiaction of the subjects with both visits k & kk
Flagk(it,:) = ismember(iSubj,id) & iVis==uVis_g{g}(k);
Flagkk(it,:) = ismember(iSubj,id) & iVis==uVis_g{g}(kk);
if k==kk
it2 = it2+1;
it4 = it2;
Ind_Cov_vis_diag(it2) = it;
else
it3 = it3 + 1;
it4 = it4 + 1;
Ind_Cov_vis_off_diag(it3) = it;
end
Ind_corr_diag(it,:) = [it2 it4];
iGr_Cov_vis_g(it) = g;
end
end
end
% weights for the vectorised SwE (to be checked)
weight=NaN(nCov_beta,nCov_vis);
it=0;
for j = 1:nBeta
for jj = j:nBeta
it=it+1;
for jjj = Ind_Cov_vis_diag
weight(it,jjj) = pX(j,Flagk(jjj,:))*pX(jj,Flagk(jjj,:))';
end
for jjj = Ind_Cov_vis_off_diag
weight(it,jjj) = pX(j,Flagk(jjj,:))*pX(jj,Flagkk(jjj,:))' + ...
pX(j,Flagkk(jjj,:))*pX(jj,Flagk(jjj,:))';
end
end
end
%-compute the effective dof from each homogeneous group if dof_type
switch dof_type
case 1
edof_Gr = zeros(1,nGr);
nSubj_g = zeros(1,nGr);
for g = 1:nGr
nSubj_g(g) = length(unique(iSubj(iGr == g)));
tmp = 0;
for j = 1:nSubj_g(g)
tmp = tmp + 1/edof_Subj(uSubj == uSubj_g{g}(j));
end
edof_Gr(g) = nSubj_g(g)^2/tmp;
end
case {2,3} % compute a matrix containing the variables linked to the degrees of freedom (for test II and III)
dofMat = cell(nGr,1);
for g = 1:nGr
dofMat{g} = zeros(nCov_vis_g(g));
it1 =0;
for i = 1:nVis_g(g)
for j = i:nVis_g(g)
it1 = it1 + 1;
it2 = 0;
for a = 1:nVis_g(g)
for b = a:nVis_g(g)
it2 = it2 + 1;
mij = 0;mab = 0;tmp = 0;
for ii = 1:nSubj_g(g)
mij = mij + 1*(...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(i)) &...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(j)));
mab = mab + 1*(...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(a)) &...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(b)));
tmp = tmp + 1*(...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(a)) &...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(b)) &...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(i)) &...
any(iSubj==uSubj_g{g}(ii) & iVis==uVis_g{g}(j)))...
/edof_Subj(uSubj==uSubj_g{g}(ii));
end
dofMat{g}(it1,it2) = tmp/mij/mab;
end
end
end
end
dofMat{g}(isnan(dofMat{g})) = 0;
end
clear tmp mij mab
end
end
%-preprocessing for the classic SwE
if isfield(SwE.type,'classic')
nVis_i = zeros(1,nSubj);
for i = 1:nSubj
nVis_i(i) = sum(uSubj(i)==iSubj);
end
nCov_vis = sum(nVis_i.*(nVis_i+1)/2); % total number of covariance elements to be estimated
weight = NaN(nCov_beta,nCov_vis);
Ind_Cov_vis_classic = NaN(1,nCov_vis);
Indexk = NaN(1,nCov_vis);
Indexkk = NaN(1,nCov_vis);
it = 0;
for j = 1:nBeta
for jj = j:nBeta
it = it + 1;
it2 = 0;
for i = 1:nSubj
ind_i=find(iSubj == uSubj(i));
for ii = 1:nVis_i(i)
it2 = it2 + 1;
weight(it,it2) = pX(j,ind_i(ii))*pX(jj,ind_i(ii));
Ind_Cov_vis_classic(it2) = i;
Indexk(it2) = ind_i(ii);
Indexkk(it2) = ind_i(ii);
for iii = (ii+1):nVis_i(i)
it2 = it2 + 1;
weight(it,it2) = pX(j,ind_i([ii,iii]))*pX(jj,ind_i([iii,ii]))';
Ind_Cov_vis_classic(it2) = i;
Indexk(it2) = ind_i(ii);
Indexkk(it2) = ind_i(iii);
end
end
end
end
end
%-compute the effective dof from each homogeneous group (here, subject)
if dof_type == 1
edof_Gr = edof_Subj;
end
end
%-If xM is not a structure then assume it's a vector of thresholds
%--------------------------------------------------------------------------
try
xM = SwE.xM;
catch
xM = -Inf(nScan,1);
end
if ~isstruct(xM)
xM = struct('T', [],...
'TH', xM,...
'I', 0,...
'VM', {[]},...
'xs', struct('Masking','analysis threshold'));
end
fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...done'); %-#
if ~isMat
%-Image dimensions and data
%==========================================================================
VY = SwE.xY.VY;
spm_check_orientations(VY);
% check files exists and try pwd
%--------------------------------------------------------------------------
for i = 1:numel(VY)
if ~spm_existfile(VY(i).fname)
[~,n,e] = fileparts(VY(i).fname);
VY(i).fname = [n,e];
end
end
M = VY(1).mat;
DIM = VY(1).dim;
% check how the data image treat 0 (as NaN or not)
YNaNrep = VY(1).dt(2);
%-Maximum number of residual images for smoothness estimation
%--------------------------------------------------------------------------
% MAXRES = Inf; (commented by BG on 08/11/2016)
% nSres = nScan; (commented by BG on 08/11/2016)
fprintf('%-40s: %30s','Output images','...initialising'); %-#
%-Initialise new mask name: current mask & conditions on voxels
%----------------------------------------------------------------------
VM = swe_data_hdr_write(sprintf('swe_%s_mask%s', file_data_type, file_ext), DIM, M,...
'swe_cp:resultant analysis mask', metadata, 'uint8');
%-Initialise beta image files
%----------------------------------------------------------------------
for i = 1:nBeta
Vbeta(i) = swe_data_hdr_write(sprintf('swe_%s_beta_b%02d%s',file_data_type,i,file_ext),...
DIM, M,...
sprintf('swe_cp:beta (%02d) - %s',i,xX.name{i}),...
metadata);
end
%-Initialise Cov_beta image files
%----------------------------------------------------------------------
it=0;
for i=1:nBeta
for ii=i:nBeta
it=it+1;
Vcov_beta(it) = swe_data_hdr_write(sprintf('swe_%s_cov_b%02d_b%02d%s',file_data_type,i,ii,file_ext),...
DIM, M, sprintf('cov_beta_%02d_%02d hats - %s/%s',...
i,ii,xX.name{i},xX.name{ii}),...
metadata);
end
end
%-Initialise Cov_beta_g image files if needed
%----------------------------------------------------------------------
if dof_type == 1
if isfield(SwE.type,'classic')
nGr = nSubj;
uGr = uSubj;
end
it=0;
for g=1:nGr
for ii=1:nBeta
for iii=ii:nBeta
it=it+1;
Vcov_beta_g(it) = swe_data_hdr_write([sprintf('swe_%s_cov_g%02d_b%02d_b%02d',file_data_type,g,ii,iii) file_ext],...
DIM, M, sprintf('cov_beta_g_%02d_%02d_%02d hats - group %s - %s/%s',...
g,ii,iii,num2str(uGr(g)),xX.name{ii},xX.name{iii}), metadata);
end
end
end
end
%-Initialise cov_vis image files
%----------------------------------------------------------------------
if isfield(SwE.type,'modified')
it=0;
for g =1:nGr
for ii=1:nVis_g(g)
for iii=ii:nVis_g(g)
it=it+1;
Vcov_vis(it) = swe_data_hdr_write([sprintf('swe_%s_cov_g%02d_v%02d_v%02d',file_data_type,g,ii,iii) file_ext],...
DIM, M, sprintf('cov_vis_%02d_%02d_%02d hats - group %s - visits %s/%s',...
g,ii,iii,num2str(uGr(g)),num2str(uVis_g{g}(ii)),num2str(uVis_g{g}(iii))),...
metadata);
end
end
end
end
%-Initialise standardised residual images
%----------------------------------------------------------------------
% for i = 1:nSres
% VResI(i) = swe_create_vol(sprintf('swe_%s_resid_y%02d.img', i),...
% DIM, M, sprintf('spm_spm:ResI (%02d)', i),...
% isMeshData);
% end
fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...initialised'); %-#
%
%==========================================================================
% - F I T M O D E L & W R I T E P A R A M E T E R I M A G E S
%==========================================================================
%-Get explicit mask(s)
%==========================================================================
mask = true(DIM);
for i = 1:numel(xM.VM)
if isCifti
v = swe_data_read(xM.VM(i)) > 0;
mask = mask & v(:);
clear v
elseif ~(isfield(SwE,'xVol') && isfield(SwE.xVol,'G'))
%-Assume it fits entirely in memory
C = spm_bsplinc(xM.VM(i), [0 0 0 0 0 0]');
v = true(DIM);
[x1,x2] = ndgrid(1:DIM(1),1:DIM(2));
for x3 = 1:DIM(3)
M2 = inv(M\xM.VM(i).mat);
y1 = M2(1,1)*x1+M2(1,2)*x2+(M2(1,3)*x3+M2(1,4));
y2 = M2(2,1)*x1+M2(2,2)*x2+(M2(2,3)*x3+M2(2,4));
y3 = M2(3,1)*x1+M2(3,2)*x2+(M2(3,3)*x3+M2(3,4));
v(:,:,x3) = spm_bsplins(C, y1,y2,y3, [0 0 0 0 0 0]') > 0;
end
mask = mask & v;
clear C v x1 x2 x3 M2 y1 y2 y3
else
if spm_mesh_detect(xM.VM(i))
v = xM.VM(i).private.cdata() > 0;
else
v = spm_mesh_project(gifti(SwE.xVol.G), xM.VM(i)) > 0;
end
mask = mask & v(:);
clear v
end
end
%-Split data into chunks
%==========================================================================
chunksize = floor(spm_get_defaults('stats.maxmem') / 8 / nScan);
nbchunks = ceil(prod(DIM) / chunksize);
chunks = min(cumsum([1 repmat(chunksize,1,nbchunks)]),prod(DIM)+1);
%-Cycle over bunches blocks within planes to avoid memory problems
%==========================================================================
swe_progress_bar('Init',nbchunks,'Parameter estimation','Chunks');
for iChunk=1:nbchunks
chunk = chunks(iChunk):chunks(iChunk+1)-1;
%-Report progress
%======================================================================
if iChunk > 1, fprintf(repmat(sprintf('\b'),1,72)); end %-#
fprintf('%-40s: %30s', sprintf('Chunk %3d/%-3d',iChunk,nbchunks),...
'...processing');
%-Get the data in mask, compute threshold & implicit masks
%------------------------------------------------------------------
Y = zeros(nScan, numel(chunk));
cmask = mask(chunk);
if size(cmask, 2) == 1
cmask = cmask';
end
for iScan=1:nScan
if ~any(cmask), break, end %-Break if empty mask
Y(iScan, cmask) = swe_data_read(VY(iScan), chunk(cmask));%-Read chunk of data
cmask(cmask) = Y(iScan, cmask) > xM.TH(iScan); %-Threshold (& NaN) mask
if xM.I && ~YNaNrep && xM.TH(iScan) < 0 %-Use implicit mask
cmask(cmask) = abs(Y(iScan, cmask)) > eps;
end
end
cmask(cmask) = any(diff(Y(:,cmask),1)); %-Mask constant data
%-Mask out voxels where data is constant in at least one separable
% matrix design either in a visit category or within-subject (BG - 27/05/2016)
%------------------------------------------------------------------
for g = 1:nGr_dof % first look data for each separable matrix design
if sum(iGr_dof'==g) > 1 % do not look for cases where the separable matrix design is only one row (BG - 05/08/2016)
cmask(cmask) = any(abs(diff(Y(iGr_dof'==g, cmask),1)) > eps, 1); % mask constant data within separable matrix design g (added by BG on 29/08/16)
if isfield(SwE.type,'modified') % added by BG on 29/08/16
for g2 = 1:nGr % then look data for each "homogeneous" group
% check if the data is contant over subject for each visit category
for k = 1:nVis_g(g2)
if sum(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(k)) > 1 % do not look for cases when the data is only one row (BG - 05/08/2016)
cmask(cmask) = any(abs(diff(Y(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(k), cmask),1)) > eps, 1);
for kk = k:nVis_g(g2)
if k ~= kk
% extract the list of subject with both visit k and kk
subjList = intersect(iSubj(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(k)), iSubj(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(kk)));
% look if some difference are observed within subject
if ~isempty(subjList)
diffVis = cmask(cmask) == 0;
for i = 1:length(subjList)
diffVis = diffVis | (abs(Y(iSubj == subjList(i) & iVis == uVis_g{g2}(k), cmask) - Y(iSubj == subjList(i) & iVis == uVis_g{g2}(kk), cmask)) > eps);
end
cmask(cmask) = diffVis;
end
end
end
end
end
end
end
end
end
clear diffVis subjList
Y = Y(:, cmask); %-Data within mask
CrS = sum(cmask);
%==================================================================
%-Proceed with General Linear Model (if there are voxels)
%==================================================================
if CrS
beta = pX*Y; %-Parameter estimates
if SwE.SS >= 4 % Cluster-wise adjustments
res = zeros(size(Y));
for i = 1:nSubj
res(iSubj==uSubj(i),:) = corr{i} *...
(Y(iSubj==uSubj(i),:)-xX.X(iSubj==uSubj(i),:)*beta);
end
else
res = diag(corr)*(Y-xX.X*beta); %-Corrected residuals
end
clear Y %-Clear to save memory
%-Estimation of the data variance-covariance components (modified SwE)
%-SwE estimation (classic version)
%--------------------------------------------------------------
c = zeros(numel(chunk),1);
if isfield(SwE.type,'modified')
Cov_vis=zeros(nCov_vis,CrS);
for i = Ind_Cov_vis_diag
Cov_vis(i,:) = mean(res(Flagk(i,:),:).^2, 1);
end
% Check if some voxels have variance < eps and mask them
tmp = ~any(Cov_vis(Ind_Cov_vis_diag,:) < eps); % modified by BG on 29/08/16
if any(~tmp)
beta = beta(:,tmp);
res = res(:,tmp);
cmask(cmask) = tmp;
CrS = sum(cmask);
Cov_vis = Cov_vis(:,tmp);
end
if CrS % Check if there is at least one voxel left
% compute the visit covariance matrices
for i = Ind_Cov_vis_off_diag
if any(Flagk(i,:))
Cov_vis(i,:)= sum(res(Flagk(i,:),:).*res(Flagkk(i,:),:), 1).*...
sqrt(Cov_vis(Ind_Cov_vis_diag(Ind_corr_diag(i,1)),:).*...
Cov_vis(Ind_Cov_vis_diag(Ind_corr_diag(i,2)),:)./...
sum(res(Flagk(i,:),:).^2, 1)./...
sum(res(Flagkk(i,:),:).^2, 1));
end
end
%NaN may be produced in cov. estimation when one correspondant
%variance are = 0, so set them to 0
Cov_vis(isnan(Cov_vis))=0;
%need to check if the eigenvalues of Cov_vis matrices are >=0
for g = 1:nGr
for iVox = 1:CrS
tmp = zeros(nVis_g(g));
tmp(tril(ones(nVis_g(g)))==1) = Cov_vis(iGr_Cov_vis_g==g,iVox);
tmp = tmp + tmp' - diag(diag(tmp));
[V, D] = eig(tmp);
if any (diag(D)<0) %Bug corrected (BG - 19/09/13)
D(D<0) = 0;
tmp = V * D * V';
Cov_vis(iGr_Cov_vis_g==g,iVox) = tmp(tril(ones(nVis_g(g)))==1); %Bug corrected (BG - 19/09/13)
end
end
end
% compute the beta covariance matrice(s)
switch dof_type
case 1
Cov_beta = zeros(nCov_beta, CrS);
it = 0;
for g = 1:nGr
Cov_beta_g = weight(:,iGr_Cov_vis_g==g) * Cov_vis(iGr_Cov_vis_g==g,:);
for i=1:nCov_beta
it = it + 1;
c(cmask) = Cov_beta_g(i,:);
Vcov_beta_g(it) = swe_data_write(Vcov_beta_g(it), c, chunk);
end
Cov_beta = Cov_beta + Cov_beta_g;
end
case {0 2 3}
Cov_beta = weight * Cov_vis;
end
end
else % else for "if isfield(SwE.type,'modified')"
Cov_beta = 0;
it = 0;
for i = 1:nSubj
Cov_beta_i_tmp = weight(:,Ind_Cov_vis_classic==i) *...
(res(Indexk(Ind_Cov_vis_classic==i),:) .* res(Indexkk(Ind_Cov_vis_classic==i),:));
Cov_beta = Cov_beta + Cov_beta_i_tmp;
if dof_type == 1 %need to save all subject contributions...
for ii=1:nCov_beta
it = it + 1;
c(cmask) = Cov_beta_i_tmp(ii,:);
Vcov_beta_g(it) = swe_data_write(Vcov_beta_g(it), c, chunk);
end
end
end
end
end % (CrS)
%-Write output files
%======================================================================
c = zeros(numel(chunk),1);
%-Write mask file
%----------------------------------------------------------------------
mask(chunk) = cmask;
VM = swe_data_write(VM, cmask', chunk);
%-Write beta files
%----------------------------------------------------------------------
for iBeta=1:nBeta
if CrS
c(cmask) = beta(iBeta,:);
end
Vbeta(iBeta) = swe_data_write(Vbeta(iBeta), c, chunk);
end
%-Write CovVis files if needed
%----------------------------------------------------------------------
if isfield(SwE.type,'modified')
for iCov_vis=1:nCov_vis
if CrS
c(cmask) = Cov_vis(iCov_vis,:);
end
Vcov_vis(iCov_vis) = swe_data_write(Vcov_vis(iCov_vis), c, chunk);
end
end
%-Write CovBeta files
%----------------------------------------------------------------------
for iCov_beta=1:nCov_beta
if CrS
c(cmask) = Cov_beta(iCov_beta,:);
end
Vcov_beta(iCov_beta) = swe_data_write(Vcov_beta(iCov_beta), c, chunk);
end
%-Report progress
%======================================================================
fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...done'); %-#
swe_progress_bar('Set',i);
end % iChunk=1:nbchunks
swe_progress_bar('Clear');
%==========================================================================
% - P O S T E S T I M A T I O N C L E A N U P
%==========================================================================
S = nnz(mask);
if S == 0
error('Please check your data: There are no inmask voxels.');
end
%-Compute coordinates of voxels within mask
%--------------------------------------------------------------------------
[x,y,z] = ind2sub(DIM,find(mask));
XYZ = [x y z]';
else % matrix input
% check how the data image treat 0 (as NaN or not)
YNaNrep = 0;
%-Get data & construct analysis mask
%=================================================================
fprintf('%s%30s',repmat(sprintf('\b'),1,30),'...read & mask data')
%-Get the data in mask, compute threshold & implicit masks
%------------------------------------------------------------------
Y = importdata(SwE.xY.P{1});
% do some checking
if ~isnumeric(Y)
error('The input data is not a matrix. Please revised the model specification and specify a ".mat" file containing a unique matrix as data input file')
elseif size(Y, 1) ~= SwE.nscan
error('The input data does not have %i rows and thus is not compatible with the other specified variables. Please revised the model specification.', SwE.nscan)
end
nVox = size(Y, 2);
%-Produce the mask
cmask = true(1, nVox);
%-Use the explicit mask if specified
if length(SwE.xM.VM) == 1
cmask(:) = importdata(SwE.xM.VM{1}) > 0;
end
%-check if some data need to be masked
for i = 1:nScan
if ~any(cmask), break, end %-Break if empty mask
cmask(cmask) = Y(i,cmask) > xM.TH(i); %-Threshold (& NaN) mask
if xM.I && ~YNaNrep && xM.TH(i) < 0 %-Use implicit mask
cmask(cmask) = abs(Y(i,cmask)) > eps;
end
end
%-Mask out voxels where data is constant in at least one separable
% matrix design either in a visit category or within-subject (BG - 27/05/2016)
%------------------------------------------------------------------
for g = 1:nGr_dof % first look data for each separable matrix design
if sum(iGr_dof'==g) > 1 % do not look for cases where the separable matrix design is only one row (BG - 05/08/2016)
cmask(cmask) = any(abs(diff(Y(iGr_dof'==g,cmask),1)) > eps, 1); % mask constant data within separable matrix design g (added by BG on 29/08/16)
if isfield(SwE.type,'modified') % added by BG on 29/08/16
for g2 = 1:nGr % then look data for each "homogeneous" group
% check if the data is contant over subject for each visit category
for k = 1:nVis_g(g2)
if sum(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(k)) > 1 % do not look for cases when the data is only one row (BG - 05/08/2016)
cmask(cmask) = any(abs(diff(Y(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(k) ,cmask),1)) > eps, 1);
for kk = k:nVis_g(g2)
if k ~= kk
% extract the list of subject with both visit k and kk
subjList = intersect(iSubj(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(k)), iSubj(iGr_dof'==g & iGr == uGr(g2) & iVis == uVis_g{g2}(kk)));
% look if some difference are observed within subject
if ~isempty(subjList)
diffVis = cmask(cmask) == 0;
for i = 1:length(subjList)
diffVis = diffVis | (abs(Y(iSubj == subjList(i) & iVis == uVis_g{g2}(k), cmask) - Y(iSubj == subjList(i) & iVis == uVis_g{g2}(kk), cmask)) > eps);
end
cmask(cmask) = diffVis;
end
end
end
end
end
end
end
end
end
clear diffVis
Y = Y(:,cmask); %-Data within mask
CrS = sum(cmask); %-# current voxels
%==================================================================
%-Proceed with General Linear Model (if there are voxels)
%==================================================================
if CrS
%-General linear model: Ordinary least squares estimation
%--------------------------------------------------------------
fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...estimation');%-#
crBeta = pX*Y; %-Parameter estimates
if SwE.SS >= 4 % Cluster-wise adjustments
res = zeros(size(Y));
for i = 1:nSubj
res(iSubj==uSubj(i),:) = corr{i} *...
(Y(iSubj==uSubj(i),:)-xX.X(iSubj==uSubj(i),:)*crBeta);
end
else
res = diag(corr)*(Y-xX.X*crBeta); %-Corrected residuals
end
clear Y %-Clear to save memory
%-Estimation of the data variance-covariance components (modified SwE)
%-SwE estimation (classic version)
%--------------------------------------------------------------
if isfield(SwE.type,'modified')
crCov_beta = 0;
crCov_vis=zeros(nCov_vis,CrS);
for i = Ind_Cov_vis_diag
crCov_vis(i,:) = mean(res(Flagk(i,:),:).^2, 1);
end
% Check if some voxels have variance < eps and mask them
tmp = ~any(crCov_vis(Ind_Cov_vis_diag,:) < eps); % modified by BG on 29/08/16
if any(~tmp)
crBeta = crBeta(:,tmp);
res = res(:,tmp);
cmask(cmask) = tmp;
CrS = sum(cmask);
crCov_vis = crCov_vis(:,tmp);
end
if CrS % Check if there is at least one voxel left
for i = Ind_Cov_vis_off_diag
if any(Flagk(i,:))
crCov_vis(i,:)= sum(res(Flagk(i,:),:).*res(Flagkk(i,:),:), 1).*...
sqrt(crCov_vis(Ind_Cov_vis_diag(Ind_corr_diag(i,1)),:).*...
crCov_vis(Ind_Cov_vis_diag(Ind_corr_diag(i,2)),:)./...
sum(res(Flagk(i,:),:).^2, 1)./...
sum(res(Flagkk(i,:),:).^2, 1));
end
end
%NaN may be produced in crCov. estimation when one correspondant
%variance are = 0, so set them to 0