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swe_cp_WB.m
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swe_cp_WB.m
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function swe_cp_WB(SwE)
% Computes statistic and p-value maps for non-parametric analyses.
% =========================================================================
% For a non-parametric SwE analysis with either NIfTI, GIfTI, CIfTI or '.mat' input, the
% following maps are computed:
%
% - swe_{unit}_mask:
% The mask image for the analysis.
%
% - swe_{unit}_{T|F}stat_c{c#}:
% Voxelwise parametric statistic map (T or F) for contrast {c#}.
%
% - swe_{unit}_{zT|xF}stat_c{c#}:
% Voxelwise parametric equivalent statistic map (Z or Chi Squared)
% for contrast {c#}.
%
% - swe_{unit}_{zT|xF}stat-WB_c{c#}:
% Voxelwise non-parametric equivalent statistic map (Z or Chi
% Squared) for contrast {c#}.
%
% - swe_{unit}_{T|F}stat_lp-WB_c{c#}:
% Log10 map of the voxelwise uncorrected P values for contrast
% {c#}.
%
% - swe_{unit}_{T|F}stat_lpFWE-WB_c{c#}:
% Log10 map of the voxelwise bootstrap-calculated FWE P values for
% contrast {c#}.
%
% - swe_{unit}_{T|F}stat_lpFDR-WB_c{c#}:
% Log10 map of the voxelwise bootstrap-calculated FDR P values for
% contrast {c#}.
%
% - swe_clustere_{T|F}stat_lpFWE-WB_c{c#}:
% Log10 map of the clusterwise bootstrap-calculated FWE P values
% for contrast {c#}.
%
% - swe_tfce_c{c#}:
% TFCE parametric statistic map for contrast {c#}.
%
% - swe_tfce_lp-WB_c{c#}:
% Log10 map of the TFCE bootstrap-calculated P values for contrast
% {c#}.
%
% - swe_tfce_lpFWE-WB_c{c#}:
% Log10 map of the TFCE bootstrap-calculated FWE P values for
% contrast {c#}.
%
% The field {unit} used above represents the unit in space in which the statistic is calculated.
% It can be the following strings:
% - 'vox' for NifTI files,
% - 'dpx' for GIfTI and CIfTI files,
% - 'dat' for .mat files.
% Currently (30/08/2018), the only contrasts computed are activation
% (contrast #1) and deactivation (contrast #2) for the contrast vector the
% user input during the batch entry.
% =========================================================================
% FORMAT swe_cp_WB(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')
%-Change to SwE.swd if specified
%--------------------------------------------------------------------------
try
cd(SwE.swd);
catch %#ok<*CTCH>
SwE.swd = pwd;
end
%-Shuffle seed of random number generator
%--------------------------------------------------------------------------
swe_seed
%-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';
dataType = swe_DataType('Cifti');
dataTypeSpecificInformation = SwE.cifti;
end
if isMat
file_data_type = 'dat';
if SwE.WB.clusterWise == 1
isVolumeMat = isfield(SwE.WB.clusterInfo, 'Vxyz');
isSurfaceMat = isfield(SwE.WB.clusterInfo, 'Vfaces');
if isVolumeMat
dataType = swe_DataType('VolumeMat');
dataTypeSpecificInformation = [];
elseif isSurfaceMat
dataType = swe_DataType('SurfaceMat');
dataTypeSpecificInformation = importdata(SwE.WB.clusterInfo.Vfaces{1});
if size(dataTypeSpecificInformation,1) ~=3 && size(dataTypeSpecificInformation,2) ~=3
error('faces coodinates do not seem correct')
elseif size(dataTypeSpecificInformation,1) == 3
dataTypeSpecificInformation = dataTypeSpecificInformation';
end
else
dataType = swe_DataType('Mat');
dataTypeSpecificInformation = [];
end
else
dataType = swe_DataType('Mat');
dataTypeSpecificInformation = [];
end
end
if ~isMat && ~isCifti
isMeshData = spm_mesh_detect(SwE.xY.VY);
if isMeshData
file_ext = '.gii';
file_data_type = 'dpx';
dataType = swe_DataType('Gifti');
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};
if (SwE.WB.clusterWise == 1)
dataTypeSpecificInformation = export(gifti(SwE.xVol.G),'patch');
end
else
dataType = swe_DataType('Nifti');
dataTypeSpecificInformation = [];
file_ext = spm_file_ext;
file_data_type = 'vox';
metadata = {};
end
else
isMeshData = false;
end
try
giftiAreaFile = SwE.gifti.areaFile;
catch
giftiAreaFile = '';
end
isVolumeMat = (dataType == swe_DataType('VolumeMat'));
isSurfaceMat = (dataType == swe_DataType('SurfaceMat'));
isNifti = (dataType == swe_DataType('Nifti'));
isGifti = (dataType == swe_DataType('Gifti'));
%-Check whether we are doing a TFCE analysis
%--------------------------------------------------------------------------
% deactivate for now TFCE if we analyse surface data
TFCE = isfield(SwE.WB, 'TFCE') && ~isMeshData;
if TFCE
H = SwE.WB.TFCE.H;
E = SwE.WB.TFCE.E;
dh = SwE.WB.TFCE.dh;
C = 18;
end
%-Prevent unnecessary octave warning
%--------------------------------------------------------------------------
if isOctave
warning ('off', 'histc: empty EDGES specified\n');
end
%-Delete files from previous analyses
%--------------------------------------------------------------------------
if exist(fullfile(SwE.swd,sprintf('swe_v%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}$',...
'^swe_clustere_\w{1,2}stat_lp\w{0,3}-WB_c\d{2}(\.dtseries)?(\.dscalar)?\..{3}$',...
'^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
% Tolerance for comparing real numbers
tol = 1e-8;
%==========================================================================
% - 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
iSubj = SwE.Subj.iSubj;
uSubj = unique(iSubj);
nSubj = length(uSubj);
SwE.Subj.uSubj = uSubj;
SwE.Subj.nSubj = nSubj;
%-WB variables
%
WB = SwE.WB;
conWB = WB.con;
nSizeCon = size(conWB,1);
rankCon = rank(conWB);
% If clusterWise inference, force the U-SwE to be used
if WB.clusterWise == 1 && WB.RSwE ==1
WB.RSwE = 0;
SwE.WB.RSwE = 0;
if isOctave
save('SwE.mat','SwE');
elseif spm_matlab_version_chk('7') >=0
save('SwE','SwE','-V6');
else
save('SwE','SwE');
end
end
% If clusterWise inference and .mat format, check for the presence of
% spatial information
if isMat && WB.clusterWise == 1
if isVolumeMat
XYZ = importdata(SwE.WB.clusterInfo.Vxyz{1});
if size(XYZ,1) ~=3 && size(XYZ,2) ~=3
error('voxel coodinates do not seem correct')
elseif size(XYZ,2) ==3
XYZ = XYZ';
end
elseif ~isSurfaceMat
error('clusterWise inference cannot be done without spatial information when inputs are in ".mat" format. Please supply faces coordinates (faces or tris) for surface data or voxel coordinates (XYZ_vox) for volumetric data');
end
end
% small sample correction (for WB)
[corrWB, tmpR2] = swe_resid_corr(SwE, 1, WB.SS, pX);
% small sample correction (for parametric)
[corr, tmpR2] = swe_resid_corr(SwE, WB.RSwE, SwE.SS, pX, tmpR2);
%-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
if nSizeCon==1
ind = find(conWB ~= 0);
else
ind = find(any(conWB~=0));
end
indSubDesignMatrices = iBeta_dof(ind);
subjectsInvolved = [];
for iIndSubDesignMatrices = indSubDesignMatrices
subjectsInvolved = [subjectsInvolved; iSubj(iGr_dof == iIndSubDesignMatrices)];
end
subjectsInvolved = unique(subjectsInvolved);
indSubjInvolved = nan(length(subjectsInvolved),1);
% convert into in
for iSubjInvolved = 1:length(subjectsInvolved)
indSubjInvolved(iSubjInvolved) = find(uSubj == subjectsInvolved(iSubjInvolved));
end
edf = sum(edof_Subj(indSubjInvolved));
dof_cov = zeros(1,nBeta);
for i = 1:nBeta
dof_cov(i) = nSubj_dof(iBeta_dof(i)) - ...
pB_dof(iBeta_dof(i));
end
% This variable should be left empty for Niave estimation.
dofMat = [];
xX.erdf_niave = edf;
SwE.xX = xX;
else
edf = NaN;
end
%-preprocessing for the modified SwE
if isfield(SwE.type,'modified')
if dof_type == 1
error('degrees of freedom type still not implemented for the modified SwE and the WB')
end
iVis = SwE.Vis.iVis;
iGr = SwE.Gr.iGr;
uGr = unique(iGr);
nGr = length(uGr);
SwE.Gr.uGr = uGr;
SwE.Gr.nGr = nGr;
% 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
% Save Vis variables.
SwE.Vis.uVis_g = uVis_g;
SwE.Vis.nVis_g = nVis_g;
SwE.Vis.max_nVis_g = max_nVis_g;
SwE.Vis.min_nVis_g = min_nVis_g;
% 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
% Record igr_Cov_vis_g.
SwE.WB.iGr_Cov_vis_g = iGr_Cov_vis_g;
% weights for the vectorised SwE
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
% Weight giving only the contrasted SwE (WB)
weightR = pinv(swe_duplication_matrix(nSizeCon)) * kron(conWB,conWB) * swe_duplication_matrix(nBeta) * weight; % used to compute the R SwE R'
Wg = cell(nGr,1);
Wg_testII = cell(nGr,1);
Wg_testIII = cell(nGr,1);
tmp = eye(nSizeCon^2);
for g = 1:nGr
Wg{g} = kron(weightR(:,iGr_Cov_vis_g==g),weightR(:,iGr_Cov_vis_g==g)) * swe_duplication_matrix(nCov_vis_g(g));
Wg_testII{g} = sum(kron(swe_duplication_matrix(nSizeCon),swe_duplication_matrix(nSizeCon)), 1) * Wg{g};
Wg_testIII{g} = tmp(:)' * (kron(swe_duplication_matrix(nSizeCon),swe_duplication_matrix(nSizeCon))) * Wg{g};
end
SwE.WB.Wg{1} = Wg;
SwE.WB.Wg{2} = Wg_testII;
SwE.WB.Wg{3} = Wg_testIII;
%-compute the effective dof from each homogeneous group if dof_type
switch dof_type
case 1
dofMat = NaN;
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
weightR = pinv(swe_duplication_matrix(nSizeCon)) * kron(conWB,conWB) * swe_duplication_matrix(nBeta) * weight; % used to compute the R SwE R'
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)
[p,n,e] = fileparts(VY(i).fname);
VY(i).fname = [n,e];
end
end
M = VY(1).mat;
DIM = VY(1).dim;
YNaNrep = VY(1).dt(2);
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 original parametric score image, T or F
%----------------------------------------------------------------------
if WB.stat=='T'
eSTAT='z';
else % F stat
eSTAT='x';
end
Vscore = swe_data_hdr_write(sprintf('swe_%s_%cstat_c%02d%s', file_data_type, WB.stat, 1, file_ext), DIM, M,...
sprintf('Original parametric %c statistic data.', WB.stat), metadata);
%-Initialise parametric TFCE score image, if TFCE has been selected.
%----------------------------------------------------------------------
if TFCE
Vscore_tfce = swe_data_hdr_write(sprintf('swe_tfce_c%02d%s', 1, file_ext), DIM, M,...
'Original parametric TFCE statistic data.', metadata);
if WB.stat=='T'
Vscore_tfce_neg = swe_data_hdr_write(sprintf('swe_tfce_c%02d%s', 2, file_ext), DIM, M,...
'Original parametric TFCE statistic data for a negative contrast.', metadata);
end
end
%-Initialise original parametric edf image
%----------------------------------------------------------------------
Vedf = swe_data_hdr_write(sprintf('swe_%s_edf_c%02d%s', file_data_type, 1, file_ext), DIM, M,...
sprintf('Original parametric %c edf data.', WB.stat), metadata);
%-Initialise parametric P-Value image
%----------------------------------------------------------------------
VlP = swe_data_hdr_write(sprintf('swe_%s_%cstat_lp_c%02d%s', file_data_type, WB.stat, 1, file_ext), DIM, M,...
'Original parametric -log10(P) value data (positive).', metadata);
if WB.stat=='T'
VlP_Neg = swe_data_hdr_write(sprintf('swe_%s_%cstat_lp_c%02d%s', file_data_type, WB.stat, 2, file_ext), DIM, M,...
'Original parametric -log10(P) value data (negative).', metadata);
end
%-Initialise converted parametric score image
%----------------------------------------------------------------------
VcScore = swe_data_hdr_write(sprintf('swe_%s_%c%cstat_c%02d%s', file_data_type, eSTAT, WB.stat, 1, file_ext), DIM, M,...
sprintf('Parametric %c statistic data derived from %c-Statistic data.', eSTAT, WB.stat), metadata);
if WB.stat=='T'
VcScore_neg = swe_data_hdr_write(sprintf('swe_%s_%c%cstat_c%02d%s', file_data_type, eSTAT, WB.stat, 2, file_ext), DIM, M,...
sprintf('Parametric %c statistic data derived from %c-Statistic data.', eSTAT, WB.stat), metadata);
end
%-Initialise residual images for the resampling
%----------------------------------------------------------------------
for i = 1:nScan
descrip = sprintf('adjusted restricted residuals (%04d)', i);
VResWB(i) = swe_data_hdr_write(sprintf('swe_%s_resid_y%04d%s', file_data_type, i, file_ext), DIM, M, descrip, metadata);
end
%-Initialise fitted data images for the resampling
%----------------------------------------------------------------------
for i = 1:nScan
descrip = sprintf('restricted fitted data (%04d)', i);
VYWB(i) = swe_data_hdr_write(sprintf('swe_%s_fit_y%04d%s',file_data_type,i,file_ext), DIM, M, descrip, metadata);
end
%-Initialise result images
%----------------------------------------------------------------------
VlP_wb_pos = swe_data_hdr_write(sprintf('swe_%s_%cstat_lp-WB_c%02d%s', file_data_type, WB.stat, 1, file_ext), DIM, M,...
'Non-parametric voxelwise -log10(P) value data (positive).', metadata);
VlP_wb_FWE_pos = swe_data_hdr_write(sprintf('swe_%s_%cstat_lpFWE-WB_c%02d%s', file_data_type, WB.stat, 1, file_ext), DIM, M,...
'Non-parametric voxelwise FWE -log10(P) value data (positive).', metadata);
VlP_wb_FDR_pos = swe_data_hdr_write(sprintf('swe_%s_%cstat_lpFDR-WB_c%02d%s', file_data_type, WB.stat, 1, file_ext), DIM, M,...
'Non-parametric voxelwise FDR -log10(P) value data (positive).', metadata);
if WB.stat=='T'
VlP_wb_neg = swe_data_hdr_write(sprintf('swe_%s_%cstat_lp-WB_c%02d%s', file_data_type, WB.stat, 2, file_ext), DIM, M,...
'Non-parametric voxelwise -log10(P) value data (negative).', metadata);
VlP_wb_FWE_neg = swe_data_hdr_write(sprintf('swe_%s_%cstat_lpFWE-WB_c%02d%s', file_data_type, WB.stat, 2, file_ext), DIM, M,...
'Non-parametric voxelwise FWE -log10(P) value data (negative).', metadata);
VlP_wb_FDR_neg = swe_data_hdr_write(sprintf('swe_%s_%cstat_lpFDR-WB_c%02d%s', file_data_type, WB.stat, 2, file_ext), DIM, M,...
'Non-parametric voxelwise FDR -log10(P) value data (negative).', metadata);
end
%-Initialise parametric TFCE results images, if TFCE has been selected.
%----------------------------------------------------------------------
if TFCE
VlP_tfce_pos = swe_data_hdr_write(sprintf('swe_tfce_lp-WB_c%02d%s', 1, file_ext), DIM, M,...
'Non-parametric TFCE -log10(P) value data (positive).', metadata);
VlP_tfce_FWE_pos = swe_data_hdr_write(sprintf('swe_tfce_lpFWE-WB_c%02d%s', 1, file_ext), DIM, M,...
'Non-parametric TFCE FWE -log10(P) value data (positive).', metadata);
if WB.stat=='T'
VlP_tfce_neg = swe_data_hdr_write(sprintf('swe_tfce_lp-WB_c%02d%s', 2, file_ext), DIM, M,...
'Non-parametric TFCE -log10(P) value data (negative).', metadata);
VlP_tfce_FWE_neg = swe_data_hdr_write(sprintf('swe_tfce_lpFWE-WB_c%02d%s', 2, file_ext), DIM, M,...
'Non-parametric TFCE FWE -log10(P) value data (negative).', metadata);
end
end
% Converted score for WB.
VcScore_wb_pos = swe_data_hdr_write(sprintf('swe_%s_%c%cstat-WB_c%02d%s', file_data_type, eSTAT, WB.stat, 1, file_ext), DIM, M,...
sprintf('Non-parametric %c statistic data derived from %c-Statistic data.', eSTAT, WB.stat), metadata);
if WB.clusterWise == 1
% We also need cluster p value maps here.
V_clustere_pos = swe_data_hdr_write(sprintf('swe_clustere_%cstat_c%02d%s', WB.stat, 1, file_ext), DIM, M,...
sprintf('Cluster extent (positive, CFT %g).',...
SwE.WB.clusterInfo.primaryThreshold), metadata);
if WB.stat=='T'
V_clustere_neg = swe_data_hdr_write(sprintf('swe_clustere_%cstat_c%02d%s', WB.stat, 2, file_ext), DIM, M,...
sprintf('Cluster extent (negative, CFT %g).',...
SwE.WB.clusterInfo.primaryThreshold), metadata);
end
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);
nDataElements = numel(mask);
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
coeff = 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(coeff, y1,y2,y3, [0 0 0 0 0 0]') > 0;
end
mask = mask & v;
clear coeff 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);
% activated voxels for cluster-wise inference
if (WB.clusterWise == 1)
activatedVoxels = false(0);
maxClusterSize = nan(1, WB.nB + 1);
maxClusterSizeInSurfaces = nan(1, WB.nB + 1);
maxClusterSizeInVolume = nan(1, WB.nB + 1);
activatedVoxelsNeg = false(0);
if (WB.stat == 'T')
maxClusterSizeNeg = nan(1, WB.nB + 1);
maxClusterSizeInSurfacesNeg = nan(1, WB.nB + 1);
maxClusterSizeInVolumeNeg = nan(1, WB.nB + 1);
end
end
maxScore = nan(1, WB.nB + 1);
if (WB.stat == 'T')
minScore = nan(1, WB.nB + 1);
end
%-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('Original statistics: 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 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)
%------------------------------------------------------------------
[cmask, Y, CrS] = swe_mask_seperable(SwE, cmask, Y, iGr_dof);
%==================================================================
%-Proceed with General Linear Model (if there are voxels)
%==================================================================
if CrS
%-General linear model: Ordinary least squares estimation
%--------------------------------------------------------------
beta = pX*Y; %-Parameter estimates
% restricted fitted data
[resWB, YWB] = swe_fit(SwE, Y, tmpR2, corrWB, beta, SwE.WB.SS);
if WB.RSwE == 1
res = swe_fit(SwE, Y, tmpR2, corr, beta, SwE.SS);
else
res = swe_fit(SwE, Y, xX.X, corr, beta, SwE.SS);
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')
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);
resWB = resWB(:,tmp);
res = res(:,tmp);
YWB = YWB(:,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
if dof_type == 1
tmpSum = zeros(1,CrS);
end
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
if dof_type == 1
Cov_beta_g_tmp = weightR(:, iGr_Cov_vis_g==g) * Cov_vis(iGr_Cov_vis_g==g,:);
if nSizeCon == 1
tmpSum = tmpSum + Cov_beta_g_tmp.^2/edof_Gr(g);
else
for iVox = 1:CrS
cCovBc_g_vox = zeros(nSizeCon);
cCovBc_g_vox(tril(ones(nSizeCon))==1) = Cov_beta_g_tmp(:, iVox);
cCovBc_g_vox = cCovBc_g_vox + cCovBc_g_vox' - diag(diag(cCovBc_g_vox));
tmpSum(iVox) = tmpSum(iVox) + (trace(cCovBc_g_vox^2) + (trace(cCovBc_g_vox))^2)/edof_Gr(g);
end
end
end
end
end
end
cCovBc = weightR * Cov_vis;
clear Cov_beta_g_tmp
else % else for "if isfield(SwE.type,'modified')"
cCovBc = 0;
if dof_type == 1
tmpSum = zeros(1,CrS);
end
for i = 1:nSubj
Cov_beta_i_tmp = weightR(:,Ind_Cov_vis_classic==i) *...
(res(Indexk(Ind_Cov_vis_classic==i),:) .* res(Indexkk(Ind_Cov_vis_classic==i),:));
cCovBc = cCovBc + Cov_beta_i_tmp;
if dof_type == 1
if nSizeCon == 1
tmpSum = tmpSum + Cov_beta_i_tmp.^2/edof_Gr(i);
else
for iVox = 1:CrS
cCovBc_g_vox = zeros(nSizeCon);
cCovBc_g_vox(tril(ones(nSizeCon))==1) = Cov_beta_i_tmp(:,iVox);
cCovBc_g_vox = cCovBc_g_vox + cCovBc_g_vox' - diag(diag(cCovBc_g_vox));
tmpSum(iVox) = tmpSum(iVox) + (trace(cCovBc_g_vox^2) + (trace(cCovBc_g_vox))^2)/edof_Gr(i);
end
end
end
end
% These variables are left empty for classic SwE.
Cov_vis = [];
dofMat = [];