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tfce_estimate_stat.m
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tfce_estimate_stat.m
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function tfce_estimate_stat(job)
% main TFCE function for estimating TFCE statistics
%
% FORMAT tfce_estimate_stat(job)
% job - job from tbx_cfg_tfce
%
% job fields:
% data
%
% nproc
% 0 - no multi-threading
% x - number of processors for multi-threading
%
% nuisance_method
% method to deal with nuisance variables
% 0 - Draper-Stoneman
% 1 - Freedman-Lane
% 2 - Smith
%
% single-threaded
% 0 - multi-threaded TFCE estimation
% 1 - single-threaded TFCE estimation
%
% conspec.contrasts
% Inf - interactive selection of contrast
% x - index of contrast(s)
%
% mask
% mask for restricting TFCE estimation (SVC)
%
% tbss
% TBSS weighting
%
% ______________________________________________________________________
%
% Christian Gaser
% Structural Brain Mapping Group (https://neuro-jena.github.io)
% Departments of Neurology and Psychiatry
% Jena University Hospital
% ______________________________________________________________________
global old_method_stat
% disable parallel processing for only one SPM.mat file
if numel(job.data) == 1
job.nproc = 0;
end
% split job and data into separate processes to save computation time
if isfield(job,'nproc') && job.nproc>0 && (~isfield(job,'process_index')) && exist('cat_parallelize','file')
cat_parallelize(job,mfilename,'data');
return
% or run though all data step-by-step
elseif numel(job.data)>1
data = job.data;
for i=1:numel(job.data)
job = rmfield(job,'data');
job.process_index = i;
job.data{1} = data{i};
tfce_estimate_stat(job)
end
return
end
% Use tail approximation from the Gamma distribution for corrected P-values
use_gamma_tail_approximation = true;
tail_approximation_wo_unpermuted_data = false;
% single-threaded?
singlethreaded = job.singlethreaded;
% convert to z-statistic
convert_to_z = false;
% option to stop estimation if no FWE-corrected result was found after defined number of permutations
stop_if_no_FWEeffects_found = Inf;
% use (too liberal) method for estimating maximum statistic from old release
% r184 for compatibility purposes only that was estimating max/min statistics
% only inside pos./neg. effects and not both
if isfield(job,'old_method_stat')
old_method_stat = job.old_method_stat;
elseif exist('old_method_stat','var') && isempty(old_method_stat)
old_method_stat = false;
end
if old_method_stat
fprintf('Use old method from r184 to estimate maximum statistics which might be too liberal.\n');
end
% save null distribution
save_null_distribution = true;
% method to deal with nuisance variables
% 0 - Draper-Stoneman
% 1 - Freedman-Lane
% 2 - Smith
nuisance_method = job.nuisance_method;
% display permuted design matrix (otherwise show t distribution)
show_permuted_designmatrix = true;
% allow to test permutations without analyzing data
% test mode is automatically chosen if only a SPM.mat is available
% without any data
test_mode = false;
% define stepsize for tfce
n_steps_tfce = 100;
% colors and alpha levels
col = [0.25 0 0; 1 0 0; 1 0.75 0];
alpha = [0.05 0.01 0.001];
% give same results each time
if exist('rng','file') == 2
rng('default')
rng(0)
else
rand('state',0);
end
% tolerance for comparing real numbers
tol = 1e-4;
% check spm version
if ~strcmp(spm('ver'),'SPM12')
error('Please use any TFCE version < r215 that still supports SPM8.')
end
% indicate if voxel-wise covariates were modeled
[pt,nm] = fileparts(job.data{1});
is_vSPM = strcmp(nm,'vSPM');
load(job.data{1});
cwd = fileparts(job.data{1});
%-Check that model has been estimated
if ~isfield(SPM, 'xVol')
str = { 'This model has not been estimated.';...
'Would you like to estimate it now?'};
if spm_input(str,1,'bd','yes|no',[1,0],1)
cd(cwd)
SPM = spm_spm(SPM);
else
return
end
end
Ic0 = job.conspec.contrasts;
% if just one contrast is defined use this and skip interactive selection
if (numel(Ic0) == 1) && ~isfinite(Ic0) && isfield(SPM,'xCon') && (numel(SPM.xCon) == 1)
Ic0 = 1;
end
% check whether contrast are defined
if (numel(Ic0) == 1)
if ~isfinite(Ic0) || ~isfield(SPM,'xCon') || (isfield(SPM,'xCon') && isempty(SPM.xCon))
[Ic0,xCon] = spm_conman(SPM,'T&F',Inf,...
' Select contrast(s)...',' ',1);
SPM.xCon = xCon;
end
end
% for default SPM.mat results has to be called first
if isempty(SPM.xCon(Ic0(1)).eidf) && ~is_vSPM
fprintf('You have to call results first.\n');
cat_spm_results_ui('Setup',SPM);
load(job.data{1});
end
% check that no temporal filter was used
if isstruct(SPM.xX.K)
fprintf('ERROR: No first level analysis with temporal correlations allowed.\n');
return
end
% get some parameters from SPM
xX = SPM.xX;
VY = SPM.xY.VY;
n_data = size(xX.X,1);
% sometimes xX.iB and xX.iH are not correct and cannot be used to reliably recognize the design
xX = correct_xX(xX);
% check whether voxel-wise covariate is defined
voxel_covariate = 0;
for i = 1:numel(SPM.xC)
if isfield(SPM.xC(i),'P') && ~isempty(SPM.xC(i).P)
voxel_covariate = i;
fprintf('Voxel-wise covariate found.\n');
end
end
% find exchangeability block labels for longitudinal designs (paired t-test, flexible factorial)
repeated_anova = ~isempty(xX.iB);
if repeated_anova
[rw,cl] = find(xX.I == length(xX.iB)); % find column which codes subject factor (length(xX.iB) -> n_subj)
exch_block_labels = xX.I(:,cl(1)); % column from above contains the subject factor
% check that labels are defined for each block
for i=1:n_data
groupListed(exch_block_labels(i)) = true;
end
for i = 1:max(exch_block_labels)
if ~groupListed(i)
fprintf('\nError: block %d must be assigned to at least one design row in the blocks file.\n\n',i);
return
end
end
% check whether ate least two time points are available
n_tp = sum(xX.X(:,xX.iB));
if any(n_tp == 1)
fprintf('\nError: You need at least two time points for a longitudinal design for every subjects.\n\n');
return
end
if ~isempty(xX.iC)
fprintf('\Warning: Covariates are not recommended for repeated measures Anova and will not properly work.\n');
fprintf('Please remove any covariates from your longitudinal design.\n\n');
end
fprintf('\nPlease note that permutation is only done within subjects for repeated Anova.\n');
else
exch_block_labels = ones(1,n_data);
end
if ~test_mode
% check for meshes
if spm_mesh_detect(VY)
mesh_detected = true;
else
mesh_detected = false;
end
% set E according to type data
if job.tbss || mesh_detected
E = 1.0; H = 2.0;
else
E = 0.5; H = 2.0;
end
% check for mask image that should exist for any analysis
if exist(fullfile(cwd, 'mask.img'),'file')
file_ext = '.img';
elseif exist(fullfile(cwd, 'mask.nii'),'file')
file_ext = '.nii';
elseif exist(fullfile(cwd, 'mask.gii'),'file')
file_ext = '.gii';
else
fprintf('WARNING: No mask file found. Switch to test mode.\n');
test_mode = true;
end
else
% for test_mode
mesh_detected = false;
end
% check whether 3D filters were selected for surfaces meshes or whether CAT12 is installed
if mesh_detected
if ~exist('spm_cat12','file')
error('For using surface analysis you need to install CAT12.');
end
end
if ~test_mode
% get mask file
if isempty(job.mask)
maskname = fullfile(cwd,['mask' file_ext]);
else
if ~isempty(job.mask{1})
maskname = job.mask{1};
else
maskname = fullfile(cwd,['mask' file_ext]);
end
end
% load mask
try
Vmask = spm_data_hdr_read(maskname);
catch
if mesh_detected
maskname = spm_select(1,'mesh','select surface mask');
else
maskname = spm_select(1,'image','select mask image');
end
Vmask = spm_data_hdr_read(maskname);
end
% if first image was not found you have to select all files again
if ~exist(VY(1).fname,'file')
fprintf('Data not found. Please select data in the order defined in the design matrix.\n');
n = size(SPM.xY.VY,1);
if mesh_detected
P = spm_select(n,'mesh','select surfaces');
else
P = spm_select(n,'image','select images');
end
VY = spm_data_hdr_read(P);
%-Apply gSF to memory-mapped scalefactors to implement scaling
%--------------------------------------------------------------------------
for i = 1:n
VY(i).pinfo(1:2,:) = VY(i).pinfo(1:2,:)*SPM.xGX.gSF(i);
if mesh_detected
if isfield(VY(i).private.private.data{1}.data,'scl_slope')
VY(i).private.private.data{1}.data.scl_slope = ...
VY(i).private.private.data{1}.data.scl_slope * SPM.xGX.gSF(i);
VY(i).private.private.data{1}.data.scl_inter = ...
VY(i).private.private.data{1}.data.scl_inter * SPM.xGX.gSF(i);
end
else
VY(i).private.dat.scl_slope = ...
VY(i).private.dat.scl_slope * SPM.xGX.gSF(i);
VY(i).private.dat.scl_inter = ...
VY(i).private.dat.scl_inter * SPM.xGX.gSF(i);
end
end
SPM.xY.VY = VY;
% update SPM
if size(SPM.xY.VY,1)==n
save(job.data{1},'SPM','-v7.3');
else
fprintf('ERROR: Number of files is not correct\n');
return
end
end
if voxel_covariate
% if first image of voxel-wise covariate was not found you have to select all files again
if ~exist(SPM.xC(voxel_covariate).VC(1).fname,'file')
fprintf('Covariate data not found. Please select covariate data in the order defined in the design matrix.\n');
n = numel(SPM.xC(voxel_covariate).P);
if mesh_detected
P = spm_select(n,'mesh','select surfaces');
else
P = spm_select(n,'image','select images');
end
SPM.xC(voxel_covariate).P = cellstr(P);
if size(P,1)==n
save(job.data{1},'SPM','-v7.3');
else
fprintf('ERROR: Number of files is not correct\n');
return
end
end
VC = spm_data_hdr_read(char(SPM.xC(voxel_covariate).P));
n = numel(VC);
%-Apply gSF to memory-mapped scalefactors to implement scaling for
% voxel-wise covariate
%--------------------------------------------------------------------------
if isfield(SPM.xC(voxel_covariate),'gSF')
for i = 1:n
VC(i).pinfo(1:2,:) = VC(i).pinfo(1:2,:)*SPM.xC(voxel_covariate).gSF(i);
if mesh_detected
if isfield(VC(i).private.private.data{1}.data,'scl_slope')
VC(i).private.private.data{1}.data.scl_slope = ...
VC(i).private.private.data{1}.data.scl_slope * SPM.xC(voxel_covariate).gSF(i);
VC(i).private.private.data{1}.data.scl_inter = ...
VC(i).private.private.data{1}.data.scl_inter * SPM.xC(voxel_covariate).gSF(i);
end
else
VC(i).private.dat.scl_slope = ...
VC(i).private.dat.scl_slope * SPM.xC(voxel_covariate).gSF(i);
VC(i).private.dat.scl_inter = ...
VC(i).private.dat.scl_inter * SPM.xC(voxel_covariate).gSF(i);
end
end
end
SPM.xC(voxel_covariate).VC = VC;
end
% check whether mask images fits to the data
if mesh_detected, dim_index = 1; else dim_index=1:3; end
if sum(sum((Vmask.mat-VY(1).mat).^2)) > 1e-6 || any(Vmask.dim(dim_index) ~= VY(1).dim(dim_index))
error('Mask must have the same dimensions and orientation as the data.');
end
if voxel_covariate
do_resample = false;
if sum(sum((Vmask.mat-VC(1).mat).^2)) > 1e-6 || any(Vmask.dim(dim_index) ~= VC(1).dim(dim_index))
fprintf('Covariate data has different dimensions and orientation as the other data. Thus, your covariate data will be resampled.\n');
fprintf('Please check that your covariate data are co-registered to your other data!\n');
do_resample = true;
end
end
% read mask and data
mask = spm_data_read(Vmask);
ind_mask = find(mask>0);
n = numel(VY);
if ~isempty(ind_mask)
Y = zeros([length(ind_mask) n],'single');
if voxel_covariate
C = zeros([length(ind_mask) n],'single');
meanC = zeros(length(ind_mask),1,'single');
else
C = [];
end
% load data
fprintf('Load data ')
for i=1:n
fprintf('.')
tmp = spm_data_read(VY(i));
Y(:,i) = tmp(ind_mask);
if voxel_covariate
% we need to resample voxel-wise covariate
if do_resample
tmp = zeros(Vmask.dim);
for z=1:Vmask.dim(3)
M = spm_matrix([0 0 z]);
M1 = Vmask(1).mat\VC(i).mat\M;
tmp(:,:,z) = spm_slice_vol(VC(i),M1, Vmask.dim(1:2),[1 0]);
end
else
tmp = spm_data_read(VC(i));
end
tmp = tmp(ind_mask);
C(:,i) = tmp;
meanC = meanC + tmp;
end
end
fprintf('\n')
clear tmp;
% whitening matrix
W = single(full(xX.W));
Y = Y*W;
% mean correct voxel-wise covariate
if voxel_covariate
meanC = meanC/n;
for i=1:n
C(:,i) = C(:,i) - meanC;
end
C = C*W;
end
else
error('Empty mask.');
end
t0 = zeros(Vmask.dim);
t = zeros(Vmask.dim);
end % if ~test_mode
% if just one contrast is defined, use this contrast and skip interactive selection
if isfield(SPM,'xCon') && numel(SPM.xCon) == 1 && ~isfinite(Ic0)
Ic0 = 1;
end
% interactively select contrast(s) if necessary
if numel(Ic0)==1 && ~isfinite(Ic0) && numel(SPM.xCon) > 1
[Ic0,xCon] = spm_conman(SPM,'T&F',Inf,...
' Select contrast(s)...',' ',1);
SPM.xCon = xCon;
end
% go through all contrasts
for con = 1:length(Ic0)
Ic = Ic0(con);
xCon = SPM.xCon(Ic);
n_perm = job.conspec.n_perm(1);
if numel(job.conspec.n_perm) > 1
n_perm_break = job.conspec.n_perm(2);
end
if length(Ic) > 1
fprintf('ERROR: No conjunction allowed.\n');
return
end
fprintf('Use contrast #%d of %s\n',Ic,job.data{1})
% get contrast and name
c0 = xCon.c;
F_contrast_multiple_rows = 0;
% for F-contrasts if rank is 1 we can use the first row
if strcmp(xCon.STAT,'F')
if rank(c0) == 1
c0 = c0(:,1);
else
F_contrast_multiple_rows = 1;
end
end
[indi, indj] = find(c0~=0);
ind_X = unique(indi)';
xCon.ind_X = ind_X;
% check for contrasts that are defined for columns with subject effects
if ~isempty(xX.iB)
if max(ind_X) > min(xX.iB)
fprintf('ERROR: No contrasts on subjects/block effects allowed.\n');
return
end
end
% find exchangeability blocks using contrasts without zero values
exch_blocks = c0(ind_X,:);
n_exch_blocks = length(ind_X);
% recognize effects of interest contrast for F-tests
if F_contrast_multiple_rows && size(exch_blocks,2) == n_exch_blocks
is_eoi = all(all(exch_blocks == eye(n_exch_blocks)));
if is_eoi
n_exch_blocks = 1;
end
end
% check for exchangeability blocks and design matrix
if n_exch_blocks == 1
n_cond = length(find(xX.iH==ind_X)); % check whether the contrast is defined at columns for condition effects
else
n_cond = 0;
n_data_cond = [];
for k=1:length(xX.iH)
n_data_cond = [n_data_cond sum(xX.X(:,xX.iH(k)))];
end
% for F-contrast with multiple rows n_cond is always n_exch_blocks
if F_contrast_multiple_rows && length(xX.iH) > 1
n_cond = n_exch_blocks;
elseif F_contrast_multiple_rows && length(xX.iH) == 1
n_cond = 0;
else
for j=1:n_exch_blocks
col_exch_blocks = find(c0==exch_blocks(j));
for k=1:length(col_exch_blocks)
n_cond = n_cond + length(find(xX.iH==col_exch_blocks(k)));
end
end
end
end
use_half_permutations = 0;
% check if sample size is equal for both conditions
if n_cond == 2
try
% repated Anova or F-test don't allow to use only half of the permutions
if repeated_anova || strcmp(xCon.STAT,'F')
use_half_permutations = 0;
elseif sum(n_data_cond(c0==exch_blocks(1))) == sum(n_data_cond(c0==exch_blocks(2)))
use_half_permutations = 1;
end
end
end
ind_exch_blocks = cell(n_exch_blocks,1);
for j=1:n_exch_blocks
if strcmp(xCon.STAT,'T')
ind_exch_blocks{j} = find(c0==exch_blocks(j));
else
ind_exch_blocks{j} = ind_X(j);
end
end
fprintf('\n');
% check design
interaction_design = false;
switch n_cond
case 0 % correlation
label = 1:n_data;
% we have to correct for some F-contrasts (i.e. effects of interest
% with eyes)
if F_contrast_multiple_rows && is_eoi
is_one = find(any(c0'));
for j=1:numel(is_one)
ind_exch_blocks{j} = is_one(j);
end
ind_exch_blocks = ind_exch_blocks';
end
if n_exch_blocks >= 2 && any(diff(exch_blocks(:))) % # exch_blocks >1 & differential contrast
fprintf('Interaction design between two or more regressors found\n')
interaction_design = true;
% remove all entries where contrast is not defined
% this does not work for all data CG 20200829
% label(all(xX.X(:,ind_X)==0,2)) = [];
else
if repeated_anova
fprintf('Repeated Anova with contrast for covariate found\n');
else
fprintf('Multiple regression design found\n');
end
end
case 1 % one-sample t-test
fprintf('One sample t-test found\n');
% use exchangeability blocks for labels
label = zeros(1,n_data);
for j=1:n_exch_blocks
for k=1:length(ind_exch_blocks{j})
label(xX.X(:,ind_exch_blocks{j}(k))~=0) = j;
end
end
otherwise % Anova with at least 2 groups
if repeated_anova
fprintf('Repeated Anova found\n');
else
fprintf('Anova found\n');
end
% use exchangeability blocks for labels
label = zeros(1,n_data);
for j=1:n_exch_blocks
for k=1:length(ind_exch_blocks{j})
label(xX.X(:,ind_exch_blocks{j}(k))~=0) = j;
end
end
end
fprintf('\n')
% get index for label values > 0
ind_label = find(label > 0);
n_data_with_contrast = length(ind_label);
% estimate # of permutations
% Anova/correlation: n_perm = (n1+n2+...+nk)!/(n1!*n2!*...*nk!)
if n_cond ~=1 % Anova/correlation
n_perm_full = factorial(n_data_with_contrast);
single_subject = 0;
for i=1:n_cond
% check whether only a single subject is in one group
if length(find(label == i)) == 1
single_subject = 1;
end
n_perm_full = n_perm_full/factorial(length(find(label == i)));
end
if isnan(n_perm_full)
% correct number of permutations for large samples when factorial is not working
if (n_cond == 2) && (single_subject == 1)
n_perm_full = n_data_with_contrast;
else
n_perm_full = realmax;
end
end
% find where data are defined for that contrast
if ~isempty(find(xX.iH == ind_X(1), 1))
% first checking whether contrasts are defined for iH
ind_data_defined = find(any(xX.X(:,xX.iH(ind_X)),2));
else
ind_data_defined = find(any(xX.X(:,ind_X),2));
end
% correct ind_label and n_data_with_contrast using ind_data_defined
ind_label = ind_data_defined';
n_data_with_contrast = length(ind_label);
% and restrict exchangeability block labels to those rows
exch_block_labels_data_defined = exch_block_labels(ind_data_defined);
% Repated Anova: n_perm = n_cond1!*n_cond2!*...*n_condk!
% for a full model where each condition is defined for all subjects the easier
% estimation is: n_perm = (n_cond!)^n_subj
% check that no regression analysis inside repeated anova is used
if repeated_anova && n_cond~=0
n_subj = max(exch_block_labels_data_defined);
n_perm_full = 1;
for k=1:n_subj
n_cond_subj = length(find(exch_block_labels_data_defined == k));
n_perm_full = n_perm_full*factorial(n_cond_subj);
end
else
n_perm_full = round(n_perm_full);
end
else % one-sample t-test: n_perm = 2^n
n_perm_full = 2^n_data_with_contrast;
exch_block_labels_data_defined = exch_block_labels;
ind_data_defined = ind_label;
end
% sometimes for F-tests with multiple independent rows the design cannot be fully recognized
% and # of permutations is wrong
if n_perm_full == 1 && F_contrast_multiple_rows
fprintf('ERROR: This F-contrast and type of design with multiple independent rows is not yet supported.\n');
return
end
if n_perm_full < n_perm
fprintf('Warning: Maximum number of possible permutations is lower than defined number of permutations: %d\n',n_perm_full);
end
n_perm = min([n_perm n_perm_full]);
fprintf('Number of permutations: %d\n',n_perm);
if use_half_permutations
fprintf('Equal sample sizes: Use half the number of permutations.\n');
end
fprintf('Exchangeability block/variable: ');
fprintf('%d ',unique(cell2mat(ind_exch_blocks)));
fprintf('\n');
fprintf('# of conditions: %d\n',n_cond);
% Guttman partioning of design matrix into effects of interest X and nuisance variables Z
X = xX.X(:,ind_X);
ind_Z = [xX.iH xX.iC xX.iB xX.iG];
ind_Z(ind_X) = [];
Z = xX.X(:,ind_Z);
Hz = Z*pinv(Z);
Rz = eye(size(X,1)) - Hz;
% if Hz is zero or Ic is empty then no confounds were found and we can skip the time-consuming
% Freedman-Lane permutation
if (all(~any(Hz)) || isempty(xX.iC)) || all(~any(diff(Hz))) || (interaction_design && numel(xX.iC) == numel(ind_X))
exist_nuisance = false;
else
exist_nuisance = true;
end
if ~exist_nuisance && nuisance_method > 0
fprintf('No nuisance variables were found: Use Draper-Stoneman permutation.\n\n');
nuisance_method = 0;
end
if nuisance_method > 0 && repeated_anova
fprintf('Use Draper-Stoneman permutation for repeated measures Anova.\n\n');
nuisance_method = 0;
end
switch nuisance_method
case 0
str_permutation_method = 'Draper-Stoneman';
case 1
str_permutation_method = 'Freedman-Lane';
case 2
str_permutation_method = 'Smith';
end
% name of contrast
c_name0 = deblank(xCon.name);
if test_mode
c_name = '';
else
c_name = sprintf('%s (E=%1.1f H=%1.1f %s) ',c_name0, E, H, str_permutation_method);
end
if ~test_mode
% compute unpermuted t/F-map
if voxel_covariate
% check which pinv-method is faster and use that one for all permutations
X = xX.W*xX.X;
tstart1 = tic;
for i=1:20, pX = pinv(X); end
telapsed1 = toc(tstart1);
tstart2 = tic;
for i=1:20, pX = pinv2(X); end
telapsed2 = toc(tstart2);
if 1.1*telapsed2 < telapsed1
pinv_method = 2;
fprintf('Use faster pinv2 function\n');
else
pinv_method = 1;
end
[t0, df2] = calc_GLM_voxelwise(Y,xX,SPM.xC(voxel_covariate),xCon,ind_mask,VY(1).dim,C,[],ind_X,pinv_method);
else
[t0, df2] = calc_GLM(Y,xX,xCon,ind_mask,VY(1).dim);
end
df1 = size(xCon.c,2);
% transform to z statistic
if convert_to_z
% use faster z-transformation of SPM for T-statistics
if strcmp(xCon.STAT,'T')
t0 = spm_t2z(t0,df2);
else
t0 = palm_gtoz(t0,df1,df2);
end
end
mask_0 = (t0 == 0);
mask_1 = (t0 ~= 0);
mask_P = (t0 > 0);
mask_N = (t0 < 0);
mask_NaN = (mask == 0);
found_P = sum(mask_P(:)) > 0;
found_N = sum(mask_N(:)) > 0;
% remove all NaN and Inf's
t0(isinf(t0) | isnan(t0)) = 0;
% sometimes z-transformation produces neg. values even for F-statistics
if strcmp(xCon.STAT,'F')
t0(t0 < 0) = 0;
end
% get parametric p-values for comparison
tname = sprintf('spm%s_%04d',xCon.STAT,Ic);
tname = fullfile(cwd,[tname file_ext]);
if ~exist(tname,'file') && ~voxel_covariate
spm_contrasts(SPM,Ic);
end
if ~voxel_covariate
Z0 = spm_data_read(tname);
Pt = zeros(size(Z0));
if strcmp(xCon.STAT,'T')
if found_P
Pt(mask_P) = 1-spm_Tcdf(Z0(mask_P),df2);
else
Pt(mask_N) = spm_Tcdf(Z0(mask_N),df2)-1;
end
else
if found_P
Pt(mask_P) = 1-spm_Fcdf(Z0(mask_P),[df1, df2]);
else
Pt(mask_N) = spm_Fcdf(Z0(mask_N),[df1, df2])-1;
end
end
% Check correlation between parametric and non-parametric T/F-values.
% Low correlation points to issues with image mask and we have to use a
cc = corrcoef(Z0(:),t0(:));
mask_shared = Z0 ~= 0;
if cc(1,2) < 0.85 && isempty(job.mask)
% check whether mask size differes and create a shared mask
if sum(Z0(:) ~= 0) ~= sum(t0(:) ~= 0)
fprintf('\nWARNING: Large discrepancy between parametric and non-parametric statistic found (cc=%g) which either points to different image masks or to missing absolute threshold for VBM analysis.\n',cc(1,2));
mask_shared = Z0 ~= 0 & t0 ~=0;
else
fprintf('\nWARNING: Large discrepancy between parametric and non-parametric statistic found (cc=%g) which is likely due to creating parametric statistics in fMRI mode, which slightly handles noise differently.\n',cc(1,2));
end
end
else
mask_shared = ones(size(t0));
end
% get dh for unpermuted map
dh = max(abs(t0(:)))/n_steps_tfce;
% calculate tfce of unpermuted t-map
if mesh_detected
if ~isa(SPM.xVol.G,'gifti')
% check whether path is correct and file exist
if ~exist(SPM.xVol.G,'file')
[pathG,nameG,extG] = spm_fileparts(SPM.xVol.G);
% use new path
if ~isempty(strfind(pathG,'_32k'))
SPM.xVol.G = fullfile(fileparts(which('cat12')),'templates_surfaces_32k',[nameG extG]);
else
SPM.xVol.G = fullfile(fileparts(which('cat12')),'templates_surfaces',[nameG extG]);
end
end
SPM.xVol.G = gifti(SPM.xVol.G);
end
tfce0 = tfce_mesh(SPM.xVol.G.faces, t0, dh, E, H)*dh;
else
% measure computation time to test whether multi-threading causes issues
% start with single-threading for unpermuted data
tstart = tic;
% only estimate neg. tfce values for non-positive t-values
if found_N
tfce0 = tfceMex_pthread(t0,dh,E,H,1,1)*dh;
else
tfce0 = tfceMex_pthread(t0,dh,E,H,0,1)*dh;
end
telapsed = toc(tstart);
end
% prepare output files
Vt = VY(1);
Vt.dt(1) = 16;
Vt.pinfo(1) = 1;
%---------------------------------------------------------------
% save unpermuted t map
%---------------------------------------------------------------
name = sprintf('%s_%04d',xCon.STAT,Ic);
Vt.fname = fullfile(cwd,[name file_ext]);
Vt.descrip = sprintf('%s %04d %s',xCon.STAT,Ic, str_permutation_method);
Vt = spm_data_hdr_write(Vt);
spm_data_write(Vt,t0);
%---------------------------------------------------------------
% save unpermuted TFCE map
%---------------------------------------------------------------
name = sprintf('TFCE_%04d',Ic);
Vt.fname = fullfile(cwd,[name file_ext]);
Vt.descrip = sprintf('TFCE %04d %s',Ic, str_permutation_method);
Vt = spm_data_hdr_write(Vt);
spm_data_write(Vt,tfce0);
% get largest tfce
tfce0_max = max(tfce0(:));
t0_max = max(t0(:));
tfce0_min = min(tfce0(:));
t0_min = min(t0(:));
% prepare countings
tperm = zeros(size(t));
tfceperm = zeros(size(t));
t_min = [];
t_max = [];
t_max_th = [];
tfce_min = [];
tfce_max = [];
tfce_max_th = [];
end % test_mode
% general initialization
try % use try commands to allow batch mode without graphical output
Fgraph = spm_figure('GetWin','Graphics');
spm_figure('Clear',Fgraph);
figure(Fgraph)
h = axes('position',[0.45 0.95 0.1 0.05],'Units','normalized','Parent',...
Fgraph,'Visible','off');
text(0.5,0.6,c_name,...
'FontSize',spm('FontSize',10),...
'FontWeight','Bold',...
'HorizontalAlignment','Center',...
'VerticalAlignment','middle')
text(0.5,0.25,spm_str_manip(spm_fileparts(job.data{1}),'a80'),...
'FontSize',spm('FontSize',8),...
'HorizontalAlignment','Center',...
'VerticalAlignment','middle')
end
% check that label has correct dimension
sz = size(label);
if sz(1)>sz(2)
label = label';
end
stopStatus = false;
if ~test_mode, tfce_progress('Init',n_perm,'Calculating','Permutations'); end
% update interval for progress bar
progress_step = max([1 round(n_perm/100)]);
% Regression design found where contrast is defined for covariate?
if ~isempty(xX.iC) && all(ismember(ind_X,SPM.xX.iC))
ind_label_gt0 = find(label(ind_data_defined) > 0);
else
ind_label_gt0 = find(label > 0);
end
unique_labels = unique(label(ind_label_gt0));
n_unique_labels = length(unique_labels);
perm = 1;
check_validity = false;
while perm<=n_perm
% randomize subject vector
if perm==1 % first permutation is always unpermuted model
if n_cond == 1 % one-sample t-test
rand_label = ones(1,n_data_with_contrast);
label_matrix = rand_label;
else % correlation or Anova