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catQC_NARPS_masks.m
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catQC_NARPS_masks.m
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clear, clc; warning off all
%--------------- readout of path-to-data ----------------------------
% datadir = fullfile('/data/BnB_TEMP/Data_NARPS/Derivatives/');
datadir = fullfile('/data/BnB_USER/Langner/NARPS/analysis/SPM/SingleSubjectAnalysis/NARPS_gain1st');
%--------------- select sample directory containing preprocessed data ----------------------------
inpt = spm_input('Input directories or Lookupfile?','!+1','b','Directory|Lookup.mat',[1 2],1,'Select mode');
% --------------- select type of data to do covariance analysis ----------------------------
data = spm_input('Choose data to QC ->','!+1','b','mri|surf|NARPS_gain1st|MGT2|MGT3|MGT4',[1 2 3 4 5 6],1,'Select mode');
datafolder = {'' '' '' '' '' ''};
datatype = {'CAT/12.5' 'CAT/12.5' '' 'MGT2' 'MGT3' 'MGT4'};
cnt = 0; Pcnt = 0;
%--------------- Gap between voxels to be considered in covariance: default = 3 -------------------
%--------------- lower values take very long -------------------
if data ~= 2
matlabbatch{1}.spm.tools.cat.tools.check_cov.gap = 2;
end
if inpt == 1
% Dirs = spm_select(1,'dir','Select sample directory to check homogeniety','',fullfile(datadir,datatype{data}));
Dirs = fullfile(datadir,datatype{data});
Di = dir(fullfile(Dirs,datafolder{data},'*')); Di = Di([Di.isdir]); Di = Di(3:end);
for i=1:size(Di,1)
subjects{i} = fullfile(Dirs,datafolder{data},Di(i).name);
xsub{i} = Di(i).name;
end
elseif inpt == 2
Dirs = spm_select(1,'.*\.mat$','Select Lookup file to check homogeniety','');
load(Dirs);
cova = spm_input('Include nuisance variable?','!+1','b','--|Age|ICV|GMV|FD',[0 1 2 3 4],1,'Select mode');
for i=1:numel(sub)
subjects{i} = fullfile(datadir,datatype{data},SubDir{i},datafolder{data},sub{i});
end
xsub = sub;
end
for sub = 1:numel(xsub)
% ----- define files checke covariance: 3D = modulated / RS = normalized -----
datafile = {['mri/m0wp1' xsub{sub} '.nii'] ['surf/mesh.thickness.resampled_32k.' xsub{sub} '.gii'] ...
['mask.nii'] ['w' xsub{sub} '_meanEPI.nii'] ['w' xsub{sub} '_meanEPI.nii'] ['w' xsub{sub} '_meanEPI.nii']};
% ['wu' xsub{sub} '_sbref.nii'] ['wu' xsub{sub} '_sbref.nii'] ['wu' xsub{sub} '_sbref.nii'] ['wu' xsub{sub} '_sbref.nii']};
if inpt == 1
if exist(fullfile(subjects{sub},datafile{data}),'file')
cnt = cnt+1;
if data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.data_surf{1}{cnt,1} = ...
fullfile(subjects{sub},datafile{data});
else
matlabbatch{1}.spm.tools.cat.tools.check_cov.data_vol{1}{cnt,1} = ...
fullfile(subjects{sub},datafile{data});
end
% ----- for cat preprocessing include quality parameters in QC -----
if data == 1
matlabbatch{1}.spm.tools.cat.tools.check_cov.data_xml{cnt,1} = ...
fullfile(subjects{sub},'report',['cat_' xsub{sub} '.xml']);
elseif data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.data_xml{cnt,1} = ...
fullfile(subjects{sub},'report',['cat_' xsub{sub} '.xml']);
end
else
fprintf(1,'%s\n',[datafolder{data} ' missing for ' xsub{sub}]);
end
elseif inpt == 2
if exist(fullfile(subjects{sub},datafile{data}),'file')
if isPat(sub) == 0
cnt = cnt+1;
if data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.data_surf{1}{cnt,1} = ...
fullfile(subjects{sub},datafile{data});
else
matlabbatch{1}.spm.tools.cat.tools.check_cov.data_vol{1}{cnt,1} = ...
fullfile(subjects{sub},datafile{data});
end
% ----- for cat data include quality parameters in QC -----
if data == 1
matlabbatch{1}.spm.tools.cat.tools.check_cov.data_xml{cnt,1} = ...
fullfile(subjects{sub},'report',['cat_' xsub{sub} '.xml']);
elseif data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.data_xml{cnt,1} = ...
fullfile(subjects{sub},'report',['cat_' xsub{sub} '.xml']);
end
% ----- gather nuisance variables for controls -----
if cova == 1
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'age')==1));
elseif cova == 2
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'icv')==1));
elseif cova == 3
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'gmv')==1));
elseif cova == 4
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'fd')==1));
end
elseif isPat(sub) == 1
Pcnt = Pcnt+1;
if data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.data_surf{2}{Pcnt,1} = ...
fullfile(subjects{sub},datafile{data});
else
matlabbatch{1}.spm.tools.cat.tools.check_cov.data_vol{2}{Pcnt,1} = ...
fullfile(subjects{sub},datafile{data});
end
% ----- for cat data include quality parameters in QC -----
if data == 1
matlabbatch{1}.spm.tools.cat.tools.check_cov.data_xml{Pcnt+cnt,1} = ...
fullfile(subjects{sub},'report',['cat_' xsub{sub} '.xml']);
elseif data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.data_xml{Pcnt+cnt,1} = ...
fullfile(subjects{sub},'report',['cat_' xsub{sub} '.xml']);
end
% ----- gather nuisance variables for patients -----
if cova == 1
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'age')==1));
elseif cova == 2
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'icv')==1));
elseif cova == 3
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'gmv')==1));
elseif cova == 4
c(Pcnt+cnt,1) = Cov(sub,find(strcmpi(Covariates,'dvars')==1));
end
end
else
fprintf(1,'%s\n',[datafolder{data} ' missing for ' xsub{sub}]);
end
end
end
%--------------- with dircectory as input no nuisance variables are considered ----------------------------
if inpt == 1 || cova == 0
if data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.c = {};
else
matlabbatch{1}.spm.tools.cat.tools.check_cov.c = {};
end
fprintf(1,'\n%s\n',[num2str(cnt) ' of ' num2str(sub) ' subjects found with ' datafolder{data}]);
else
if data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.c{1} = c(:);
else
matlabbatch{1}.spm.tools.cat.tools.check_cov.c{1} = c(:);
end
fprintf(1,'\n%s\n',[num2str(cnt) ' controls and ' num2str(Pcnt) ' patients found with ' datafolder{data}]);
end
try
spm_jobman('run',matlabbatch)
catch
if data == 1
matlabbatch{1}.spm.tools.cat.tools.check_cov.data_xml = {};
spm_jobman('run',matlabbatch)
elseif data == 2
matlabbatch{1}.spm.tools.cat.stools.check_mesh_cov.data_xml = {};
end
end