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setupSPM.m
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setupSPM.m
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function setupSPM(subject, workdir, results_folder, labels, cwd, TR)
addpath /data/BnB2/TOOLS/spm12
work_dir=fullfile(results_folder,subject);
mkdir(work_dir); delete([work_dir '/*.*']);
datarun=[];
% xrun = dir(['Data/' subject '/Orig/*']);
%
% cnt = 1;
% runs = {};
% for i=3:numel(xrun)
% if xrun(i).isdir
% runs{cnt} = xrun(i).name;
% cnt = cnt+1;
% end
% end
% runs = {runs{[1 2 3 4 5 6 7]}}; % alle 7 Sessions (Reihenfolge: alphabetisch nach Beding.namen)
% runs = {runs{[2 3 4 5 6 7]}}; % 6 Sessions (ohne erste) (Reihenfolge: alphabetisch nach Beding.namen)
% runs = {runs{[7]}}; % alle Sessions einzeln
runs = labels;
runs = {runs{[1 2 3 4]}};
nruns = numel(runs);
%for j = 1:nruns; datarun{j}=fullfile(workdir, 'Derivatives',subject,smoothedfolder,runs{j}); end
%for j = 1:nruns; datarun{j}=fullfile(workdir, 'Derivatives',runs{j},subject,smoothedfolder); end % bei 3-D Niftis
for j = 1:nruns;
datarun{j}=fullfile(workdir, 'Derivatives', runs{j},subject);
end % bei 4-D Nifti
clear soas
%load(fullfile('S:\D\Arbeit\ExtKoop\NARPS\data\Behavior\Logfiles\SOAfiles\',[subject '.mat']));
load(fullfile(workdir, '/Behavior/Logfiles/SOAfiles/', [subject '.mat']));
for i=1:nruns
indx = nan(1,numel(labels));
Labs = {};
for ii=1:numel(labels)
for iii=1:numel(soas)
try
if strcmp(soas(iii).name{1},labels{ii})
indx(ii) = iii;
Labs{ii} = [labels{ii}];
end
end
end
end
% SESS{i,1} = Labs;
% SESS{i,2} = indx;
SESS{i,1} = Labs{i};
SESS{i,2} = indx(i);
end
if any(isnan([SESS{:,2}]))
huhu Error!!
end
clear nscans
% P = [];
% for run = 1:nruns
% [Files,Dirs] = spm_list_files(datarun{run},'s*.img');
% P = [P; [repmat([datarun{run} filesep],size(Files,1),1) Files]];
% nscans(run) = size(Files,1);
% end;
%size(spm_select('expand', fullfile('S:\D\Arbeit\ExtKoop\NARPS\data\fMRI\MGT1\sub-001\','s8wsub-001.nii')),1)
P = '';
for run = 1:nruns
%files = dir(fullfile(datarun{run},'s*.nii')); % 3-D Niftis
files = spm_select('expand', fullfile(datarun{run},['s5w' subject '.nii'])); % 4-D Nifti
%P = [P; [repmat([datarun{run} filesep], size(files,1), 1) files(i).name]]; % 3-D Niftis
P = [P; files];
nscans(run) = size(files,1);
end
clear SPM; SPM = struct('nscan',nscans);
SPM.xY.P = P;
SPM.xY.RT = TR;
SPM.xBF.UNITS = 'secs'; SPM.xBF.name = 'hrf (with time derivative)'; % Option: 'hrf'
SPM.xBF.T = 16; SPM.xBF.T0 = 8; SPM.xBF.dt = SPM.xY.RT/SPM.xBF.T;
SPM.xBF.length = 32.2; SPM.xBF.order = 2; SPM.xBF = spm_get_bf(SPM.xBF); SPM.xBF.Volterra = 1;
% QAfile = dir(fullfile(workdir, 'Derivatives',subject,'Orig','QA.mat'));
% load([workdir, 'Derivatives', subject, filesep, 'Orig', filesep, QAfile(1).name]);
% % qaruns = [6 8 4 9 5 10]; % Re-Gruppierung (zu alphabetisch) der Run-Reihenfolge aus der Qualitaetsanylase AQUA
% (Original-Reihenfolge laut Script aqua_ale:
% ap_simult00, avp_mix0000, av_simult00, kontr_piezo, piezo000000, audi0000000, avp_simult0, kontr_audi0,...
% kontr_visu0, visu0000000)
% qaruns = [1 2 3 4 5 6 7];
for ses = 1:numel(nscans)
SPM.Sess(ses).U.ons = soas(SESS{ses,2}).ons;
SPM.Sess(ses).U.dur = soas(SESS{ses,2}).dur;
SPM.Sess(ses).U.name = {SESS{ses,1}};
%if numel(soas(SESS{ses,2}(xi)).par2')==0
if numel(soas(SESS{ses,2}).par1')==0
SPM.Sess(ses).U.P(1).name = 'none';
else
SPM.Sess(ses).U.P(1).name = 'Gain';
SPM.Sess(ses).U.P(1).P = soas(SESS{ses,2}).par1';
SPM.Sess(ses).U.P(1).h = 1; % 1 = lin. Modulation
end
if numel(soas(SESS{ses,2}).par2')==0
SPM.Sess(ses).U.P(2).name = 'none';
else
SPM.Sess(ses).U.P(2).name = 'Loss';
SPM.Sess(ses).U.P(2).P = soas(SESS{ses,2}).par2';
SPM.Sess(ses).U.P(2).h = 1; % 1 = lin. Modulation
end
if numel(soas(SESS{ses,2}).par3')==0
SPM.Sess(ses).U.P(3).name = 'none';
else
SPM.Sess(ses).U.P(3).name = 'RT';
SPM.Sess(ses).U.P(3).P = soas(SESS{ses,2}).par3';
SPM.Sess(ses).U.P(3).h = 1; % 1 = lin. Modulation
end
if any(SPM.Sess(ses).U.dur < 0)
err = 1;
elseif any(diff(round(SPM.Sess(ses).U.ons)) < 1) % war mal 5
err = 1;
end
rp = dir(fullfile(workdir, 'Derivatives',runs{ses},subject,'rp_*.txt'));
[r1,r2,r3,r4,r5,r6] = textread(fullfile(workdir, 'Derivatives',runs{ses},subject,rp.name),'%f%f%f%f%f%f');
Conf = load(fullfile(workdir, 'Derivatives',runs{ses},subject,['Counfounds_' subject '.mat']));
censTP = zeros(numel(r1),1);
censTP(Conf.badTP,1) = 1;
% if sum(censTP) > 0
% SPM.Sess(ses).C.C = []; % Modell ohne zusaetzliche Regressoren
SPM.Sess(ses).C.C = [r1,r2,r3,r4,r5,r6,censTP];
% SPM.Sess(ses).C.name = {}; % Modell ohne zusaetzliche Regressoren
SPM.Sess(ses).C.name = {'x', 'y', 'z', 'yaw', 'pitch', 'roll', 'badTP'};
% else
% % SPM.Sess(ses).C.C = []; % Modell ohne zusaetzliche Regressoren
% SPM.Sess(ses).C.C = [r1,r2,r3,r4,r5,r6];
% % SPM.Sess(ses).C.name = {}; % Modell ohne zusaetzliche Regressoren
% SPM.Sess(ses).C.name = {'x', 'y', 'z', 'yaw', 'pitch', 'roll'};
% end
end
SPM.xGX.iGXcalc = 'none';
% SPM.xVi.form = 'none';
SPM.xVi.form = 'AR(1) + w';
SPM.xX.K(1).HParam = 128;
cd(work_dir);
SPM = spm_fmri_spm_ui(SPM);
save SPM.mat SPM;
cd(cwd)
end