JC_FCpreproSetup.m

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%		CREATED BY:		JASON CRAGGS
%		CREATED ON:		2017-12-07
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%		USAGE:    PREPARING/CLEANING THE FUNCTIONAL DATASETS FOR THE
%                   FUNCTIONAL CONNECTIVITY ANALYSES
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%         MODIFIED ON:	  2017_12_08
%    	MODIFIED ON:	 2017_12_12
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%           SETUP FOR A BATCH PROCESSING
%           THIS MUST BE RUN FROM ROOT FOLDER
%
%           rpfiles = rp-*.txt files contain motion correction parameters
%                     Columns 1-3 are X/Y/Z movement in mm.
%                     Columns 4-6 are rotation around X/Y/Z in radians.
%           wc* = TISSUE CLASSES FROM THE STRUCTURAL DATA
%                   c1/c2/c3 images are the first three "tissue classes"
%                   (GM/WM/CSF) from the segmentation
%                   r = realigned
%                   w = normalized
%           swa = THE FUNCTIONAL DATA FROM EACH RUN
%                   a = slice time corrected
%                   s = smoothed
%                   w = normalized
%
%              OUTPUT FILES
%                   The function will make a copy of (each of) the input file(s)
%                   with a prefix of "fc" (e.g. "fcswraRUN.nii").
%
n = neuroelf;


%rootpath = '/Volumes/Data/Imaging/R01/preprocessed/_Jason_0/';
%rootpath = '/Volumes/Data/Imaging/R01/preprocessed/_Jason/';
rootpath = '/Volumes/Data/Imaging/R01/preprocessed/';
subpattern = 'Sub*_v*';

% find subjects in root folder
dirinfo = dir([rootpath subpattern]);
subjlist = {dirinfo.name};
%
% pick subject according to job number
for sc = 1:numel(subjlist)

    % set primary path
    primary_path = [rootpath subjlist{sc} filesep];
    cd(primary_path);

     rpfiles = n.findfiles([pwd], 'rp*.txt', '-d1');
     wc1 = n.findfiles([pwd], 'wc1*.nii', '-d1');
     wc2 = n.findfiles([pwd], 'wc2*.nii', '-d1');
     wc3 = n.findfiles([pwd], 'wc3*.nii', '-d1');
     swa = n.findfiles([pwd], 'sw*.nii', '-d1');

%
%           THE WC FILES NEED TO BE REFORMATTED TO MAINTAIN THE SAME
%           DIMENSIONS AS THE ARRAY IN THE RP FILE
     wc1 = repmat(wc1', 4, 1);
     wc2 = repmat(wc2', 4, 1);
     wc3 = repmat(wc3', 4, 1);
%
%           RUNNING THE JC_FCprepro SCRIPT USING THE ABOVE VARIABLES
      for c=1:4,  %   THE NUMBER OF FUNCTIONAL RUNS
          JC_FCprepro(swa{c}, rpfiles{c}, 120/2.8, {wc1{c}; wc2{c}; wc3{c}});
     end
end
%           SETUP FOR MULTIPLE SUBJECTS
% n = neuroelf;
% rpfiles = n.findfiles([pwd '/Sub004*'], 'rp*.txt', '-d1');
% wc1 = n.findfiles([pwd '/Sub*'], 'wc1*.nii', '-d1');
% wc2 = n.findfiles([pwd '/Sub*'], 'wc2*.nii', '-d1');
% wc3 = n.findfiles([pwd '/Sub*'], 'wc3*.nii', '-d1');
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%         END OF SCRIPT