GSP_Script_Preprocessing_Task.m
b8089f5f662emaster

GSP_Script_Preprocessing_Task.m
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			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
			% Authors: Thomas Bolton and Mert Inan
			% Date: 05/10/2017 -
			% Description:
			% This is supposed to be a script doing preprocessing on HCP data. The goal
			% is to preprocess both RS sessions (LR and RL) and task recordings for a
			% given subject, from the unzipping to the final matlab matrices
			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

			%% 1. Setting up path files and options

			% Index of the subject to preprocess
			HCP_index = 100408;

			% Adds the shared folder to do all we want: SPM12, DPARSFA files,
			% preprocessing routines...
			PathUtils = '/Volumes/Lorena/GSP_HCP_Test_Subject/Utilities';
			addpath(genpath(PathUtils));

			% Path towards the data
			PathToData = ['/Volumes/Lorena/GSP_HCP_Test_Subject/',num2str(HCP_index)];


			% Path of the atlas that is going to be used by the mapVolumeToVolume
			% function.
			atlasDir = '/Volumes/Lorena/GSP_HCP_Test_Subject/Atlas';

			% Name of the atlas to use for atlasing
			Atlas = {'Craddock_950.nii','Craddock_300.nii'};
			Atlas_suffix = {'_950','_300'};

			% Number of tasks and task names, as well as number of NIFTi volumes
			n_tasks = 7;
			task_names = {'TASK_WM','TASK_GAMBLING','TASK_MOTOR','TASK_LANGUAGE',...
			'TASK_SOCIAL','TASK_RELATIONAL','TASK_EMOTION'};

			acqu_dir_names = {'LR','RL'};


			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
			n_task_volumes = [405];
			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
			%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


			% Unzipping the files downloaded from the HCP website into a common folder;
			% they all have the same name, so I must unzip them in appropriate order to
			% make sure that I do things properly

			% 1. Structural
			disp(['Creating structural data for subject ',num2str(HCP_index),'...']);
			unzip([num2str(HCP_index),'_3T_Structural_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'Structural');
			cd('Structural');
			copyfile('T1w/T1w_acpc_dc_restore_brain.nii.gz',pwd);
			rmdir('T1w','s');
			rmdir('release-notes','s');
			rmdir('MNINonLinear','s');
			cd('..');
			cd('..');

			% 2. Diffusion
			disp(['Creating diffusion data for subject ',num2str(HCP_index),'...']);
			unzip([num2str(HCP_index),'_3T_Diffusion_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'Diffusion');
			cd('Diffusion');
			copyfile('T1w/Diffusion/bvals',pwd);
			copyfile('T1w/Diffusion/bvecs',pwd);
			copyfile('T1w/Diffusion/data.nii.gz',pwd);
			copyfile('T1w/Diffusion/nodif_brain_mask.nii.gz',pwd);
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');


			% 5. Task1 (working memory)
			disp(['Creating task (WM) data for subject ',HCP_index,'...']);
			unzip([num2str(HCP_index),'_3T_tfMRI_WM_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'TASK_WM');

			cd('TASK_WM');
			copyfile('MNINonLinear/Results/tfMRI_WM_LR/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_WM_LR.txt');
			copyfile('MNINonLinear/Results/tfMRI_WM_LR/tfMRI_WM_LR.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_WM_LR/EVs',fullfile(pwd,'EVs_WM_LR'));

			copyfile('MNINonLinear/Results/tfMRI_WM_RL/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_WM_RL.txt');
			copyfile('MNINonLinear/Results/tfMRI_WM_RL/tfMRI_WM_RL.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_WM_RL/EVs',fullfile(pwd,'EVs_WM_RL'));

			rmdir('MNINonLinear','s');
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');

			% 6. Task2 (gambling)
			disp(['Creating task (gambling) data for subject ',HCP_index,'...']);
			unzip([num2str(HCP_index),'_3T_tfMRI_GAMBLING_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'TASK_GAMBLING');

			cd('TASK_GAMBLING');
			copyfile('MNINonLinear/Results/tfMRI_GAMBLING_LR/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_GAMBLING_LR.txt');
			copyfile('MNINonLinear/Results/tfMRI_GAMBLING_LR/tfMRI_GAMBLING_LR.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_GAMBLING_LR/EVs',fullfile(pwd,'EVs_GAMBLING_LR'));

			copyfile('MNINonLinear/Results/tfMRI_GAMBLING_RL/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_GAMBLING_RL.txt');
			copyfile('MNINonLinear/Results/tfMRI_GAMBLING_RL/tfMRI_GAMBLING_RL.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_GAMBLING_RL/EVs',fullfile(pwd,'EVs_GAMBLING_RL'));

			rmdir('MNINonLinear','s');
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');


			% 7. Task3 (motor)
			unzip([num2str(HCP_index),'_3T_tfMRI_MOTOR_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'TASK_MOTOR');

			cd('TASK_MOTOR');
			copyfile('MNINonLinear/Results/tfMRI_MOTOR_LR/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_MOTOR_LR.txt');
			copyfile('MNINonLinear/Results/tfMRI_MOTOR_LR/tfMRI_MOTOR_LR.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_MOTOR_LR/EVs',fullfile(pwd,'EVs_MOTOR_LR'));

			copyfile('MNINonLinear/Results/tfMRI_MOTOR_RL/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_MOTOR_RL.txt');
			copyfile('MNINonLinear/Results/tfMRI_MOTOR_RL/tfMRI_MOTOR_RL.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_MOTOR_RL/EVs',fullfile(pwd,'EVs_MOTOR_RL'));

			rmdir('MNINonLinear','s');
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');

			% 8. Task 4 (language)
			unzip([num2str(HCP_index),'_3T_tfMRI_LANGUAGE_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'TASK_LANGUAGE');

			cd('TASK_LANGUAGE');
			copyfile('MNINonLinear/Results/tfMRI_LANGUAGE_LR/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_LANGUAGE_LR.txt');
			copyfile('MNINonLinear/Results/tfMRI_LANGUAGE_LR/tfMRI_LANGUAGE_LR.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_LANGUAGE_LR/EVs',fullfile(pwd,'EVs_LANGUAGE_LR'));

			copyfile('MNINonLinear/Results/tfMRI_LANGUAGE_RL/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_LANGUAGE_RL.txt');
			copyfile('MNINonLinear/Results/tfMRI_LANGUAGE_RL/tfMRI_LANGUAGE_RL.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_LANGUAGE_RL/EVs',fullfile(pwd,'EVs_LANGUAGE_RL'));

			rmdir('MNINonLinear','s');
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');

			% 9. Task 5 (social)
			unzip([num2str(HCP_index),'_3T_tfMRI_SOCIAL_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'TASK_SOCIAL');

			cd('TASK_SOCIAL');
			copyfile('MNINonLinear/Results/tfMRI_SOCIAL_LR/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_SOCIAL_LR.txt');
			copyfile('MNINonLinear/Results/tfMRI_SOCIAL_LR/tfMRI_SOCIAL_LR.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_SOCIAL_LR/EVs',fullfile(pwd,'EVs_SOCIAL_LR'));

			copyfile('MNINonLinear/Results/tfMRI_SOCIAL_RL/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_SOCIAL_RL.txt');
			copyfile('MNINonLinear/Results/tfMRI_SOCIAL_RL/tfMRI_SOCIAL_RL.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_SOCIAL_RL/EVs',fullfile(pwd,'EVs_SOCIAL_RL'));

			rmdir('MNINonLinear','s');
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');

			% 10. Task 6 (relational)
			unzip([num2str(HCP_index),'_3T_tfMRI_RELATIONAL_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'TASK_RELATIONAL');

			cd('TASK_RELATIONAL');
			copyfile('MNINonLinear/Results/tfMRI_RELATIONAL_LR/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_RELATIONAL_LR.txt');
			copyfile('MNINonLinear/Results/tfMRI_RELATIONAL_LR/tfMRI_RELATIONAL_LR.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_RELATIONAL_LR/EVs',fullfile(pwd,'EVs_RELATIONAL_LR'));

			copyfile('MNINonLinear/Results/tfMRI_RELATIONAL_RL/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_RELATIONAL_RL.txt');
			copyfile('MNINonLinear/Results/tfMRI_RELATIONAL_RL/tfMRI_RELATIONAL_RL.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_RELATIONAL_RL/EVs',fullfile(pwd,'EVs_RELATIONAL_RL'));

			rmdir('MNINonLinear','s');
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');

			% 11. Task 7 (emotion)
			unzip([num2str(HCP_index),'_3T_tfMRI_EMOTION_preproc'],num2str(HCP_index));
			cd(num2str(HCP_index));
			movefile(num2str(HCP_index),'TASK_EMOTION');

			cd('TASK_EMOTION');
			copyfile('MNINonLinear/Results/tfMRI_EMOTION_LR/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_EMOTION_LR.txt');
			copyfile('MNINonLinear/Results/tfMRI_EMOTION_LR/tfMRI_EMOTION_LR.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_EMOTION_LR/EVs',fullfile(pwd,'EVs_EMOTION_LR'));

			copyfile('MNINonLinear/Results/tfMRI_EMOTION_RL/Movement_Regressors.txt',pwd);
			movefile('Movement_Regressors.txt','Movement_Regressors_EMOTION_RL.txt');
			copyfile('MNINonLinear/Results/tfMRI_EMOTION_RL/tfMRI_EMOTION_RL.nii.gz',pwd);
			copyfile('MNINonLinear/Results/tfMRI_EMOTION_RL/EVs',fullfile(pwd,'EVs_EMOTION_RL'));

			rmdir('MNINonLinear','s');
			rmdir('T1w','s');
			rmdir('release-notes','s');
			cd('..');
			cd('..');

			% How many scans do we discard due to magnetization
			removeFirstNscans = 10;

			% There are many more available RS volumes, but for the moment the reading
			% does not work past 594 for some obscure reason...
			n_RS_volumes = 594;


			%% 2. Unzipping data files and creating 3D NIFTI volumes
			disp('Unzipping the data and creating 3D NIFTI files...');

			% Structural data path towards the image to unzip
			structPath = fullfile(PathToData,'Structural');
			structGzDirs = dir(fullfile(structPath,'T1w_acpc_dc_restore_brain.nii.gz'));

			% Unzipping the structural volume if required
			if size(structGzDirs,1)
			disp('Unzipping structural files...');
			gzStructFile = fullfile(structPath, structGzDirs(1).name);
			gunzip(gzStructFile);
			end


			% Reads, for each task of the HCP, for both directions (LR and RL), the
			% gii.gz files, and write 3D NIFTI volumes from them
			for task = 1:n_tasks
			taskPath = fullfile(PathToData,task_names{task});
			taskGzDirs = dir(fullfile(taskPath,'*.nii.gz'));

			if size(taskGzDirs,1)
			disp('Unzipping task files...');

			for di = 1:2
			gzTaskFile = fullfile(taskPath, taskGzDirs(di).name);
			gunzip(gzTaskFile);

			gzTaskFile = fullfile(taskPath, taskGzDirs(di).name);
			gunzip(gzTaskFile);
			end
			end

			taskNiiDirs = dir(fullfile(taskPath,'*.nii'));

			for di = 1:2
			taskFile = fullfile(taskPath, taskNiiDirs(di).name);

			vols = spm_vol(taskFile);

			for j = 1:n_task_volumes(task)

			vol = vols(j);
			[path name ext] = fileparts(vol.fname);
			numberStr = ['00' int2str(j)];
			numberStr = numberStr(size(numberStr,2)-2:end);

			vol.fname = fullfile(path, ['f' name numberStr ext]);
			vol.n = [1 1];

			spm_write_vol(vol, spm_read_vols(vols(j)));
			end

			clear vols
			end
			end


			%% 3. Getting the motion information for RS and task runs
			disp('Creating proper motion regressors...');

			% For each task and both directions of acquisition, rewrites an updated
			% regressor file
			for task = 1:n_tasks

			taskPath = fullfile(PathToData,task_names{task});

			for di = 1:2

			movText = textread(fullfile(taskPath,['Movement_Regressors_',task_names{task},'_',acqu_dir_names{di},'.txt']));

			%Changes degrees to radians
			movText = movText(1:n_task_volumes(task),1:6);
			for l = 4:6
			movText(:,l) = movText(:,l)*pi/180;
			end

			dlmwrite(fullfile(taskPath,['Movement_Regressors_',task_names{task},'_',acqu_dir_names{di},'_Correct.txt']),movText,' ');
			end
			end


			%% 4. Getting WM and CSF masks (conservative DPARSFA ones)

			% Gets one volume only
			fMeanFN_noPath = spm_select('List',taskPath,['^' 'f' '.*\.nii$']);

			% Gets the path and the volume itself
			fMean_fn=fullfile(functPath,fMeanFN_noPath(1,:));

			% Transforms the WM and CSF masks from the toolbox into native
			% resolution
			Masks={'WhiteMask_09_121x145x121.nii','CsfMask_07_121x145x121.nii'};

			% I must convert the masks to the right resolution
			for j=1:length(Masks)
			tmp=which(Masks{j});

			if j == 1
			WMmaskLidx = mapVolumeToVolume_TB(tmp,fMean_fn);
			else
			CSFmaskLidx = mapVolumeToVolume_TB(tmp,fMean_fn);
			end
			end


			%% 5. Processing the data


			% For task data...
			for task = 1:n_tasks

			taskPath = fullfile(PathToData,task_names{task});

			for di = 1:2

			% Finds the images of a given acquisition direction and for a
			% particular task
			tVolsFNlist = getImageFNinAcqOrder_TB(taskPath,['f*_',acqu_dir_names{di}],'nii',[]);
			tVolsFNlist_fullpath = cellfun(@(x) fullfile(taskPath,x), tVolsFNlist,'UniformOutput',false);

			% Reads the header of those images
			T_V0i = spm_vol(tVolsFNlist_fullpath);

			% Removes the first scans
			if removeFirstNscans~=0
			T_V0i = T_V0i((removeFirstNscans+1):end);
			end

			% Indices of time point kept
			T_IDX = 1:length(T_V0i);

			% Creates a 4D matrix to contain the data
			T_V0 = zeros(T_V0i{1}.dim(1),T_V0i{1}.dim(2),T_V0i{1}.dim(3),length(T_V0i),'single');

			% Fills the matrix with the read data
			for iter=1:length(T_V0i)
			T_V0(:,:,:,T_IDX(iter))=spm_read_vols(T_V0i{iter});
			end

			% Detrending parameter
			CUTNUMBER = 10;

			% Reshapes into time x space matrix for detrending
			T_AllVolume = reshape(T_V0,[],size(T_V0,4))'; % time x space

			% How many subpacks of data do we detrend ?
			SegmentLength = ceil(size(T_AllVolume,2) / CUTNUMBER);

			% Detrending itself...
			for iCut=1:CUTNUMBER
			if iCut~=CUTNUMBER
			Segment = (iCut-1)SegmentLength+1 : iCutSegmentLength;
			else
			Segment = (iCut-1)*SegmentLength+1 : size(T_AllVolume,2);
			end

			T_AllVolume(:,Segment) = detrend(T_AllVolume(:,Segment));
			end

			% Reshapes back the detrended data into 4D format
			T_V0=reshape(T_AllVolume',size(T_V0,1),size(T_V0,2),size(T_V0,3),size(T_V0,4));


			% Returns the locations in X, Y and Z of the elements from the CSF mask
			[Ci,Cj,Ck]=ind2sub(size(T_V0(:,:,:,1)),find(CSFmaskLidx));

			% How many voxels in there
			nCSFvoxels=numel(Ci);

			% Constructs the CSF regressor time course
			tmp_CSF = zeros(1,1,1,size(T_V0,4));
			for idx=1:numel(Ci)
			tmp_CSF = tmp_CSF + T_V0(Ci(idx),Cj(idx),Ck(idx),:);
			end

			REG_CSF_T = squeeze(tmp_CSF/nCSFvoxels)';
			REG_CSF_T = REG_CSF_T - mean(REG_CSF_T);

			clear tmp_CSF;

			clear Ci
			clear Cj
			clear Ck


			% Returns the locations in X, Y and Z of the elements from the WM mask
			[Ci,Cj,Ck]=ind2sub(size(T_V0(:,:,:,1)),find(WMmaskLidx));

			% How many voxels in there
			nWMvoxels=numel(Ci);

			% Constructs the WM regressor time course
			tmp_WM = zeros(1,1,1,size(T_V0,4));
			for idx=1:numel(Ci)
			tmp_WM = tmp_WM + T_V0(Ci(idx),Cj(idx),Ck(idx),:);
			end

			REG_WM_T = squeeze(tmp_WM/nWMvoxels)';
			REG_WM_T = REG_WM_T - mean(REG_WM_T);

			clear tmp_WM;


			% Constant, linear and quadratic regressors
			X_T = [ones(numel(REG_WM_T),1) [1:numel(REG_WM_T)]'/numel(REG_WM_T) ...
			[1:numel(REG_WM_T)].^2'/(numel(REG_WM_T)^2)];

			% Adds the WM regressor
			X_T = [X_T, REG_WM_T'];

			% Adds the CSF regressor
			X_T = [X_T, REG_CSF_T'];


			% Regression voxel by voxel, of those nuisance regressors
			T_V0Cov = zeros(size(T_V0));

			for i=1:size(T_V0,1)
			for j=1:size(T_V0,2)
			for k=1:size(T_V0,3)

			% We keep the residual from the regression process (i.e.
			% what is left after we remove the impact from the
			% covariates)
			[~,res_VX] = y_regress_ss(squeeze(T_V0(i,j,k,T_IDX)),X_T);

			% The volumes following regression of covariates are created
			T_V0Cov(i,j,k,T_IDX) = res_VX + mean(squeeze(T_V0(i,j,k,T_IDX)));

			clear res_VX
			end
			end
			end

			% If some elements are equal to NaN, they are set to 0 instead
			T_V0Cov(isnan(T_V0Cov))=0;


			% Will contain the data of interest (header)
			m2V0i = T_V0i;

			% For each volume over time, we create the name of the header and
			% write the output
			for y = 1:size(T_V0Cov,4)

			% Name creation
			s=strfind(T_V0i{y}.fname,'/');
			last=s(size(s,2));
			m2V0i{y}.fname=strcat(T_V0i{y}.fname(1:last),'Cov_',T_V0i{y}.fname(last+1:end));

			% Writing
			spm_write_vol(m2V0i{y}, T_V0Cov(:,:,:,y));
			fprintf('.');
			end

			% All volumes to consider for atlasing of the task data
			AllVolume_V0Cov = reshape(T_V0Cov,[],size(T_V0Cov,4));

			for at = 1:length(Atlas)

			% Creating a right resolution atlas file
			mappedAtlas = mapVolumeToVolume(fullfile(atlasDir,Atlas{at}),tVolsFNlist_fullpath{1});

			% Converting it to voxels x time
			AllVolume_mappedAtlas = reshape(mappedAtlas,[],1);

			% Atlased data
			ATLAS_T = Make_Atlasing(AllVolume_V0Cov, AllVolume_mappedAtlas);

			% Scrubbing of the data from the atlas
			[ATLAS_T_scrubbed,Scrub_mask] = GSP_Scrub(ATLAS_T,fullfile(taskPath,...
			['Movement_Regressors_',task_names{task},'_',acqu_dir_names{di},'_Correct.txt']),removeFirstNscans);

			disp(['Amount scrubbed for ',task_names{task},' ',acqu_dir_names{di},': ',num2str(sum(Scrub_mask)),'/',num2str(length(Scrub_mask)),'...']);

			save(['ATLAS_',task_names{task},'_',acqu_dir_names{di},Atlas_suffix{at}],'ATLAS_T_scrubbed','Scrub_mask');
			end
			end
			end

Slepi/Code/GSP_Script_Preprocessing_Task.mb8089f5f662emaster

GSP_Script_Preprocessing_Task.mView Options

Slepi/Code/GSP_Script_Preprocessing_Task.m
b8089f5f662emaster

GSP_Script_Preprocessing_Task.m
View Options