Hello, 

I am wondering how I can skip denoising in CONN using the HCP-A/AABC study scans (AABC is a follow-up of the HCP-A Study). I am using the MNI-Space T1w image (T1w_restore_brain.nii.gz) and the 4# functional image (Ex: rfMRI_REST1_AP_hp0_clean_rclean_tclean.nii.gz) that have undergone sICA and tICA "denoising." 

How do I skip denoising using the conn_batch_humanconnectomeproject.m- I am copying over my edited script below. Also would I need to include Movement_Regressors.txt and still do ART Detection or no since the data has been "denoised" elsewhere. The script below still seems to be denoising the data when I don't want it to (unless it is recommended that I do as well as apply band-pass filtering 0.008-0.09Hz). 

Thank you in advance for your help!

-Zach

%% DEFAULT SETTINGS: EDIT THE LINES BELOW (minimally set CONNECTOMEpath to the actual location of your connectome data)

TARGETpath=pwd;                                                                                                 % target folder for conn project (default current folder)

CONNECTOMEpath_T1w = '/fs/scratch/PAS2966/ZB_Directory/AABC/CONN/Data/T1w/%s/MNINonLinear/';                    % source of connectome dataset (%s stands for numeric subject-specific folders)

CONNECTOMEpath_Resting = '/fs/scratch/PAS2966/ZB_Directory/AABC/CONN/Data/Resting/%s/MNINonLinear/Results/';

RUNPARALLEL=false;                                                                                              % run in parallel using computer cluster

NSUBJECTS=[4];                                                                                                  % number of subjects to include in your project (leave empty for all subjects)

NJOBS=[];                                                                                                       % number of parallel jobs to submit (leave empty for one job per subject)

COPYFILES=false;                                                                                                % true/false: set to true if you do not have write-permissions into connectome data folders

                                                                                                                % This will create a local copy (in same folder as your conn project) of the structural/functional data where any post-processed files will also be stored.

OVERWRITE=false;                                                                                                % overwrites files if they exist in target folder (unzipped files and/or files in local-copy folder)

                                                                                                                % Set to false if you have already unzipped / copied-to-local-folder your data and would like to skip this step





%% FINDS STRUCTURAL/FUNCTIONAL Files

clear FUNCTIONAL_FILE* STRUCTURAL_FILE;

subs=dir(regexprep(CONNECTOMEpath_T1w,'%s.*$','*')); 

subs=subs([subs.isdir]>0);

subs={subs.name};

subs = subs(startsWith(subs,'HCA'));                                                                            % keeps your HCA subject folders

if isempty(NSUBJECTS)

    NSUBJECTS = numel(subs); 

else

    NSUBJECTS = min(NSUBJECTS, numel(subs));

    subs = subs(1:NSUBJECTS);

end

if isempty(NJOBS), NJOBS=NSUBJECTS; 

end

NJOBS=min(NSUBJECTS,NJOBS);



for n=1:numel(subs)

    fprintf('Locating subject %s files\n',subs{n});

    

    t1 = fullfile(sprintf(CONNECTOMEpath_T1w,subs{n}),'T1w_restore_brain.nii.gz');                                             %STRUCTURAL VOLUME

    f1=fullfile(sprintf(CONNECTOMEpath_Resting,subs{n}),'rfMRI_REST1_AP','rfMRI_REST1_AP_hp0_clean_rclean_tclean.nii.gz');   % FUNCTIONAL VOLUME (1/4)

    f2=fullfile(sprintf(CONNECTOMEpath_Resting,subs{n}),'rfMRI_REST1_PA','rfMRI_REST1_PA_hp0_clean_rclean_tclean.nii.gz');   % FUNCTIONAL VOLUME (2/4)

    f3=fullfile(sprintf(CONNECTOMEpath_Resting,subs{n}),'rfMRI_REST2_AP','rfMRI_REST2_AP_hp0_clean_rclean_tclean.nii.gz');   % FUNCTIONAL VOLUME (3/4)

    f4=fullfile(sprintf(CONNECTOMEpath_Resting,subs{n}),'rfMRI_REST2_PA','rfMRI_REST2_PA_hp0_clean_rclean_tclean.nii.gz');   % FUNCTIONAL VOLUME (4/4)

    if isempty(dir(t1)), error('file %s not found',t1); end

    if isempty(dir(f1)), error('file %s not found',f1); end

    if isempty(dir(f2)), error('file %s not found',f2); end

    if isempty(dir(f3)), error('file %s not found',f3); end

    if isempty(dir(f4)), error('file %s not found',f4); end



    

    if COPYFILES

        fprintf('Copying files to local folder\n');

        [ok,nill]=mkdir(TARGETpath,'LocalCopyDataFiles');

        [ok,nill]=mkdir(fullfile(TARGETpath,'LocalCopyDataFiles'),subs{n});

        t1b=fullfile(TARGETpath,'LocalCopyDataFiles',subs{n},'structural.nii.gz');  if OVERWRITE||isempty(dir(t1b)), [ok,nill]=system(sprintf('cp ''%s'' ''%s''',t1,t1b)); end; t1=t1b;

        f1b=fullfile(TARGETpath,'LocalCopyDataFiles',subs{n},'functional1.nii.gz'); if OVERWRITE||isempty(dir(f1b)), [ok,nill]=system(sprintf('cp ''%s'' ''%s''',f1,f1b)); end; f1=f1b;

        f2b=fullfile(TARGETpath,'LocalCopyDataFiles',subs{n},'functional2.nii.gz'); if OVERWRITE||isempty(dir(f2b)), [ok,nill]=system(sprintf('cp ''%s'' ''%s''',f2,f2b)); end; f2=f2b;

        f3b=fullfile(TARGETpath,'LocalCopyDataFiles',subs{n},'functional3.nii.gz'); if OVERWRITE||isempty(dir(f3b)), [ok,nill]=system(sprintf('cp ''%s'' ''%s''',f3,f3b)); end; f3=f3b;

        f4b=fullfile(TARGETpath,'LocalCopyDataFiles',subs{n},'functional4.nii.gz'); if OVERWRITE||isempty(dir(f4b)), [ok,nill]=system(sprintf('cp ''%s'' ''%s''',f4,f4b)); end; f4=f4b;

        

    end

    fprintf('Unzipping files\n');

    if OVERWRITE||isempty(dir(regexprep(t1,'\.gz$',''))), gunzip(t1); end; t1=regexprep(t1,'\.gz$','');

    if OVERWRITE||isempty(dir(regexprep(f1,'\.gz$',''))), gunzip(f1); end; f1=regexprep(f1,'\.gz$','');

    if OVERWRITE||isempty(dir(regexprep(f2,'\.gz$',''))), gunzip(f2); end; f2=regexprep(f2,'\.gz$','');

    if OVERWRITE||isempty(dir(regexprep(f3,'\.gz$',''))), gunzip(f3); end; f3=regexprep(f3,'\.gz$','');

    if OVERWRITE||isempty(dir(regexprep(f4,'\.gz$',''))), gunzip(f4); end; f4=regexprep(f4,'\.gz$','');

    

    STRUCTURAL_FILE{n,1}=t1;

    FUNCTIONAL_FILE(n,1:4)={f1,f2,f3,f4};

end

nsessions=4;

fprintf('%d subjects, %d sessions\n',NSUBJECTS,nsessions);







%% CREATES CONN BATCH STRUCTURE

clear batch;

batch.filename=fullfile(TARGETpath,'conn_HCP_AABC.mat');

if RUNPARALLEL

    batch.parallel.N=NJOBS;                             % number of parallel processing batch jobs

end                                                     % note: use default parallel profile (defined in GUI Tools.GridSettings)



% CONN Setup                                           

batch.Setup.isnew=1;

batch.Setup.nsubjects=NSUBJECTS;

batch.Setup.RT=0.8;                                    % TR (seconds)



batch.Setup.conditions.names={'rest'};                  % single condition (aggregate across all sessions)

for ncond=1,for nsub=1:NSUBJECTS,for nses=1:nsessions,      batch.Setup.conditions.onsets{ncond}{nsub}{nses}=0; batch.Setup.conditions.durations{ncond}{nsub}{nses}=inf;end;end;end     % rest condition (all sessions)



batch.Setup.functionals=repmat({{}},[NSUBJECTS,1]);     % Point to functional volumes for each subject/session

for nsub=1:NSUBJECTS,for nses=1:nsessions,                  batch.Setup.functionals{nsub}{nses}=FUNCTIONAL_FILE(nsub,nses);end; end 



batch.Setup.structurals=STRUCTURAL_FILE;                % Point to anatomical volumes for each subject



batch.Setup.voxelresolution=1;                          % default 2mm isotropic voxels analysis space... that is the resolution of the fMRI data anyway



batch.Setup.covariates.names={};

batch.Setup.covariates.files={};      



batch.Setup.outputfiles=[0,0,0,0,0,0];                  % creates d*.nii denoised output files

batch.Setup.analyses=[1,2];                             % seed-to-voxel and ROI-to-ROI pipelines

batch.Setup.overwrite='Yes';                            

batch.Setup.done=1;



batch.Setup.preprocessing.steps={'structural_segment','functional_smooth'};  % Run additional preprocessing steps: segmentation & smoothing

batch.Setup.preprocessing.fwhm=4;                       % smoothing fwhm (mm)

conn_batch(batch);



clear batch;

% Do NOT run a denoising batch at all since HCP-A+AABC data are tICA+rICA cleaned for motion

batch.Denoising.filter=[-inf inf];    % no temporal filtering

batch.Denoising.detrending=0;         % no detrending

batch.Denoising.confounds.names={};   % no nuisance regression

batch.Denoising.done=1;

batch.Denoising.overwrite='Yes';

conn_batch(batch);





clear batch;

% CONN Analysis                                         % Default options (uses all ROIs in conn/rois/ as connectivity sources); see conn_batch for additional options 

batch.Analysis.done=1;

batch.Analysis.overwrite='Yes';





%% RUNS CONN BATCH STRUCTURE

conn_batch(batch);