Dear CONN experts,
I am encountering a "[b]Not ready to process step conn_process_5[/b]" problem when doing functional preprocessing and denoising only but cannot find an answer elsewhere.
My experiment involves multiple tasks, and each participant has one functional image for each task and one structural image. To reduce computational time, i did structural image preprocessing first and then planned to use the preprocessed structural image and the same conn*.mat file for further analysis (functional preprocessing and denoising),so that it will use the structural preprocessing. However, after doing all preprocessing, the denoising did pass through, it reports such info (not 'error') as below and no filtered data was generated. I pasted the commands I used as below. Can any expert help to find out a solution?
Thanks a lot in advance!
Jing
[color=#ff0000]<font color="#008000">Step 1/7: Expanding conditions[/color]</font>
[color=#ff0000]<font color="#008000">100.0% (Condition rest)[/color]</font>
[color=#ff0000]<font color="#008000">Step 2/7: Importing conditions/covariates[/color]</font>
[color=#ff0000]<font color="#008000">100.0% (Subject 1 Session 1)[/color]</font>
[color=#ff0000]<font color="#008000">Step 3/7: Updating Denosing variables[/color]</font>
[color=#ff0000]<font color="#008000">Not ready to process step conn_process_5[/color]</font>
[color=#ff0000]<font color="#008000">saved /media/storage/CausalConnectome/tmsfmri/test/NTHC1003/conn_NTHC1003.mat[/color]</font>
------------------------------------------------------------------
[b][color=#008000]##commands for structural preprocessing[/color][/b]
cwd=[pwd '/' subid];
batch.filename=fullfile(cwd, ['conn_' subid '.mat']);
%% --------------------------------- CONN Set up ----------------------------------
%% data
STRUCTURAL_FILE={[cwd, '/CausCon_' subid '_t1.nii']};
batch.Setup.structurals=STRUCTURAL_FILE;
%% preprocessing steps
batch.Setup.preprocessing.steps={'structural_center','structural_segment&normalize'};
conn_batch(batch)
[color=#008000][b]## commands for functional preprocessing and denoising[/b][/color]
cwd=[pwd '/' subid];
batch.filename=fullfile(cwd, ['conn_' subid '.mat']);
%% --------------------------------- CONN Set up ----------------------------------
batch.Setup.isnew=0; % use the old setting from struct!!!
batch.Setup.overwrite=0; % not overwrite!!!
batch.Setup.RT=2.4;
%% data
FUNCTIONAL_FILE={[cwd, '/func/CausCon_' subid '_tms_' site '.nii.gz']};
batch.Setup.functionals=FUNCTIONAL_FILE;
batch.Setup.outputfiles= [0,1,1,1,1,0]; %Optional output files (outputfiles(1): 1/0 creates confound beta-maps; outputfiles(2): 1/0 creates
% confound-corrected timeseries; outputfiles(3): 1/0 creates seed-to-voxel r-maps) ;outputfiles(4):
% 1/0 creates seed-to-voxel p-maps) ;outputfiles(5): 1/0 creates seed-to-voxel FDR-p-maps);
% outputfiles(6): 1/0 creates ROI-extraction REX files; [0,0,0,0,0,0]
%% preprocessing steps
batch.Setup.preprocessing.steps={'functional_realign&unwarp' ,'functional_center', ...
'functional_normalize_direct','functional_smooth'};
batch.Setup.preprocessing.fwhm=6;
%% --------------------------------- CONN denoising ---------------------------------
batch.Setup.conditions.names={'rest'};
batch.Setup.conditions.onsets{1}{1}{1}=0;
batch.Setup.conditions.durations{1}{1}{1}=inf;
batch.Denoising.filter=[0.008, inf]; % frequency filter (band-pass values, in Hz)
batch.Denoising.confounds.names=... % Effects to be included as confounds (cell array of effect names, effect names can be first-level covariate names, condition names, or noise ROI names)
{'Grey Matter','White Matter','CSF','realignment'};
batch.Denoising.confounds.dimensions=... % dimensionality of each effect listed above (cell array of values, leave empty a particular value to set to the default value -maximum dimensions of the corresponding effect-)
{1, 3, 3, []};
batch.Denoising.confounds.deriv=... % derivatives order of each effect listed above (cell array of values, leave empty a particular value to set to the default value)
{0, 0, 0, 1};
batch.Denoising.overwrite=1; % skip previously-processed subjects
batch.Denoising.done=1; % use default denoising step (CompCor, motion regression, scrubbing, detrending)
end
conn_batch(batch)[b]
[/b]