You would want to apply temporal filtering separately to each run.  I wonder if 
there is a way you could just provide the ROI timecourses to SPM’s DCM model 
without using its tools for extracting the ROIs so that you could avoid the 
issues spatial localization that SPM has.  If you used areal ROIs, you likely 
wouldn’t even need the eigenvariate approach as you would be getting a much 
purer signal.



 on behalf of David Hofmann 
Date: Wednesday, March 7, 2018 at 2:32 AM
To: hcp-users 
Subject: [HCP-Users] Concatenating resting state runs

Hi all,

for a later analysis where I extract ROIs with SPM, I need to concatenate the 
resting state runs and want to make sure I'm doing it correctly. SPM extracts 
the first eigenvariate of a ROI, i.e. the component that explains the most 

I'm using the Resting State fMRI 1 FIX-Denoised (Extended) and Resting State 
fMRI 2 FIX-Denoised (Extended) datasets.  That is, the files: 
rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL _hp2000_clean.nii asf.

I chose the following approach:

1.  z-standardize each session (each voxel timecourse), i.e. RL, LR separately
2. Then concatenate them
3. Run the SPM routines which will also apply a high-pass filter of about 128s 
on the already concatenated data (it's for the processing of a DCM rather than 
functional connectivity)

I have the following questions:

1. Is this approach correct?
2. Does the order of concatenation matter? That is, (RL/LR or LR/RL) or is it 
important to concatenate it in the order it was acquired in each subject? I 
read that it sometimes changes between subjects such that LR came first in one 
subject and RL first in another.
3. Since SPM will run a hp-filter on the concatenated data, would it be better 
to hp filter each run separately before concatenation?
4. Is this approach also applicable to the task data (i.e. standardize and 
filter separately before concatenation)?

Thanks in advance


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