ok, so temporal filtering separately for each run. Any comments on
concatenation and z-standardization?
I think there might be a work-around to supplying a custom ROI timecourse
to the DCM VOI-files somehow, but which values to input as alternative to
the eigenvariate? The mean over all voxels in the ROI would also be an
option but not sure what you had in mind.
Can you elaborate on the issue of spatial localization you mention please,
not sure I understood? I'm using mask files to extract the time courses and
I am especially interested in amygdala subregions.
Also, what do you mean by areal ROIs and that they give a purer signal?
2018-03-07 14:51 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:
> 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.
> From: <hcp-users-boun...@humanconnectome.org> on behalf of David Hofmann <
> Date: Wednesday, March 7, 2018 at 2:32 AM
> To: hcp-users <email@example.com>
> 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 variance.
> 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
> 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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