Hey Matthew,

not sure I understood where to get the unstructured noise variance from,
i.e. is it even possible to apply this to the FIX extended datasets?

I thought about using 4mm smoothing (maybe 2mm) before extracting the VOIs
/ ROI timecourses for each subject. This is then fed into the DCMs for each
subject. I experimented with some HCP data before and it seems
smoothing increases the effect sizes a little bit. What is smoothing
between parcellations btw.?

Also, any comments on the order of concatenation? I concatenate all of the
data RL and then LR.

2018-03-07 16:17 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:

> I typically variance normalize before concatenation, but do this based on
> the unstructured noise variance.
> I would take the mean time course over an ROI that I thought to be
> representative of a meaningful neuroanatomical subunit.
> My understanding of how SPM’s DCM is typically implemented is that there
> are large amounts of spatial smoothing, cross-subject alignment is done in
> the volume, and ROIs are spheres of some radius.  All this would lead to a
> lot of mixing of timecourses.  My suggestion was to use parcel timecourses
> from some kind of parcellation.  If you have a good amygdala parcellation
> that might be fine, though I would avoid smoothing the data between the
> parcels.
> Peace,
> Matt.
> From: David Hofmann <davidhofma...@gmail.com>
> Date: Wednesday, March 7, 2018 at 9:12 AM
> To: Matt Glasser <glass...@wustl.edu>
> Cc: hcp-users <hcp-users@humanconnectome.org>
> Subject: Re: [HCP-Users] Concatenating resting state runs
> Hi Matthew,
> 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?
> Thanks :)
> 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.
>> Peace,
>> Matt.
>> From: <hcp-users-boun...@humanconnectome.org> on behalf of David Hofmann
>> <davidhofma...@gmail.com>
>> Date: Wednesday, March 7, 2018 at 2:32 AM
>> To: hcp-users <hcp-users@humanconnectome.org>
>> 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
>> 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
>> David
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