The unstructured noise variance is the standard deviation of the timeseries after you regress out all of the signal component timeseries. By doing this you make the unstructured noise equal in magnitude across the brain.
I wouldn’t do smoothing unless it is constrained to the greymatter. Really you won’t get an obvious benefit if you will be averaging voxels in an ROI anyway and that is a more accurate way to do things. I guess I don’t know enough about your study to know if the order matters. If you are interested in effects that might be related to order (e.g. drowsiness being higher in later scans, then order might matter). Peace, Matt. From: David Hofmann <davidhofma...@gmail.com<mailto:davidhofma...@gmail.com>> Date: Wednesday, March 7, 2018 at 10:02 AM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>> Cc: hcp-users <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] Concatenating resting state runs 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<mailto: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<mailto:davidhofma...@gmail.com>> Date: Wednesday, March 7, 2018 at 9:12 AM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>> Cc: hcp-users <hcp-users@humanconnectome.org<mailto: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<mailto: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<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of David Hofmann <davidhofma...@gmail.com<mailto:davidhofma...@gmail.com>> Date: Wednesday, March 7, 2018 at 2:32 AM To: hcp-users <hcp-users@humanconnectome.org<mailto: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 _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
