Hi all,

that being said, why is this regression approach for variance normalization
superior to a z-standardization? That is, will it practically matter e.g.
for correlations or partial correlations?

2018-03-07 19:31 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:

> Hi Mike,
>
> I doubt that matters for this application of making an unstructured noise
> timeseries for the purpose of variance normalization.
>
> Matt.
>
> From: "Harms, Michael" <mha...@wustl.edu>
> Date: Wednesday, March 7, 2018 at 12:09 PM
>
> To: Matt Glasser <glass...@wustl.edu>, David Hofmann <
> davidhofma...@gmail.com>
> Cc: hcp-users <hcp-users@humanconnectome.org>
> Subject: Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Hi Matt,
>
> Right, that recipe is straightforward, but for completeness there should
> be two additional steps if one wants to match the FIX cleaning precisely:
>
> 1) the 24 motion parameters should be filtered with the same HP filter
> applied to the data
>
> 2) those HP filtered 24 motion parameters should then be removed from the
> (‘signal’) ICA time-series prior to regressing that (modified) ICA
> time-series onto the cleaned data (i.e., that modified ICA time-series
> becomes the basis for deriving ‘betaICA’).
>
>
>
> Cheers,
>
> -MH
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> -----------------------------------------------------------
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> <https://maps.google.com/?q=660+South+Euclid+Ave&entry=gmail&source=g>.
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110                          Email: mha...@wustl.edu
>
>
>
> *From: *"Glasser, Matthew" <glass...@wustl.edu>
> *Date: *Wednesday, March 7, 2018 at 11:24 AM
> *To: *"Harms, Michael" <mha...@wustl.edu>, David Hofmann <
> davidhofma...@gmail.com>
> *Cc: *hcp-users <hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Hi Mike,
>
>
>
> Not for the volume data that he is asking about and not for the MSMAll
> data either unfortunately.  I thought it was better to explain this method
> on the list so that it can be applied to arbitrary data whether or not we
> precomputed it.
>
>
>
> Matt.
>
>
>
> *From: *"Harms, Michael" <mha...@wustl.edu>
> *Date: *Wednesday, March 7, 2018 at 11:21 AM
> *To: *Matt Glasser <glass...@wustl.edu>, David Hofmann <
> davidhofma...@gmail.com>
> *Cc: *hcp-users <hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
>
>
> Matt,
>
> Don’t we compute an estimate of the unstructured noise variance as part of
> RestingStateState, and then place that into one of the packages?
>
>
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> -----------------------------------------------------------
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> <https://maps.google.com/?q=660+South+Euclid+Ave&entry=gmail&source=g>.
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110                          Email: mha...@wustl.edu
>
>
>
> *From: *<hcp-users-boun...@humanconnectome.org> on behalf of "Glasser,
> Matthew" <glass...@wustl.edu>
> *Date: *Wednesday, March 7, 2018 at 11:01 AM
> *To: *David Hofmann <davidhofma...@gmail.com>
> *Cc: *hcp-users <hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Yes they should be in that same package:
>
>
>
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/.fix — Tells you which are the noise
> components (so you can use setdiff to find the signal components from a
> list of all components) so that you can exclude the noise component from
> the regression below.
>
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix — ICA
> component timeseries (you should remove the mean of each ICA component
> timeseries before doing the regression).
>
>
>
> Probably the time to read in and write the file will be longer than the
> time to do the regression if you do it in matlab.  Here is some example
> code:
>
>
>
> betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x
> components and should include only the signal components (since the noise
> components were already removed).
>
> UnstructNoiseTCS = TCS - (ICA * betaICA);
>
>
>
> You then compute the temporal standard deviation of the unstructured noise
> timeseries and divide the data by it to get the variance normalized data.
>
>
>
> Peace,
>
>
>
> Matt.
>
>
>
> *From: *David Hofmann <davidhofma...@gmail.com>
> *Date: *Wednesday, March 7, 2018 at 10:47 AM
> *To: *Matt Glasser <glass...@wustl.edu>
> *Cc: *hcp-users <hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Ah I understand. However, I'm not sure how to do this practically for the
> FIX extended data. I'd need all the signal component timeseries and run a
> regression for each voxel which might take a while. I'm not sure if the
> signals are supplied in the dataset, or are they?
>
>
>
> Thanks for the support!
>
>
>
> 2018-03-07 17:07 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:
>
> 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>
> *Date: *Wednesday, March 7, 2018 at 10:02 AM
>
>
> *To: *Matt Glasser <glass...@wustl.edu>
> *Cc: *hcp-users <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>:
>
> 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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>
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>
>
>
>
>
>
>
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