Also, if David were to do this with HCP-YA data, does he additionally need to
worry about “backing out” the bias field normalization?
--
Michael Harms, Ph.D.
-----------------------------------------------------------
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave. Tel: 314-747-6173
St. Louis, MO 63110 Email: mha...@wustl.edu
From: <hcp-users-boun...@humanconnectome.org> on behalf of "Harms, Michael"
<mha...@wustl.edu>
Date: Wednesday, March 7, 2018 at 11:21 AM
To: "Glasser, Matthew" <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. Tel: 314-747-6173
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<mailto:davidhofma...@gmail.com>>
Date: Wednesday, March 7, 2018 at 10:47 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
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<mailto: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<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
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