Re: [HCP-Users] Concatenating resting state runs

Hi all, interesting points! I will use both variance normalization techniques and test if there are any differences in the resulting DCMs. It might be worth noting, that for the resting state, cross-spectra are being fitted nowadays

Re: [HCP-Users] Concatenating resting state runs

Hi Tim, That isn’t quite an analogous situation. At least for full correlation, computed from the 15 min runs of the HCP-YA, computing a separate network matrix for each individual 15 min run, Fisher transforming those, and then averaging those r-to-z’s appears to be a little bit more robust

Re: [HCP-Users] Concatenating resting state runs

When we compute parcellated connectivity, we first compute the average timeseries within the parcels, and then correlate those, as it vastly reduces the impact of noise. If we first computed the correlations, and then averaged them within parcels, we would be losing a huge amount of power. The

Re: [HCP-Users] Concatenating resting state runs

In the case of correlations or partial correlations, I would tend to compute those separately for each run anyway, Fisher transform them, and then average the r-to-z values across runs. In which case no across-run concatentation is necessary in the first place. I don’t know if a per-run DCM

Re: [HCP-Users] Concatenating resting state runs

The basic idea for variance normalization is to equalize the variance of the noise. It is very helpful for ICA and regression-based techniques. I’m not sure we have explicitly tested the effect on correlation. Correlation is a ratio and so it would not matter at all for a single run, though

Re: [HCP-Users] Concatenating resting state runs

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 : > Hi Mike, > > I doubt that

Re: [HCP-Users] Concatenating resting state runs

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" > Date: Wednesday, March 7, 2018 at 12:09 PM To: Matt Glasser

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

Re: [HCP-Users] Concatenating resting state runs

No, however if he wanted to interpret effect size maps after having done this, he would need to back this out, back out the old bias field, and apply the corrected one. The new multiple regression takes care of adapting to whatever bias field there is or is not. Matt. From: "Harms, Michael"

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"

Re: [HCP-Users] Concatenating resting state runs

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

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

Re: [HCP-Users] Concatenating resting state runs

Awesome, I will do that, thanks very much!! 2018-03-07 18:01 GMT+01:00 Glasser, Matthew : > 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

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

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!

Re: [HCP-Users] Concatenating resting state runs

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.

Re: [HCP-Users] Concatenating resting state runs

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

Re: [HCP-Users] Problem creating scenes in wb_view

Thanks On l-Erbgħa, 07 ta Mar, 2018 03:44 , Harwell, John wrote: This error has been reported before (WB-647 in our internal bug tracking system).  The solution for the user that experienced this problem was to use the version of Workbench distributed through the HCP website

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

Re: [HCP-Users] Problem creating scenes in wb_view

This error has been reported before (WB-647 in our internal bug tracking system). The solution for the user that experienced this problem was to use the version of Workbench distributed through the HCP website (https://www.humanconnectome.org/software/get-connectome-workbench). From some

Re: [HCP-Users] Concatenating resting state runs

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

Re: [HCP-Users] Problem creating scenes in wb_view

apologies Ubuntu 16.04 LTS installed from the neurodebian repo On l-Erbgħa, 07 ta Mar, 2018 02:44 , Glasser, Matthew wrote: What OS is this on? Peace, Matt. From: > on behalf of Claude Bajada

Re: [HCP-Users] Problem creating scenes in wb_view

What OS is this on? Peace, Matt. From: > on behalf of Claude Bajada > Date: Wednesday, March 7, 2018 at 4:25 AM To:

[HCP-Users] Problem creating scenes in wb_view

Hi all, I am encountering a problem when trying to create a scene with wb_view. As soon as I try to add my scene I get a core dump: [cid:part1.C307F21E.5B1978C7@fz-juelich.de] Anyone has a similar problem and know a solution? Claude

[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