Re: [HCP-Users] Combining rfMRI data for different phase encoding directions

Hi - I think your main two choices are whether to run FIX on each 5min run separately, or to preprocess and concatenate each pair of scans from each session and run FIX for each of the 4 paired datasets. You could try FIX both ways on a few subjects and decide which is working better. Cheers.

Re: [HCP-Users] netmats prediction of fluid intelligence

In the context of the long resting state runs that we have available, I would argue that throwing in additional possible confounds is the appropriate thing to do. Are you suggesting that sex, age, age^2, sex*age, sex*age^2, brain size, head size, and average motion shouldn’t all be included?

Re: [HCP-Users] netmats prediction of fluid intelligence

Certainly one difference is that HCP (i.e., Steve) tends to take the more conservative approach of regressing a *lot* of potential confounds, which tends to result in a lower prediction values. You can see that without confound regression, Steve's prediction is 0.21 versus 0.06. Regards, Thomas

[HCP-Users] Combining rfMRI data for different phase encoding directions

Dear Experts: We have acquired rfMRI dataset with A-P and P-A phase encoding direction and the data was acquired in eight 5-minute runs split across four imaging sessions. We have processed the data using HCP pipelines (e.g., PreFreeSurfer, FreeSurfer, PostFreeSurfer, fMRIVolume, fMRISurface

Re: [HCP-Users] Mean and variance normalization

I’ll note that when I do variance normalization, I do it with just the unstructured noise map which is available in the HCP release (though it will need to be resampled to MSMAll space if you are using the MSMAll data). Peace, Matt. From:

Re: [HCP-Users] Mean and variance normalization

I’m not sure what you are looking for, beyond what is in the FAQ. For a given voxel/grayordinate/parcel, if M = mean_over_time S = std_over_time and X(t) is your time-series then demeaning is just: X(t) - M and variance normalization is: (X(t) - M)/S cheers, -MH -- Michael Harms, Ph.D.

Re: [HCP-Users] Mean and variance normalization

Re (2) (expanding on Matt’s response): Demeaning and variance normalizing a parcellated timeseries (or equivalently the time series for a single ROI), and then concatenating those, is not the same as demeaning and variance normalizing the dense time series, concatenating those, and then

Re: [HCP-Users] Mean and variance normalization

1. Yes 2. I haven’t tried it on parcellated timeseries, but suspect that would be fine too. Matt. From: > on behalf of hercp > Date: Thursday, October 5, 2017 at 2:12 PM

[HCP-Users] Mean and variance normalization

I am extracting time series from regions of interest. Matt Glasser suggested that I mean/variance-correct and concatenate the RL and LR phase encoded time series. I still have a couple of questions. 1. Is the concatenation over time? If so, doesn’t this introduce temporal discontinuity?

Re: [HCP-Users] rfMRI data files

Basically you can think of it as an SNR bias. Peace, Matt. From: > on behalf of "Elam, Jennifer" > Date: Wednesday, October 4, 2017 at 4:17 PM To: Romuald Janik

Re: [HCP-Users] Distance between surface ROIs in MMP

Indeed I think we would need to know what you needed the distance for to know how best to compute it. For things like MR artifacts, a 3D distance might be most appropriate. For something like smoothing, a geodesic distance would be appropriate. For something neurobiological, the tractography

Re: [HCP-Users] Surfaces, coordinates and beginner questions

Right we will recommend using the areal classifier to find these areas rather than the group parcellation once the areal classifier is available. Peace, Matt. From: > on behalf of Timothy Coalson