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

Thanks! Matt! I have one more following-up question. In order to run the script "hcp_fix_multi_run", we have to concatenate all the data temporally, right? I combined the data using following command: fslmerge -t …. Then, I ran the hcp_fix_multi_run script as follow: hcp_fix_multi_run

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

No multi-run ICA+FIX handles the concatenation for you so you specify the separate runs. That is the whole point. Have a look at this bioRvix paper in the methods about multi-run ICA+FIX so you understand why it is implemented the way it is:

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

It is worth noting that there IS a biological distance bias in connections that has been found with invasive tracers, though the mechanism by which this occurs in in tractography is different from the biological mechanism as Tim says. There’s more discussion of this in the paper I referenced.

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

Hi Matt/Tim, My goal is to improve network inference from tractography data by better accounting for the distance bias in tractography, so I want to use some proxy for actual connection distance between ROI pairs. Using tractography itself to account for its own bias against long-distance

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

I would not do #2 as you need to do some preprocessing prior to running ICA+FIX when concatenating across runs and this is all that the multi-run ICA+FIX pipeline does differently from regular ICA+FIX. I’ll let Steve answer that other question. Matt. From: Sang-Young Kim

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

However that is actually how it is often done. Actually in so far as the connections follow the right path, tractography should give the best estimates and we used it that way in this paper: http://www.jneurosci.org/content/36/25/6758.short Peace, Matt. From: "Gopalakrishnan, Karthik"

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

Right, I wasn't very precise in my wording. I was thinking of the "tractography distance bias" as the amount of the bias that is above and beyond the real biological distance relationship. Tim On Fri, Oct 6, 2017 at 4:36 PM, Glasser, Matthew wrote: > It is worth noting

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

This was useful and I shall make sure to go through the paper you referenced, Matt, thank you both Matt and Tim! Tim, I notice you mention that tractography reports distances as well, which shouldn’t have the same bias as the reported number of streamlines/connection strength — I wasn’t really

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

We did it for all to all connectivity at the vertex level and then did a weighted average according to number of streamlines (as the more streamlines, the more robust the distance measure). Peace, Matt. From: Timothy Coalson > Date: Friday, October 6,

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

Would that require them to run a separate probtrackx for each seed area? If the hits are recorded on the surface, is the distance also reported on vertices? If you can only get the distances in white matter voxels, or only the distances from the seed point, things could get challenging if you

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

Tractography's distance bias is in its reported strengths. The distances reported by tractography should not have a significant bias in the same way - while it takes longer paths less often, it doesn't often take paths that are even more windy and longer than the real path, and it generally can't

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

--ompl option in probtrackx2. Peace, Matt. From: "Gopalakrishnan, Karthik" > Date: Friday, October 6, 2017 at 7:05 PM To: Timothy Coalson > Cc: Matt Glasser >,

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

Hi all to chime in: we have done extensive work over the past year to replicate and understand the prediction of IQ obtained in the Finn study. Our manuscript is about to be submitted. Take away points: -- the high effect size they find is partly due to small sample size (118 subjects) and to the

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

Hi, Matt and Stephen: Thanks for your responses. So I will try below three options to see which one is better. 1. ICA+FIX on each 5 min run separately 2. Concatenate each pair of scans from each session and then ICA+FIX on each session 3. Use multi-run ICA+FIX to combine across runs I have

[HCP-Users] Genomewide data timeline.

Anyone aware of the timeline for the genomewide data to be released? Best, James Sent from my iPhone ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users

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

There is a beta version of a multi-run ICA+FIX pipeline available in the HCP Pipeline’s repository. For 5 minute runs, I would expect combining across runs to be best. We haven’t tested combining across sessions yet, so you would have to check that that was working okay if you wanted to try

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

Hi all Yes - I've discussed this with Todd and it's not immediately clear whether the difference is due to: - they used full correlation not partial - they used fewer confound regressors (IIRC) - their prediction method is *very* different (pooling across all relevant features rather than