Hi Tim, Thanks. That’s clear and sounds like a really reasonable approach.
Can you point me towards the exact files I’d need to reference and maybe suggest which function calls I’ll need to use to do the volume-to-surface mapping you describe? I’ll whip up a quick script to loop through about 120 datasets from this R01 project and let you know how well it works. Mike From: Timothy Coalson [mailto:[email protected]] Sent: Friday, February 23, 2018 6:49 PM To: Glasser, Matthew Cc: Stevens, Michael; Erin W. E. Dickie; [email protected] Subject: Re: [HCP-Users] Best Approach for using old volumetric data to pick parcels-of-interest This is an email from Outside HHC. USE CAUTION opening attachments or links from unknown senders. Surface-based methods may boost your statistical power enough (by better alignment, exclusion of irrelevant tissue, and smoothing that doesn't cross sulcal banks, if you decide you need smoothing) that you may not need to rely as much on existing ROIs. Parcel-based statistics have a lot of power, because the multiple comparisons are orders of magnitude smaller, spatially independent noise averages out, and the signal averages together. We believe that a lot of old data would benefit from reanalysis using surfaces. However, our paper is mainly focused on specificity and continuous data. If you have a binary volume ROI and you only need a rough guess of it on the surface, you can get approximate answers, in a way that should reduce false negatives (and give more false positives) from the surface/volume transition problems. You can map the ROI to the anatomical MNI surfaces of a group of subjects, and take the max across subjects. Each individual may miss the expected group ribbon location in any given location, but it is very likely that every point in the expected group ribbon location will overlap with at least one subject in the group. If this isn't enough, you can dilate the volume ROI a few mm first. Tim On Fri, Feb 23, 2018 at 11:18 AM, Glasser, Matthew <[email protected]<mailto:[email protected]>> wrote: Hi Mike, We have a preprint out on this exact question and the conclusion is that it is really hard to do this accurately for most brain regions: https://www.biorxiv.org/content/early/2018/01/29/255620 Really the best idea is probably to go back and reanalyze the old data without volume-based smoothing and aligned across surfaces. Erin Dickie, CCed is working on tools to make this a little easier, but still there are issues like needing a field map to get accurate fMRI to structural registration. The good news is that one’s statistical power should be much better if brains are actually lined up, and using parcellated analyses instead of smoothing offers further benefits. Matt. From: <[email protected]<mailto:[email protected]>> on behalf of "Stevens, Michael" <[email protected]<mailto:[email protected]>> Date: Friday, February 23, 2018 at 8:58 AM To: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Subject: [HCP-Users] Best Approach for using old volumetric data to pick parcels-of-interest Hi everyone, There’s been a lot posted here over the past year or two on the challenges and limitations of going back-and-forth between volumetric space and HCP-defined surface space, with solid arguments for moving to (and sticking with) CIFTI-defined brainordinates. Here, I’m asking a slightly different question… The field has decades of research using volume-space fMRI timeseries analyses that helps to define where to look in the brain to test new hypotheses. Has anyone got a well-thought-out approach for mapping such volume-space ROIs to the parcels within the new HCP 180 atlas? I ask because the specificity of the HCP atlas sometimes offers a half dozen candidate parcels for hypothesis-testing for what we previously thought of as just one or two regions. Even though our group currently has a half dozen newer NIH-funded studies that use HCP compliant sequences, most of that work is still predicated on a “region-of-interest” approach because the study groups sizes are less than a hundred, not in the thousands typical of the HCP grantwork. So we still have to contend with the statistical power limitations inherent in any ROI approach. It would be great to be able to use our prior volume-space data to have greater confidence in selecting among the various parcel-of-interest candidates when testing hypotheses. I’m wondering if anyone’s yet worked out a step-by-step approach for a series of warps/surface-maps/transformations that can take ROIs from MNI space and give a “best guess” as to which HCP 180 atlas parcel(s) should be queried in such instances. It would be a nice bridge from older work to newer HCP-guided work, that would allow researchers to circumvent the added burden of having to go back and collect new pilot data using HCP sequences. A thoughtful list of the analytic or conceptual pros/cons of something like this would be helpful as well. Thanks, Mike This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure, or distribution is prohibited. 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