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: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of "Stevens, Michael" <michael.stev...@hhchealth.org<mailto:michael.stev...@hhchealth.org>> Date: Friday, February 23, 2018 at 8:58 AM To: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> 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. If you are not the intended recipient, or an employee or agent responsible for delivering the message to the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message, including any attachments. _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
