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:


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.


 on behalf of "Stevens, Michael" 
Date: Friday, February 23, 2018 at 8:58 AM
To: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" 
Subject: [HCP-Users] Best Approach for using old volumetric data to pick 

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 


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