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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