If we leave out the “MNI coordinates” part of this question and think in terms 
of the volume space coordinates of the individual being studied, it is 
perfectly valid to bring results from the group surface back to an individual’s 
physical volume space. Is a single coordinate what you need or would an ROI be 
better?

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: Tuesday, February 12, 2019 at 9:47 PM
To: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" 
<hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>
Subject: [HCP-Users] Individualized HCP parcel to MNI coord

Hi everyone,

I know by now this is a tired subject that’s come up in several ways on the HCP 
listserv over the past few years.  But can y’all remind me the most 
straightforward way to go backwards from a given HCP parcel (e.g., IFSa) and 
find a reasonably close approximation in MNI space?

The problems in working from “group level” surface data and the differences 
from subject to subject have been discussed here before.  But I’m pretty sure 
I’m not risking those pitfalls with what I’m planning here.  I’m finalizing a 
study design and I need a “starting point” to select precise brain stimulation 
coordinates for individuals.  This study formulation is based on several years’ 
worth of my fMRI data in HCP space that I’ve done some things with ICA and DCM 
to put together some interesting systems-level circuit maps.  Now, based on 
findings within HCP localized space, I want to use specific parcels to plan how 
best to modulate those circuits experimentally with tDCS.  The catch… of 
course… is that the neurotargeting software works in voxelwise space.  In 
practice, I suspect this won’t be too significant a hurdle.  I envision taking 
each subject’s individual surface, backtracking a given HCP parcel to their own 
volume space, then use neurotargeting software to figure out how to optimize 
focality/field intensity to that region in volume space… purely on a 
subject-by-subject basis.  Even the individual parcel-to-volume mapping would 
be nothing more than a starting point for individualized optimization.  You can 
only do so much to maximize tDCS focality… even with impressive recent advances 
in multifocal current flow modeling.  So there’s always going to be some slop.  
The trick simply is to minimize that slop as much as possible, including making 
sure I don’t inadvertently divert current through OTHER key regions of the 
neural circuit.

So what I lack is a set of wb_command’s that can do that individualized 
back-tracking.  If possible, I want to actually do this on my own data prior to 
finalizing the grant proposal I’m working up right now, just to be sure I’ve 
got all this right.  I’ve already got some code (thank to y’all a few months 
back) that uses the –volume-to-surface-mapping command to go the OTHER way.  
Sure, I could make a string of guesses of which MNI coordinates might map best 
to my target parcels, and test iteratively over a 100 or so subject datasets 
until I find a convincing match.  But it just seems “cleaner” if I could simply 
do the reverse of this… That is, truly go from a given parcel (e.g., IFSa) to 
MNI coordinate in each subject.  I might’ve missed it, but I just don’t see an 
obvious command candidate for going in the other direction.  If there’s not, is 
there a way to string together a bunch of other wb_command’s to achieve this 
result?

Any guidance would be welcome.  Thanks in advance!

Mike


Michael C. Stevens, Ph.D.
Director, CNDLAB, Olin Neuropsychiatry Research Center
Director, Child & Adolescent Research, The Institute of Living
Adjunct Professor of Psychiatry, Yale University School of Medicine



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