[FieldTrip] across-subject analysis of source space data in a 2D triangulated cortical mesh

Schoffelen, J.M. (Jan Mathijs) jan.schoffelen at donders.ru.nl
Wed Jul 1 09:09:35 CEST 2015

Hi Brian,

Nowadays I would indeed go for a surface-registered cortical mesh. It’s possible to register the cortical meshes to a template such that, across subjects, vertices can be directly compared because they’re in the same ‘surface location’. Actually, your question came up in a different post recently, and the answer I gave back then is also valid for your current question :o). Here’s the link , it’s about question b) in the thread: http://mailman.science.ru.nl/pipermail/fieldtrip/2015-June/009312.html  I haven’t heard back from Marieke van den Nieuwenhuijzen (who asked this question earlier) whether she has managed to solve the issue for her. It may be worthwile to check with her, or otherwise for the two of you to team up in order to solve it together. Once there’s a workable solution, it would be great to share this info on the fieldtrip wiki. Here’s some additional info that may be useful: http://brainvis.wustl.edu/wiki/index.php/Caret:Operations/Freesurfer_to_fs_LR

Best wishes,

On Jul 1, 2015, at 3:25 AM, Brian Maniscalco <bmaniscalco at gmail.com<mailto:bmaniscalco at gmail.com>> wrote:

Hi all,

I have been doing MNE source space analysis of single-subject MEG data, closely following the steps described here:


This pipeline yields source activations distributed across a 2D triangulated cortical mesh, as extracted from individual subject anatomical MRI via FreeSurfer. So in order to perform across-subject analyses, it is necessary to somehow normalize individual subject meshes into a standardized space.

In principle it is possible to interpolate the 2D source data onto the subject's 3D anatomical MRI and then normalize this 3D source space data into a standardized anatomical template. However, the logic of interpolating 2D surface activations across an entire 3D volume seems questionable, and my initial attempts at doing just this have yielded results that look either nonsensical or not nearly as sharp and intuitive as the original 2D source visualization.

My question is: is there a reasonable way to normalize source space data computed on individual subject 2D meshes onto a standardized 2D mesh, rather than a standardized 3D volume? If there is no pre-existing template for such a 2D mesh, perhaps a makeshift one could be computed by averaging the geometries of the meshes of the individual subjects in this dataset?

Or to pose the question more generally, what is the best way to conduct across-subject analysis on source space data given the kind of 2D mesh data produced by the processing pipeline for the MNE tutorial linked above?

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