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

Nieuwenhuijzen, M.E. van de (Marieke) m.vandenieuwenhuijzen at donders.ru.nl
Wed Jul 1 13:19:01 CEST 2015

Hi Jan-Mathijs and Brian,

For some reason the Digest delivered the reply to my question really late, so I ended up delving into it a bit more myself. By the time the Digest finally did come through, here is what I had come up with, and what seems to be working:

Freesurfer has its own parcellation scripts. So, basically, what it can do (although it is not part of the MNE-pipeline described on the Fieldtrip wiki), is for each subject parcellate the brain into the Desikan-Killiany atlas, Destrieux atlas and DKT atlas (see https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation for details).
By adding the following lines to the freesurfer script the segmentations (per hemisphere) are added to the label folder in your freesurfer subject folder.

recon-all -cortparc -subjid $subjID % the Desikan-Killiany atlas
recon-all -cortparc2 -subjid $subjID % the Destrieux atlas
recon-all -cortparc3 -subjid $subjID % the DKT atlas

With the read_annotation.m file from the same page as explains the atlasses, you can read in these newly created annotation files. So then you have per subject for each grid point the atlas label it belongs to. The only issue then left is that the grid points are downsampled by MNEsuite before being used by Fieldtrip again. However, the headmodel (bem/subjID-oct-6-src.fif, which can be read in with ft_read_headshape, and which is also read in in the fieldtrip pipeline as sourcespace) contains a field .orig.inuse, specifying which of the original grid points are included in the final downsampled grid. So then it is just a matter of selecting your grid points of interest, and reverse engineering what atlas label it belongs to. 

Does that help (and make sense?)


From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of fieldtrip-request at science.ru.nl [fieldtrip-request at science.ru.nl]
Sent: 01 July 2015 12:00

Message: 4
Date: Wed, 1 Jul 2015 07:09:35 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] across-subject analysis of source space data
        in a 2D triangulated cortical mesh
Message-ID: <744A58BE-FFAE-458A-8809-C71AF9565070 at fcdonders.ru.nl>
Content-Type: text/plain; charset="windows-1252"

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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