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Hum Brain Mapp. 2018 May 16. doi: 10.1002/hbm.24213. [Epub ahead of print]

Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems.

Wu J1, Ngo GH1, Greve D2,3, Li J1, He T1, Fischl B2,3,4, Eickhoff SB5,6, Yeo BTT1,2,7.

Author information

1
Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore City, Singapore.
2
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.
3
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
4
Harvard-MIT Division of Health Sciences and Technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.
5
Medical Faculty, Heinrich-Heine University Düsseldorf, Institute for Systems Neuroscience, Düsseldorf, Germany.
6
Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany.
7
Center for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, Singapore.

Abstract

The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).

KEYWORDS:

Colin27; MNI152; Talairach; atlas; deformation; fMRI; group analysis; registration; structural MRI

PMID:
29770530
PMCID:
PMC6239990
[Available on 2019-11-16]
DOI:
10.1002/hbm.24213

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