期刊
CEREBRAL CORTEX
卷 28, 期 9, 页码 3095-3114出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhx179
关键词
brain parcellation; Brodmann areas; cytoarchitecture; resting-state functional connectivity; retinotopy
资金
- Singapore Ministry of Education (MOE) [MOE2014-T2-2-016]
- National University of Singapore (NUS) Strategic Research [DPRT/944/09/14]
- NUS School of Medicine (SOM) Aspiration Fund [R185000271720]
- Singapore National Medical Research Council [CBRG/0088/2015]
- NUS Young Investigator Award
- Singapore National Research Foundation (NRF)
- DAAD postdoctoral fellowship
- NIMH [MH100872, K01MH099232]
- National Basic Research (973) Program [2015CB351702]
- Natural Science Foundation of China [81 471 740, 81220108014]
- Center for Functional Neuroimaging Technologies [P41EB015896]
- Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital [S10RR023401, S10RR023043]
- Human Connectome Project, WU-Minn Consortium [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological atoms. Resting state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/ tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).
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