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Cereb Cortex. 2018 Sep 1;28(9):3095-3114. doi: 10.1093/cercor/bhx179.

Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.

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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.
VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA.
Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA.
CAS Key Laboratory of Behavioral Sciences, Institute of Psychology, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Yale University, New Haven, CT, USA.
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, Singapore.


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 (

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