Abstract #0307

Cerebral Cortex Parcellation by Fusion of Local and Global Functional Connectivity Feature

Alexander Schaefer¹, Ru Kong¹, Evan M. Gordon², Timothy Laumann ³, Simon B. Eickhoff^4,5, Xi-Nian Zuo⁶, Avram J. Holmes⁷, and B.T. Thomas Yeo¹

¹Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore, Singapore, ²VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, United States, ³Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States, ⁴Institut for Clinical Neuroscience, Heinrich Heine University, Düsseldorf, Germany, ⁵Institute for Neuroscience and Medicine, Research Center Jülich, Jülich, Germany, ⁶Lab for Functional Connectome and Development, Division of Cognitive and Developmental, Chinese Academy of Sciences, Beijing, China, People's Republic of, ⁷Department of Psychology, Yale University, New Haven, CT, United States

Current approaches to cerebral cortex parcellation with resting-state functional connectivity MRI (fcMRI) can be divided into local (e.g., fcMRI gradients) and global (e.g., clustering) approaches. Previous work suggests that local and global approaches capture complementary aspects of brain organization. Here we propose a novel hidden Markov Random Field model that fuses local connectivity gradients with global functional connectivity similarities. The resulting parcellation compares favorably with a state-of-the-art parcellation in terms of (1) parcel homogeneity in two different datasets and (2) agreement with cytoarchitectonic and visuotopic boundaries.

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