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Abstract #0069

Individual Subject Functional Connectivity Parcellation with Group-Level Spatial and Connectivity Priors

Ru Kong1, Alexander Schaefer1, Avram J. Holmes2, Simon B. Eickhoff3,4, Xi-Nian Zuo5, 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, 2Department of Psychology, Yale University, New Haven, CT, United States, 3Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, 4Institute for Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany, 5Lab for Functional Connectome and Development Division of Cognitive and Developmental Psychology, CAS, Beijing, China, People's Republic of

We propose a hidden Markov Random Field (MRF) model to parcellate the cerebral cortex of individual subjects using resting-state fMRI (rs-fMRI). Our MRF model imposes a smoothness prior on the individual-specific parcellation, while imposing group-level population priors that capture inter-subject variability in both functional connectivity profiles and spatial distribution of functional brain networks. Experiments on a test-retest dataset suggest that the resulting parcellation estimates are better than alternative approaches at capturing stable properties of individual subjects’ intrinsic brain organization, instead of transient noise or session-dependent variations.

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