Abstract #0069

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

Ru Kong¹, Alexander Schaefer¹, Avram J. Holmes², Simon B. Eickhoff^3,4, 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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