Modular organization of functional network connectivity in healthy controls and patients with schizophrenia during the resting state

• ¹ The Mind Research Network, Albuquerque, NM, USA
• ² Department of Psychology, University of New Mexico, Albuquerque, NM, USA
• ³ Olin Neuropsychiatry Research Center, Hartford, CT, USA
• ⁴ Departments of Psychiatry and Neurobiology, Yale University, New Haven, CT, USA
• ⁵ Department of ECE, University of New Mexico, Albuquerque, NM, USA

Neuroimaging studies have shown that functional brain networks composed from select regions of interest have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness.