Brain Connectivity Networks in Schizophrenia Underlying Resting State Functional Magnetic Resonance Imaging

Qingbao      Yu; Elena      A. Allen; Jing      Sui; Mohammad      R. Arbabshirani; Godfrey      Pearlson; Vince      D. Calhoun

doi:10.2174/1568026611212210012

Abstract

Schizophrenia (SZ) is a severe neuropsychiatric disorder. A leading hypothesis is that SZ is a brain dysconnection syndrome, involving abnormal interactions between widespread brain networks. Resting state functional magnetic resonance imaging (R-fMRI) is a powerful tool to explore the dysconnectivity of brain networks in SZ and other disorders. Seed-based functional connectivity analysis, spatial independent component analysis (ICA), and graph theory-based analysis are popular methods to quantify brain network connectivity in R-fMRI data. Widespread network dysconnectivity in SZ has been observed using both seed-based analysis and ICA, although most seed-based studies report decreased connectivity while ICA studies report both increases and decreases. Importantly, most of the findings from both techniques are also associated with typical symptoms of the illness. Disrupted topological properties and altered modular community structure of brain system in SZ have been shown using graph theory-based analysis. Overall, the resting-state findings regarding brain networks deficits have advanced our understanding of the underlying pathology of SZ. In this article, we review aberrant brain connectivity networks in SZ measured in R-fMRI by the above approaches, and discuss future challenges.

Keywords: Schizophrenia, Resting state-fMRI, Dysconnectivity, Brain network, Seed-based, ICA, Graph.

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