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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Published on: 19 February, 2013
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