Abstract
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.
Highlights
IntroductionModular community structure is one of the most ubiquitous properties of complex networks (Newman, 2006; Bullmore and Sporns, 2009) and is repeatedly demonstrated in resting state functional brain connectivity networks (Salvador et al, 2005; Ferrarini et al, 2009; He et al, 2009; Meunier et al, 2009b; Smith et al, 2009; Shen et al, 2010)
GROUP independent component analysis (ICA) AND WEIGHTED NETWORK Brain components indentified by ICA are similar to those observed in previous studies (Abou-Elseoud et al, 2010; Allen et al, 2011)
The clustering coefficient of the whole network is higher in healthy controls (HCs) (HCs: 0.548 ± 0.033; SZs: 0.530 ± 0.026; P = 0.043)
Summary
Modular community structure is one of the most ubiquitous properties of complex networks (Newman, 2006; Bullmore and Sporns, 2009) and is repeatedly demonstrated in resting state functional brain connectivity networks (Salvador et al, 2005; Ferrarini et al, 2009; He et al, 2009; Meunier et al, 2009b; Smith et al, 2009; Shen et al, 2010). Meunier et al (2009a) showed agerelated changes of modules in brain networks; Balenzuela et al (2010) found differences in the membership of key communities of frontal and temporal regions; Alexander-Bloch et al (2010) detected disrupted modularity of functional brain networks in childhood-onset schizophrenia. These studies typically work with networks whose nodes are selected regions of interest (ROIs) which do not necessarily respect the functional boundaries of the human brain. In this study, weighted networks were built based on ICA-derived components
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