Abstract
Schizophrenia is a disease with disruptions in thought, emotion, and behavior. The dysconnectivity hypothesis suggests these disruptions are due to aberrant brain connectivity. Many studies have identified connectivity differences but few have been able to unify gray and white matter findings into one model. Here we develop an extension of the Network-Based Statistic (NBS) called NBSm (Multimodal Network-based statistic) to compare functional and anatomical networks in schizophrenia. Structural, resting functional, and diffusion magnetic resonance imaging data were collected from 29 chronic patients with schizophrenia and 29 healthy controls. Images were preprocessed, and average time courses were extracted for 90 regions of interest (ROI). Functional connectivity matrices were estimated by pairwise correlations between wavelet coefficients of ROI time series. Following diffusion tractography, anatomical connectivity matrices were estimated by white matter streamline counts between each pair of ROIs. Global and regional strength were calculated for each modality. NBSm was used to find significant overlap between functional and anatomical components that distinguished health from schizophrenia. Global strength was decreased in patients in both functional and anatomical networks. Regional strength was decreased in all regions in functional networks and only one region in anatomical networks. NBSm identified a distinguishing functional component consisting of 46 nodes with 113 links (p < 0.001), a distinguishing anatomical component with 47 nodes and 50 links (p = 0.002), and a distinguishing intermodal component with 26 nodes (p < 0.001). NBSm is a powerful technique for understanding network-based group differences present in both anatomical and functional data. In light of the dysconnectivity hypothesis, these results provide compelling evidence for the presence of significant overlapping anatomical and functional disruption in people with schizophrenia.
Highlights
Schizophrenia is characterized by a host of observable abnormalities in integrated thought, emotion, and behavior
The hypothesis that dysconnectivity drives psychiatric symptoms in schizophrenia is supported by neuroimaging studies utilizing both functional magnetic resonance imaging and diffusion tensor imaging (Friston, 1998; Volkow et al, 1988; Weinberger et al, 1992)
In a second-level analysis, we evaluated the overlap of significantly different, connected regions of interest (ROI) between modalities using a multimodal extension NBSm which we develop for this study
Summary
Schizophrenia is characterized by a host of observable abnormalities in integrated thought, emotion, and behavior. Dysconnectivity can manifest separately as either differences in coherent brain activity (functional connectivity) or brain wiring (anatomical connectivity) (Camchong et al, 2011; Skudlarski et al, 2010). While dysconnectivity can accompany many different disease states, the specific connectivity abnormalities identified in schizophrenia patients remain far from understood. Alterations in functional connectivity have been identified across a range of brain states, affecting the default mode network (DMN) at rest (Whitfield-Gabrieli et al, 2009; Woodward et al, 2011), multiple cognitive control networks (Repovs et al, 2011), and several independent brain regions (Zhou et al, 2007). The focus of much of this previous work has been limited to a few selected regions or tracts and to a single imaging modality, potentially hampering a broader understanding of a distributed pathophysiology
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