Disrupted small-world networks in schizophrenia

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
Brain (Impact Factor: 9.2). 05/2008; 131(Pt 4):945-61. DOI: 10.1093/brain/awn018
Source: PubMed


The human brain has been described as a large, sparse, complex network characterized by efficient small-world properties, which assure that the brain generates and integrates information with high efficiency. Many previous neuroimaging studies have provided consistent evidence of 'dysfunctional connectivity' among the brain regions in schizophrenia; however, little is known about whether or not this dysfunctional connectivity causes disruption of the topological properties of brain functional networks. To this end, we investigated the topological properties of human brain functional networks derived from resting-state functional magnetic resonance imaging (fMRI). Data was obtained from 31 schizophrenia patients and 31 healthy subjects; then functional connectivity between 90 cortical and sub-cortical regions was estimated by partial correlation analysis and thresholded to construct a set of undirected graphs. Our findings demonstrated that the brain functional networks had efficient small-world properties in the healthy subjects; whereas these properties were disrupted in the patients with schizophrenia. Brain functional networks have efficient small-world properties which support efficient parallel information transfer at a relatively low cost. More importantly, in patients with schizophrenia the small-world topological properties are significantly altered in many brain regions in the prefrontal, parietal and temporal lobes. These findings are consistent with a hypothesis of dysfunctional integration of the brain in this illness. Specifically, we found that these altered topological measurements correlate with illness duration in schizophrenia. Detection and estimation of these alterations could prove helpful for understanding the pathophysiological mechanism as well as for evaluation of the severity of schizophrenia.

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Available from: Yong Liu, Sep 19, 2014
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    • "c o m / l o c a t e / s c h r e s usually characterized by the correlation of time courses between brain regions (Biswal et al., 1995). Brain network analyses have revealed a disruption of the functional and structural network structure in schizophrenia (Liu et al., 2008; Rubinov and Bullmore, 2013) including decreased clustering and small-worldness, and reduced presence of high-degree hubs. In addition, local differences of reduced degree and clustering were found in medial parietal, premotor and cingulate, and right orbitofrontal cortical nodes (Lynall et al., 2010). "
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    ABSTRACT: A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy. Copyright © 2015. Published by Elsevier B.V.
    Schizophrenia Research 08/2015; DOI:10.1016/j.schres.2015.08.011 · 3.92 Impact Factor
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    • "roup differences in connectivity density have been reported by van Den Heuvel et al . , 2010 ; Yu et al . , 2011 ) . Our recent - onset patients showed a significantly reduced degree in specific anatomical areas , particularly targeting the superior temporal and inferior frontal lobes , which is in line with other studies ( Collin et al . , 2013 ; Liu et al . , 2008 ; Lynall et al . , 2010 ; Skudlarski et al . , 2010 ; Van Den Heuvel et al . , 2010 ; Zalesky et al . , 2010a ) . We found that schizotypal cognitions as measured with the RISC , which was previously reported to have the best positive predictive value for schizophrenia in the EHRS ( Johnstone , 2005 ) , were significant - ly related to "
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    ABSTRACT: Grey matter brain networks are disrupted in schizophrenia, but it is still unclear at which point during the development of the illness these disruptions arise and whether these can be associated with behavioural predictors of schizophrenia. We investigated if single-subject grey matter networks were disrupted in a sample of people at familial risk of schizophrenia. Single-subject grey matter networks were extracted from structural MRI scans of 144 high risk subjects, 32 recent-onset patients and 36 healthy controls. The following network properties were calculated: size, connectivity density, degree, path length, clustering coefficient, betweenness centrality and small world properties. People at risk of schizophrenia showed decreased path length and clustering in mostly prefrontal and temporal areas. Within the high risk sample, the path length of the posterior cingulate cortex and the betweenness centrality of the left inferior frontal operculum explained 81% of the variance in schizotypal cognitions, which was previously shown to be the strongest behavioural predictor of schizophrenia in the study. In contrast, local grey matter volume measurements explained 48% of variance in schizotypy. The present results suggest that single-subject grey matter networks can quantify behaviourally relevant biological alterations in people at increased risk for schizophrenia before disease onset. Copyright © 2015 Elsevier B.V. All rights reserved.
    Schizophrenia Research 08/2015; DOI:10.1016/j.schres.2015.08.025 · 3.92 Impact Factor
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    • "In particular, the architecture of functional connectivity in patients is significantly affected by duration of illness. Patients with longer duration of illness show reduced segregation and integration (Liu et al., 2008), and reduced connectivity among core brain hubs (Collin et al., 2013). In light of these observations, we expected a moderating effect of illness duration on FCE. "
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    ABSTRACT: Spatial variation in connectivity is an integral aspect of the brain's architecture. In the absence of this variability, the brain may act as a single homogenous entity without regional specialization. In this study, we investigate the variability in functional links categorized on the basis of the presence of direct structural paths (primary) or indirect paths mediated by one (secondary) or more (tertiary) brain regions ascertained by diffusion tensor imaging. We quantified the variability in functional connectivity using an unbiased estimate of unpredictability (functional connectivity entropy) in a neuropsychiatric disorder where structure-function relationship is considered to be abnormal; 34 patients with schizophrenia and 32 healthy controls underwent DTI and resting state functional MRI scans. Less than one-third (27.4% in patients, 27.85% in controls) of functional links between brain regions were regarded as direct primary links on the basis of DTI tractography, while the rest were secondary or tertiary. The most significant changes in the distribution of functional connectivity in schizophrenia occur in indirect tertiary paths with no direct axonal linkage in both early (P = 0.0002, d = 1.46) and late (P = 1 × 10(-17) , d = 4.66) stages of schizophrenia, and are not altered by the severity of symptoms, suggesting that this is an invariant feature of this illness. Unlike those with early stage illness, patients with chronic illness show some additional reduction in the distribution of connectivity among functional links that have direct structural paths (P = 0.08, d = 0.44). Our findings address a critical gap in the literature linking structure and function in schizophrenia, and demonstrate for the first time that the abnormal state of functional connectivity preferentially affects structurally unconstrained links in schizophrenia. It also raises the question of a continuum of dysconnectivity ranging from less direct (structurally unconstrained) to more direct (structurally constrained) brain pathways underlying the progressive clinical staging and persistence of schizophrenia. Hum Brain Mapp, 2015. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
    Human Brain Mapping 08/2015; DOI:10.1002/hbm.22932 · 5.97 Impact Factor
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