Liu, Y. et al. Disrupted small-world networks in schizophrenia. Brain 131, 945-961

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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    • "They are subject to selective attack in the networks during pathophysiological processes (Bullmore and Sporns, 2012). In what may be a tradeoff between adaptive value and wiring cost, psychiatric disorders such as schizophrenia have been reported to be associated with abnormal shifts in small-world properties (Bassett et al., 2008; Bullmore and Sporns, 2012; Liu et al., 2008). Alternatively, synaptic pruning (which results in the reduction of fiber connections and gray matter density) is known to occur during the normative development of the brain from childhood through adolescence (Penzes et al., 2011); thus, the upwards-shifted small world topology in the UHR offspring may be due to disruptions in synaptic pruning that can lead to brain networks of higher clustering and efficiency. "
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    ABSTRACT: Background: Validating the high-risk (HR) and ultra-high-risk (UHR) stages of bipolar disorder (BP) may help enable early intervention strategies. Methods: We followed up with 44 offspring of parents with BP, subdividing into the HR and UHR categories. The offspring were aged 8-28 years and were free of any current DSM-IV diagnoses. Our multilevel, integrative approach encompassed gray matter (GM) volumes, brain network connectivity, neuropsychological performance, and clinical outcomes. Findings: Compared with the healthy controls (HCs) (n = 33), the HR offspring (n = 26) showed GM volume reductions in the right orbitofrontal cortex. Compared with the HR offspring, the UHR offspring (n = 18) exhibited increased GM volumes in four regions. Both the HR and UHR offspring displayed abnormalities in the inferior occipital cortex regarding the measures of degree and centrality, reflecting the connections and roles of the region, respectively. In the UHR versus the HR offspring, the UHR offspring exhibited upwards-shifted small world topologies that reflect high clustering and efficiency in the brain networks. Compared with the HCs, the UHR offspring had significantly lower assortativity, which was suggestive of vulnerability. Finally, processing speed, visual-spatial, and general function were impaired in the UHR offspring but not in the HR offspring. Interpretation: The abnormalities observed in the HR offspring appear to be inherited, whereas those associated with the UHR offspring represent stage-specific changes predisposing them to developing the disorder.
    10/2015; 2(8):917-26. DOI:10.1016/j.ebiom.2015.06.027
    • "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; 168(1). 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; 168(1). DOI:10.1016/j.schres.2015.08.025 · 3.92 Impact Factor
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