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Disrupted small-world networks in schizophrenia

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
Brain (Impact Factor: 10.23). 05/2008; 131(Pt 4):945-61. DOI: 10.1093/brain/awn018
Source: PubMed

ABSTRACT 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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    • "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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    • ", 2010 for review ) or degree of synaptic connectivity ( e . g . , Ermentrout , 1998 ; Golomb and Ermentrout , 1999 ) that we explicitly attempted to manipulate here . When those mechanisms including structural and oscillatory dynamics break down , the result may lead to a variety of neurophysiological disorders including schizophrenia ( e . g . , Liu et al . , 2008b ; Lynall et al . , 2010 ) in which functional dysconnectivity is thought to play a role ( Stephan et al . , 2009 ; Phillips and Uhlhaas , 2015 ) , autism ( Uhlhaas and Singer , 2006 ; Rippon et al . , 2007 ; Uhlhaas et al . , 2009 ) among other neurological diseases and disorders ( He et al . , 2007 , 2009 ) . In this study , we selecti"
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    • "Furthermore, abnormal small-world properties have been found in neuropsychiatric disorders, including Alzheimer's disease [Supekar et al., 2008], schizophrenia [Liu et al., 2008], epilepsy [Liao et al., 2010], and depression [Zhang et al., 2011]. Although different brain diseases show different changes, the topology of the functional network of an abnormal brain can be regarded as less optimal the more it deviates from small-world network topology, suggesting both a possible role in pathophysiology and potential use as a biomarker. "
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