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Liang M, Zhou Y, Jiang T, Liu Z, Tian L, Liu H et al. Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. Neuroreport 17: 209-213

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China.
Neuroreport (Impact Factor: 1.52). 03/2006; 17(2):209-13. DOI: 10.1097/01.wnr.0000198434.06518.b8
Source: PubMed

ABSTRACT

Using resting-state functional magnetic resonance imaging, we examined the functional connectivity throughout the entire brain in schizophrenia. The abnormalities in functional connectivity were identified by comparing the correlation coefficients of each pair of 116 brain regions between 15 patients and 15 controls. Then, the global distribution of the abnormal functional connectivities was examined. Experimental results indicated, in general, a decreased functional connectivity in schizophrenia during rest, and such abnormalities were widely distributed throughout the entire brain rather than restricted to a few specific brain regions. The results provide a quantitative support for the hypothesis that schizophrenia may arise from the disrupted functional integration of widespread brain areas.

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    • "Besides DMN, investigation of other networks also revealed altered functional connectivity between brain regions (Liang et al., 2006; Zhou et al., 2008a, 2008b). For instance, decreased functional connectivity among insula, prefrontal lobe and temporal lobe was observed along with increased connectivity from many cerebral cortical regions toward cerebellum (Liang et al., 2006). The introduction of graph theoretical approaches applied to the brain has allowed quantitative analysis of local and global network properties derived from functional and structural brain imaging (Bullmore and Sporns, 2009; Lynall et al., 2010; Sporns, 2010; Supekar et al., 2008; van den Heuvel et al., 2013). "
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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.
    No preview · Article · Aug 2015 · Schizophrenia Research
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    • "Several studies observe a reduction in overall strength of functional connectivity in schizophrenia (Argyelan et al., 2013; Bassett et al., 2012; Lynall et al., 2010), while both increased (Skudlarski et al., 2010; Whitfield-Gabrieli et al., 2009) and decreased (Bluhm et al., 2007; Liang et al., 2006) connectivity involving different regional connections are noted across the brain (Karbasforoushan and Woodward, 2012; Pettersson-Yeo et al., 2011; Rubinov and Bullmore, 2013). The presence of both hyper-and hypoconnectivity involving different regional connections (Guo et al., 2013; Skudlarski et al., 2010; Venkataraman et al., 2012; Woodward et al., 2012) indicates a large diversity in the distribution of connectivity across the functional links in schizophrenia. "
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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.
    Full-text · Article · Aug 2015 · Human Brain Mapping
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    • "In that study, the hypothesis on dysfunctional network integration in SZ was supported by the lower strength of the FC in the pairs of nodes and decreased synchronization of functionally connected brain regions as well as the longer absolute path to reach global functional networks (Bullmore et al., 1997, 1998; Calhoun et al., 2009; Friston and Frith, 1995; Liu et al., 2008). In addition, Liang et al. (2006) reported that aberrant SZ-associated FC patterns were widely distributed throughout the entire brain (i.e., the FC levels of approximately 89% of the observed pairs of nodes were decreased), as opposed to showing a restricted pattern within only a few specific brain regions. Machine-learning algorithms have been successfully deployed in the automated classification of altered FC patterns related to SZ (Arbabshirani et al., 2013; Du et al., 2012; Shen et al., 2010; Tang et al., 2012; Watanabe et al., 2014). "
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    ABSTRACT: Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. Copyright © 2015. Published by Elsevier Inc.
    Full-text · Article · May 2015 · NeuroImage
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