Abnormal topological organization of structural brain networks in schizophrenia

Zhang, Yuanchao; Lin, Lei; Lin, Ching Po; Zhou, Yuan; Chou, Kun Hsien; Lo, Chun Yi Zac; Su, Tung Ping; Jiang, Tianzi

doi:10.1016/j.schres.2012.08.021

Cited by 134 publications

(125 citation statements)

References 73 publications

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“…However, the present work is in line with several previous studies investigating intracerebellar connections using fiber tractography. 21,56,57 In this study, we hypothesized that schizophrenia patients would show disturbed cerebellar structural network topology, supporting evidence of previous structural network studies on the cortical level 46,47 and underlying cerebellar abnormalities in schizophrenia. We also hypothesized that the schizophrenia patients would show abnormal microstructural integrity in the cerebellum and that these abnormalities would be associated with measures of cerebellar network topology and neuropsychiatric symptom scores.…”

Section: Introductionsupporting

confidence: 83%

Disrupted Modular Architecture of Cerebellum in Schizophrenia: A Graph Theoretic Analysis

Kim

Kent

Bolbecker

et al. 2014

Schizophrenia Bulletin

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Recent studies of schizophrenia have revealed cognitive and memory deficits that are accompanied by disruptions of neuronal connectivity in cortical and subcortical brain regions. More recently, alterations of topological organization of structural networks in schizophrenia are also being identified using graph theoretical analysis. However, the role of the cerebellum in this network structure remains largely unknown. In this study, global network measures obtained from diffusion tensor imaging were computed in the cerebella of 25 patients with schizophrenia and 36 healthy volunteers. While cerebellar global network characteristics were slightly altered in schizophrenia patients compared with healthy controls, the patients showed a retained small-world network organization. The modular architecture, however, was changed mainly in crus II. Furthermore, schizophrenia patients had reduced correlations between modularity and microstructural integrity, as measured by fractional anisotropy (FA) in lobules I-IV and X. Finally, FA alterations were significantly correlated with the Positive and Negative Syndrome Scale symptom scores in schizophrenia patients. Taken together, our data suggest that schizophrenia patients have altered network architecture in the cerebellum with reduced local microstructural connectivity and that cerebellar structural abnormalities are associated symptoms of the disorder.

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Section: Introductionsupporting

confidence: 83%

Disrupted Modular Architecture of Cerebellum in Schizophrenia: A Graph Theoretic Analysis

Kim

Kent

Bolbecker

et al. 2014

Schizophrenia Bulletin

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“…(i) In sMRI, structural connectivity is indirectly estimated by calculating interregional correlation of specific morphometric parameters, such as gray matter volume or cortical thickness [71][72][73]. In this method, a measure (such as gray matter volume, cortical thickness, or other similar metrics) of each brain region is extracted, and then correlations between two brain regions are calculated as the edges of the brain network of interest.…”

Section: Edges Based On Structuralmentioning

confidence: 99%

“…Thus, the resulting connectivity matrix of each brain is symmetric and generates a weighted undirected network. In some studies, the weighted undirected resulting networks were converted into binary undirected networks by a specific threshold [72,83,84].…”

Section: Edges Based On Structuralmentioning

confidence: 99%

“…In addition, in the SCZ group, abnormal multimodal network organization showed reduced hierarchy, the loss of frontal and the emergence of nonfrontal hubs, and increased connection distance. Zhang et al [72] hypothesized that the core symptoms of SCZ originate from the inability to integrate information transmission segregated across different brain regions. To demonstrate this hypothesis, they constructed structural brain networks of SCZ patients using the cortical thickness measurement and found increased characteristic path length and clustering coefficient in the SCZ group compared with the HC group.…”

Section: Structural Brain Network In Sczmentioning

confidence: 99%

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Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

et al. 2017

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It is well known that most brain disorders are complex diseases, such as Alzheimer's disease (AD) and schizophrenia (SCZ). In general, brain regions and their interactions can be modeled as complex brain network, which describe highly efficient information transmission in a brain. Therefore, complex brain network analysis plays an important role in the study of complex brain diseases. With the development of noninvasive neuroimaging and electrophysiological techniques, experimental data can be produced for constructing complex brain networks. In recent years, researchers have found that brain networks constructed by using neuroimaging data and electrophysiological data have many important topological properties, such as small-world property, modularity, and rich club. More importantly, many brain disorders have been found to be associated with the abnormal topological structures of brain networks. These findings provide not only a new perspective to explore the pathological mechanisms of brain disorders, but also guidance for early diagnosis and treatment of brain disorders. The purpose of this survey is to provide a comprehensive overview for complex brain network analysis and its applications to brain disorders.

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“…This apparent discrepancy between findings from structural and functional studies may result from either pathophysiological processes or merely represent an artefact of different methods applied to fMRI and dwM-RI data when mapping brain connectivity. Although a detailed discussion on this topic is beyond the scope of this review (for an in-depth overview see [92]), several lines of evidence suggest that a critical factor in determining these topological alterations appears to be a core disturbance of hub node organization schizophrenia [82,93,94]. Collectively, these findings indicate that deficient wiring of these hubs and their rich clubs can dysregulate communication across widespread areas, causing complex changes in brain dynamics involving both abnormal increases and decreases in functional connectivity.…”

Section: Imaging Connectomics and Brain Networkmentioning

confidence: 99%

The quest for biomarkers in Schizophrenia: from neuroimaging to machine learning

Bajouco¹,

Mota²,

Coroa³

et al. 2017

IJCNMH

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Schizophrenia is a severe mental disorder and one of the leading causes of disease burden worldwide. It represents a source of significant suffering and disability to the affected individuals, and is associated with substantial societal and economical costs.The diagnosis of schizophrenia still depends exclusively on the detection of symptoms that are also present in other mental disorders. This situation causes overlapping of the boundaries of the diagnostic categories and constitutes a source of diagnostic errors. Moreover, current treatment algorithms do not take into account the substantial interindividual variability in response to antipsychotic drugs. As a result, around one-third of patients are treatment-resistant to first line antipsychotic drugs. This deleterious consequence is associated with poor individual outcomes and elevated healthcare costs.Neuroimaging research in schizophrenia has shed some light in a vast array of structural and functional connectivity abnormalities and neurochemical (dopamine and glutamate) imbalances, which may constitute 'organic surrogates' of this disorder. However, the neuroimaging field, so far, has not been able to identify biomarkers that could facilitate early detection and allow individualised treatment management. This paper reviews neuroimaging studies from different modalities that may provide relevant biomarkers for schizophrenia. We discuss how the current application of novel Machine Learning methods to the analyses of imaging data is allowing the translation of such findings into potential biomarkers enabling the prediction of clinical outcomes at the individual level, towards the development of innovative and personalised treatment strategies.

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Abnormal topological organization of structural brain networks in schizophrenia

Cited by 134 publications

References 73 publications

Disrupted Modular Architecture of Cerebellum in Schizophrenia: A Graph Theoretic Analysis

Disrupted Modular Architecture of Cerebellum in Schizophrenia: A Graph Theoretic Analysis

Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

The quest for biomarkers in Schizophrenia: from neuroimaging to machine learning

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