Graph-based network analysis in schizophrenia

Micheloyannis, Sifis

doi:10.5498/wjp.v2.i1.1

Cited by 48 publications

(45 citation statements)

References 94 publications

(137 reference statements)

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“…2010; Lynall et al. 2010; Micheloyannis 2012). The clustering coefficient is a measure of functional segregation, which is the ability for specialized processing to occur within highly interconnected brain regions (Rubinov and Sporns 2010).…”

Section: Discussionmentioning

confidence: 99%

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

et al. 2016

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BackgroundPrevious research has shown that the human brain can be represented as a complex functional network that is characterized by specific topological properties, such as clustering coefficient, characteristic path length, and global/local efficiency. Patients with psychotic disorder may have alterations in these properties with respect to controls, indicating altered efficiency of network organization. This study examined graph theoretical changes in relation to differential genetic risk for the disorder and aimed to identify clinical correlates.MethodsAnatomical and resting‐state MRI brain scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings, and 72 controls. Topological measures (i.e., clustering coefficient, characteristic path length, and small‐worldness) were used as dependent variables in a multilevel random regression analysis to investigate group differences. In addition, associations with (subclinical) psychotic/cognitive symptoms were examined.ResultsPatients had a significantly lower clustering coefficient compared to siblings and controls, with no difference between the latter groups. No group differences were observed for characteristic path length and small‐worldness. None of the topological properties were associated with (sub)clinical psychotic and cognitive symptoms.ConclusionsThe reduced ability for specialized processing (reflected by a lower clustering coefficient) within highly interconnected brain regions observed in the patient group may indicate state‐related network alterations. There was no evidence for an intermediate phenotype and no evidence for psychopathology‐related alterations.

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Section: Discussionmentioning

confidence: 99%

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

et al. 2016

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“…Connectivity at rest and during cognitive tasks has been the subject of various researches, based on different methodological approaches (Sporns et al 2000;Ioannides et al 2012;Stam et al 2007a, b;Micheloyannis 2012;Dimitriadis et al 2009Dimitriadis et al , 2010aDimitriadis et al , b, 2012aTurk-Browne 2013). Graph theoretic principles and algorithms are in the heart of all these approaches that are aimed at anatomical (He et al 2007;Hagmann et al 2008) and functional networks defined empirically from EEG, MEG and fMRI recordings (Basset and Bullmore 2006;Stam et al 2007a, b).…”

Section: Introductionmentioning

confidence: 99%

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

2015

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We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8-12 years) and 25 students (21-26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as "network-level microstates". The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.

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“…The models we used in our present study were based on the features of conceptually reconstructed brain networks. Despite the large number of reports to date on the modern network analysis of the brain with schizophrenia (Micheloyannis, ), most have only focused on group comparisons of network properties between schizophrenia patients and healthy controls. Therefore, the possibility is noticeable that schizophrenia patients and healthy controls can actually be classified on the basis of these network properties.…”

Section: Discussionmentioning

confidence: 99%

Diagnosing schizophrenia with network analysis and a machine learning method

Joo

Shon

et al. 2020

Int J Methods Psych Res

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Objective Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research. Methods We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls. Results In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns. Conclusion In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.

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Graph-based network analysis in schizophrenia

Cited by 48 publications

References 94 publications

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

Diagnosing schizophrenia with network analysis and a machine learning method

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