The Canadian Journal of Psychiatry · 2019 · 22 citations · 33 references
MedicineNeurosciencePsychiatryClinical NeuroscienceNeurogeneticsNeuropsychiatryBrain ConnectivityBrain Network AnalysisFunctional ConnectivityFunctional NeuroimagingGraph TheorySchizophrenia Spectrum DisordersBetween-group ComparisonFunctional Brain ConnectivityFunctional Brain NetworksFunctional Connectivity MriFunctional Magnetic Resonance ImagingGlobal EfficiencyResting-state ConnectivitySchizophrenia Comorbidity
BACKGROUND: The functional dysconnectivity observed from functional magnetic resonance imaging (fMRI) studies in schizophrenia is also seen in unaffected siblings indicating its association with the genetic diathesis. We intended to apportion resting-state dysconnectivity into components that represent genetic diathesis, clinical expression or treatment effect, and resilience. METHODS: fMRI data were acquired from 28 schizophrenia patients, 28 unaffected siblings, and 60 healthy controls. Based on Dosenbach's atlas, we extracted time series of 160 regions of interest. After constructing functional network, we investigated between-group differences in strength and diversity of functional connectivity and topological properties of undirected graphs. RESULTS: tests revealed that 62.5% were associated with genetic diathesis and 21.6% were associated with clinical expression. Topologically, we observed increased degree, clustering coefficient, and global efficiency in the sibling group compared to both patients and controls. CONCLUSION: A large portion of the resting-state functional dysconnectivity seen in patients represents a genetic diathesis effect. The most prominent network-level disruption is the dysconnectivity among nodes of the default mode and salience networks. Despite their predisposition, unaffected siblings show a pattern of resilience in the emergent connectomic topology. Our findings could potentially help refine imaging genetics approaches currently used in the pursuit of the pathophysiology of schizophrenia.
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Complex network measures of brain connectivity: Uses and interpretations
Mikail Rubinov, Olaf Sporns · NeuroImage · 2009 · 12K citations