Structural connectivity analysis reveals topological aberrations in patients with schizophrenia

Yu Sun, Renick Lee, Kaiquan Shen, Anastasios Bezerianos, Nitish Thakor, Kang Sim

Research output: Contribution to journalArticlepeer-review

Abstract

Topological analysis and the associated parameters allow elucidation of brain networks in health and illness. Evidently useful measures for defining network competency such as small-worldness can potentially improve understanding of brain connectivity and their disruptions underlying neuropsychiatric conditions such as schizophrenia. In the current study, we assessed the structural differences of brain networks in schizophrenia patients as compared with healthy controls. As proof of concept investigation, diffusion tensor imaging recordings from 2 schizophrenia patients and 2, gender and age matched, control subjects were subjected to analysis using several graph network distance metrics. Among them, those that appeared to have the ability to encode and highest sensitivity in shedding light about anatomical changes in neuron deficiency were the shortest path length and clustering coefficient parameters. Schizophrenia patients displayed comparatively lower clustering coefficient, longer path lengths and hence reduced small-worldness. These results suggest aberrant topological architecture in the structural brain networks of patients with schizophrenia, which may impact the psychopathological and cognitive manifestations of this potentially crippling illness.

ASJC Scopus subject areas

  • Medicine(all)

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