Resting-state network connectivity and metastability predict clinical symptoms in schizophrenia

Won Hee Lee, Gaelle E. Doucet, Evan Leibu, Sophia Frangou

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Background: The functional architecture of resting-state networks (RSNs) is defined by their connectivity and metastability. Disrupted RSN connectivity has been amply demonstrated in schizophrenia while the role of metastability remains poorly defined. Here, we undertake a comprehensive characterisation of RSN organization in schizophrenia and test its contribution to the clinical profile of this disorder. Methods: We extracted RSNs representing the default mode (DMN), central executive (CEN), salience (SAL), language (LAN), sensorimotor (SMN), auditory (AN) and visual (VN) networks from resting-state functional magnetic resonance imaging data obtained from patients with schizophrenia (n = 85) and healthy individuals (n = 48). For each network, we computed its functional cohesiveness and integration and used the Kuramoto order parameter to compute metastability. We used stepwise multiple regression analyses to test these RSN features as predictors of symptom severity in patients. Results: RSN features respectively explained 14%, 17%, 12% and 5% of the variance in positive, negative, anxious/depressive and agitation/disorganization symptoms. Lower functional integration between the DMN, CEN and SMN primarily contributed to positive symptoms. The functional properties of the SAL network were key predictors of all other symptom dimensions; specifically, lower cohesiveness of the SAL, lower integration of this network with the LAN and higher integration with the CEN respectively contributed to negative, anxious/depressive and disorganization symptoms. Increased SAL metastability was associated with negative symptoms. Conclusions: These results confirm the primacy of the SAL network for schizophrenia and demonstrate that abnormalities in RSN connectivity and metastability are significant predictors of schizophrenia-related psychopathology.

Original languageEnglish (US)
Pages (from-to)208-216
Number of pages9
JournalSchizophrenia Research
Volume201
DOIs
StatePublished - Nov 2018
Externally publishedYes

Keywords

  • Functional connectivity
  • Kuramoto order parameter
  • Metastability
  • Negative symptoms
  • Psychosis
  • Synchrony

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry

Fingerprint Dive into the research topics of 'Resting-state network connectivity and metastability predict clinical symptoms in schizophrenia'. Together they form a unique fingerprint.

Cite this