Network Analysis of Symptom Comorbidity in Schizophrenia: Relationship to Illness Course and Brain White Matter Microstructure

Author:

Ye Hua1,Zalesky Andrew12,Lv Jinglei3,Loi Samantha M1,Cetin-Karayumak Suheyla4,Rathi Yogesh45,Tian Ye1,Pantelis Christos1ORCID,Di Biase Maria A14ORCID

Affiliation:

1. Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia

2. Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia

3. School of Biomedical Engineering & Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia

4. Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA

5. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA

Abstract

Abstract Introduction Recent network-based analyses suggest that schizophrenia symptoms are intricately connected and interdependent, such that central symptoms can activate adjacent symptoms and increase global symptom burden. Here, we sought to identify key clinical and neurobiological factors that relate to symptom organization in established schizophrenia. Methods A symptom comorbidity network was mapped for a broad constellation of symptoms measured in 642 individuals with a schizophrenia-spectrum disorder. Centrality analyses were used to identify hub symptoms. The extent to which each patient’s symptoms formed clusters in the comorbidity network was quantified with cluster analysis and used to predict (1) clinical features, including illness duration and psychosis (positive symptom) severity and (2) brain white matter microstructure, indexed by the fractional anisotropy (FA), in a subset (n = 296) of individuals with diffusion-weighted imaging (DWI) data. Results Global functioning, substance use, and blunted affect were the most central symptoms within the symptom comorbidity network. Symptom profiles for some patients formed highly interconnected clusters, whereas other patients displayed unrelated and disconnected symptoms. Stronger clustering among an individual’s symptoms was significantly associated with shorter illness duration (t = 2.7; P = .0074), greater psychosis severity (ie, positive symptoms expression) (t = −5.5; P < 0.0001) and lower fractional anisotropy in fibers traversing the cortico-cerebellar-thalamic-cortical circuit (r = .59, P < 0.05). Conclusion Symptom network structure varies over the course of schizophrenia: symptom interactions weaken with increasing illness duration and strengthen during periods of high positive symptom expression. Reduced white matter coherence relates to stronger symptom clustering, and thus, may underlie symptom cascades and global symptomatic burden in individuals with schizophrenia.

Funder

Australian Schizophrenia Research Bank

National Health and Medical Research Council

Pratt Foundation

Ramsay Health Care

Viertel Charitable Foundation

Schizophrenia Research Institute

NSW Ministry of Health

Australian National Health

Medical Research Council

Publisher

Oxford University Press (OUP)

Subject

Psychiatry and Mental health

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