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Cereb Cortex. 2018 Dec 1;28(12):4234-4243. doi: 10.1093/cercor/bhx278.

Large-Scale Network Topology Reveals Heterogeneity in Individuals With at Risk Mental State for Psychosis: Findings From the Longitudinal Youth-at-Risk Study.

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Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorder Program, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
Research Division, Institute of Mental Health, Singapore, Singapore.
Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
School of Psychology, University of Birmingham, Edgbaston, UK.
Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Parkville VIC, Australia.
Monash Clinical and Imaging Neuroscience, School of Psychology and Psychiatry & Monash Biomedical Imaging, Monash University, Clayton VIC, Australia.
Clinical Imaging Research Centre, The Agency for Science, Technology and Research and National University of Singapore, Singapore, Singapore.


Emerging evidence demonstrates heterogeneity in clinical outcomes of prodromal psychosis that only a small percentage of at-risk individuals eventually progress to full-blown psychosis. To examine the neurobiological underpinnings of this heterogeneity from a network perspective, we tested whether the early patterns of large-scale brain network topology were associated with risk of developing clinical psychosis. Task-free functional MRI data were acquired from subjects with At Risk Mental State (ARMS) for psychosis and healthy controls (HC). All individuals had no history of drug abuse and were not on antipsychotics. We performed functional connectomics analysis to identify patterns of system-level functional brain dysconnectivity associated with ARMS individuals with different outcomes. In comparison to HC and ARMS who did not transition to psychosis at follow-up (ARMS-NT), ARMS individuals who did (ARMS-T) showed marked brain functional dysconnectivity, characterized by loss of network segregation and disruption of network communities, especially the salience, default, dorsal attention, sensorimotor and limbic networks (P < 0.05 FWE-corrected, Cohen's d > 1.00), and was associated with baseline symptom severity. In contrast, we did not observe connectivity differences between ARMS-NT and HC individuals. Taken together, these results suggest a possible large-scale functional brain network topology phenotype related to risk of psychosis transition in ARMS individuals.


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