Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis.

Matthew R. Broome ,George Gifford,Paul Allen,Philip McGuire,Carly Samson,James Stone ,Oliver Howes ,Ilaria Bonoldi,Beverly Quinn,Sophie Smart ,Mathilde Antoniades,Sarah E. Morgan ,Nicolás Crossley ,Matthijs G. Bossong ,Matthew J. Kempton ,Gemma Modinos,Matilda Azis,Jesús Pérez ,Paola Dazzan,Anthony A. Grace

doi:10.1002/hbm.25235

Abstract

The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the ‘cartographic profile’ of time windows and k‐means clustering, and sub‐network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub‐network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub‐network comprised brain areas implicated in bottom‐up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.

Highlights

A key challenge in the clinical management of people at clinical high risk (CHR) for psychosis is that it is difficult to predict whether their presenting symptoms will improve, persist, or progress to a frank psychotic disorder, and if their overall level of functioning will improve or deteriorate (Fusar-Poli et al, 2012; Simon et al, 2013)
Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC)
Within the CHR group, using integrated FC networks, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms

Summary

Introduction

A key challenge in the clinical management of people at clinical high risk (CHR) for psychosis is that it is difficult to predict whether their presenting symptoms will improve, persist, or progress to a frank psychotic disorder, and if their overall level of functioning will improve or deteriorate (Fusar-Poli et al, 2012; Simon et al, 2013). This has led to a search for biological measures that might help clinicians to predict clinical outcomes in this group (Gifford et al, 2017). Neuropsychological assessments in CHR subjects indicate that this state is associated with objective cognitive impairments across multiple domains (Fusar-Poli et al, 2012), including attention and vigilance (Zheng et al, 2018)

Objectives

Methods

Findings

Conclusion