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

Research output: Contribution to journalArticlepeer-review

Authors

  • George Gifford
  • Nicolas Crossley
  • Sarah Morgan
  • Matthew J. Kempton
  • Paola Dazzan
  • Gemma Modinos
  • Matilda Azis
  • Carly Samson
  • Ilaria Bonoldi
  • Beverly Quinn
  • Sophie E. Smart
  • Mathilde Antoniades
  • Matthijs G. Bossong
  • Jesus Perez
  • Oliver D. Howes
  • James M. Stone
  • Paul Allen
  • Anthony A. Grace
  • Philip McGuire

Colleges, School and Institutes

External organisations

  • King's College London
  • Pontificia Universidad Católica de Chile
  • University of Cambridge
  • The Alan Turing Institute
  • South London and Maudsley NHS Foundation Trust
  • Cambridgeshire and Peterborough NHS Foundation Trust
  • Cardiff University
  • Icahn School of Medicine at Mount Sinai
  • University Medical Center Utrecht
  • Roehampton Institute
  • University of Pittsburgh

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.

Bibliographic note

Funding Information: This work was supported by a Wellcome Trust Programme Grant (grant number 091667, 2011). George Gifford is supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Heath Research and Care South London at King's College Hospital NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. GM is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 202397/Z/16/Z). We thank the study volunteers for their participation and the members of OASIS, CAMEO, West London Early Intervention Service, and Warwick & Coventry clinical teams for enabling this research. O. D. H. has received investigator‐initiated research funding from and/or participated in advisory/speaker meetings organised by Astra‐Zeneca, Autifony, BMS, Eli Lilly, Heptares, Jansenn, Lundbeck, Lyden‐Delta, Otsuka, Servier, Sunovion, Rand and Roche. AAG receives consulting fees from Johnson & Johnson, Lundbeck, Pfizer, Takeda, Alkermes, Otsuka, Lilly, Roche, Asubio. The other authors declare no competing financial interests. Publisher Copyright: © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Details

Original languageEnglish
Pages (from-to)439-451
Number of pages13
JournalHuman Brain Mapping
Volume42
Issue number2
Early online date13 Oct 2020
Publication statusPublished - 1 Feb 2021

Keywords

  • cartographic profile, clinical high-risk for psychosis, network analysis, network based statistics, network integration, task fMRI