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

TY - JOUR

T1 - Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis

AU - Gifford, George

AU - Crossley, Nicolas

AU - Morgan, Sarah

AU - Kempton, Matthew J

AU - Dazzan, Paola

AU - Modinos, Gemma

AU - Azis, Matilda

AU - Samson, Carly

AU - Bonoldi, Ilaria

AU - Quinn, Beverly

AU - Smart, Sophie E

AU - Antoniades, Mathilde

AU - Bossong, Matthijs G

AU - Broome, Matthew R

AU - Perez, Jesus

AU - Howes, Oliver D

AU - Stone, James M

AU - Allen, Paul

AU - Grace, Anthony A

AU - Mcguire, Philip

PY - 2021/1/3

Y1 - 2021/1/3

N2 - The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using sam- ples 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 win- dows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the ‘carto- graphic profile’ of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differ- ences between CHR and HC groups. Within the CHR group, using integrated FC net- works, 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 bio- markers of psychosis risk.KEYWORDScartographic profile, clinical high-risk for psychosis, network analysis, network based statistics, network integration, task fMRI© 2020, The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/

AB - The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using sam- ples 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 win- dows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the ‘carto- graphic profile’ of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differ- ences between CHR and HC groups. Within the CHR group, using integrated FC net- works, 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 bio- markers of psychosis risk.KEYWORDScartographic profile, clinical high-risk for psychosis, network analysis, network based statistics, network integration, task fMRI© 2020, The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/

U2 - 10.1002/hbm.25235

DO - 10.1002/hbm.25235

M3 - Article

SN - 1065-9471

JO - HUMAN BRAIN MAPPING

JF - HUMAN BRAIN MAPPING

ER -