Cognition and Electrophysiology Clustering in Clinical High Risk for Psychosis Delineates Distinct Dimensions of Heterogeneity: Implications for Multimodal Clustering

Individuals at clinical high risk for psychosis (CHR) are cognitively and neurobiologically heterogeneous, which encourages the use of a clustering approach to parse this heterogeneity. Multimodal approaches are assumed to be superior to unimodal approaches in identifying subgroups. With the success of the use of cognition and electrophysiological measures collectively in established psychotic disorders, and the lack of such an approach in CHR, we were motivated to address this gap. Using the North American Psychosis-Risk Longitudinal Study (NAPLS) 2 consortia (CHR (N=764)), we applied unsupervised cluster analysis on the combined cognitive and electrophysiology measures to identify CHR subgroups and assess their relationship with clinical and functional outcomes. A two-cluster solution with modest separability was found, which prompted the use of an alternative probabilistic, rather than discrete, clustering approach. Individuals who were more likely to be in Cluster 1 exhibited poorer cognitive performance, larger N100, mismatch negativity, and P300 amplitudes, and worse functioning, as well as a younger age of onset. These findings were largely replicated in NAPLS 3 (CHR (N=628)). Taken together, the results of our previous study of cognition-only clustering and the current study of combining cognition and electrophysiology indicate that multimodal clustering, if not developmentally informed, may obscure meaningful subtyping.

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Journal
medRxiv
Published
2026-03-17
DOI
https://doi.org/10.64898/2026.03.14.26347633
Primary Topic
Schizophrenia research and treatment
Type
article
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article

Cognition and Electrophysiology Clustering in Clinical High Risk for Psychosis Delineates Distinct Dimensions of Heterogeneity: Implications for Multimodal Clustering

E. F. Walker, Holly Hamilton, Carrie E. Bearden, Daniel H. Mathalon et al.
medRxiv
Schizophrenia research and treatment
article

Cognition and Electrophysiology Clustering in Clinical High Risk for Psychosis Delineates Distinct Dimensions of Heterogeneity: Implications for Multimodal Clustering

E. F. Walker, Holly Hamilton, Carrie E. Bearden, Daniel H. Mathalon, James B. Green, Walid Yassin, Brian J. Roach, Gregory A. Light, S. W. Woods, Erica Nichole Duncan, Xue-jun Kong, A. Belger, Donna Perkins, Jean M. Addington, William S. Stone, Peter M. Bachman, Michael Cai, Sandra Loo, Ricardo E. Carrion, Spero Nicholas, Matcheri Keshavan, Jason K. Johannesen, Danah Ansari, Kristin S. Cadenhead, Elisabetta C. del Re, Margaret A. Niznikiewicz, Tyrone D. Cannon
article en

Abstract

Individuals at clinical high risk for psychosis (CHR) are cognitively and neurobiologically heterogeneous, which encourages the use of a clustering approach to parse this heterogeneity. Multimodal approaches are assumed to be superior to unimodal approaches in identifying subgroups. With the success of the use of cognition and electrophysiological measures collectively in established psychotic disorders, and the lack of such an approach in CHR, we were motivated to address this gap. Using the North American Psychosis-Risk Longitudinal Study (NAPLS) 2 consortia (CHR (N=764)), we applied unsupervised cluster analysis on the combined cognitive and electrophysiology measures to identify CHR subgroups and assess their relationship with clinical and functional outcomes. A two-cluster solution with modest separability was found, which prompted the use of an alternative probabilistic, rather than discrete, clustering approach. Individuals who were more likely to be in Cluster 1 exhibited poorer cognitive performance, larger N100, mismatch negativity, and P300 amplitudes, and worse functioning, as well as a younger age of onset. These findings were largely replicated in NAPLS 3 (CHR (N=628)). Taken together, the results of our previous study of cognition-only clustering and the current study of combining cognition and electrophysiology indicate that multimodal clustering, if not developmentally informed, may obscure meaningful subtyping.

medRxiv
University of North Carolina at Charlotte (US), Northwestern University (US), University of North Carolina at Chapel Hill (US), Boston Children's Hospital (US), Northwell Health (US), Beth Israel Deaconess Medical Center (US), Feinstein Institute for Medical Research (US), Harvard University (US), Emory University (US), University of Calgary (CA), University of California, San Francisco (US), Yale University (US), University of California San Diego (US), Massachusetts General Hospital (US), Neurobehavioral Systems (US), Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (US), Minneapolis VA Health Care System (US), Atlanta VA Health Care System (US), Northern California Institute for Research and Education (US), Hotchkiss Brain Institute (CA)
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Schizophrenia research and treatment
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