Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated With Psychopathology Across Independent Cohorts

Haley R Wang; Zhen-Qi Liu; Hajer Nakua; Catherine E Hegarty; Melanie Blair Thies; Pooja K Patel; Charles H Schleifer; Thomas P Boeck; Rachel A McKinney; Danielle Currin; Logan Leathem; Pamela DeRosse; Carrie E Bearden; Bratislav Misic; Katherine H Karlsgodt

doi:10.1016/j.biopsych.2024.06.011

Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated With Psychopathology Across Independent Cohorts

Biol Psychiatry. 2024 Jun 21:S0006-3223(24)01391-X. doi: 10.1016/j.biopsych.2024.06.011. Online ahead of print.

Authors

Affiliations

¹ Department of Psychology, University of California, Los Angeles, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California.
² Montréal Neurological Institute, McGill University, Montréal, Québec, Canada.
³ Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.
⁴ Department of Psychology, University of California, Los Angeles, Los Angeles, California.
⁵ Department of Psychiatry & Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, New York.
⁶ Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California; Desert Pacific Mental Illness Research, Education, and Clinical Center Greater Los Angeles VA Healthcare System, Los Angeles, California.
⁷ Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California; David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
⁸ Department of Psychology, Stony Brook University, Stony Brook, New York.
⁹ Department of Psychology, University of California, Los Angeles, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California. Electronic address: kkarlsgo@ucla.edu.

PMID: 38908657
DOI: 10.1016/j.biopsych.2024.06.011

Abstract

Background: Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions.

Methods: A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.

Results: In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use.

Conclusions: This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

Keywords: Clinical heterogeneity; Diffusion-weighted imaging; Early psychosis; Negative symptoms; PLS; Partial least squares; Psychopathology; White matter microstructure.