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

Clinical heterogeneity Diffusion-weighted Imaging Early Psychosis Negative Symptoms Psychopathology White Matter microstructure

Journal

Biological psychiatry
ISSN: 1873-2402
Titre abrégé: Biol Psychiatry
Pays: United States
ID NLM: 0213264

Informations de publication

Date de publication:
20 Jun 2024
Historique:
received: 04 01 2024
revised: 14 05 2024
accepted: 11 06 2024
medline: 23 6 2024
pubmed: 23 6 2024
entrez: 22 6 2024
Statut: aheadofprint

Résumé

Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions. A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS 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. This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

Sections du résumé

BACKGROUND BACKGROUND
Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions.
METHODS METHODS
A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.
RESULTS RESULTS
In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS 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 CONCLUSIONS
This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

Identifiants

DOI: 10.1016/j.biopsych.2024.06.011 PMID: 38908657
pubmed: 38908657
pii: S0006-3223(24)01391-X
doi: 10.1016/j.biopsych.2024.06.011
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Haley R Wang (HR)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States.

Zhen-Qi Liu (ZQ)

Montréal Neurological Institute, McGill University, Montréal, QC, Canada.

Hajer Nakua (H)

Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada.

Catherine E Hegarty (CE)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.

Melanie Blair Thies (MB)

Department of Psychiatry & Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, United States.

Pooja K Patel (PK)

Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States; Desert Pacific Mental Illness Research, Education, and Clinical Center Greater Los Angeles VA Healthcare System, Los Angeles, CA, United States.

Charles H Schleifer (CH)

Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States; David Geffen School of Medicine, University of California, Los Angeles, CA, United States.

Thomas P Boeck (TP)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.

Rachel A McKinney (RA)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.

Danielle Currin (D)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.

Logan Leathem (L)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.

Pamela DeRosse (P)

Department of Psychology, Stony Brook University, Stony Brook, NY, United States.

Carrie E Bearden (CE)

Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States; Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.

Bratislav Misic (B)

Montréal Neurological Institute, McGill University, Montréal, QC, Canada.

Katherine H Karlsgodt (KH)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States. Electronic address: kkarlsgo@ucla.edu.

Classifications MeSH