ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2024
Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Stress and Sleep Dynamics in Young Adults with Psychotic-Like Experiences Across Daily Life.Schizophrenia bulletin · 2026Article
- Brainwide Structural Markers of Familial and Clinical Risk for Bipolar Disorder in Youth.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- A Window of Opportunity: Unraveling How Puberty Relates to Psychotic-Like Experiences During Adolescence.Biological psychiatry global open science · 2026Article
- Trajectories of psychotic-like experiences in youth and associations with lifestyle factors.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Mapping Multimodal Risk Factors to Mental Health Outcomes.Nature. Mental health · 2025Article
Corrections and comments
- Erratum issuedErratum.2024
Authors and funding
6 authors.
Funding
Abstract
backgroundPersistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically relevant PLEs early in development is important for improving our understanding of the etiopathogenesis of these experiences. Machine learning analyses were used to examine the most important baseline factors distinguishing persistent distressing PLEs.
methodsUsing Adolescent Brain Cognitive Development (ABCD) Study data on PLEs from 3 time points (ages 9-13 years), we created the following groups: individuals with persistent distressing PLEs (n = 305), individuals with transient distressing PLEs (n = 374), and individuals with low-level PLEs demographically matched to either the persistent distressing PLEs group (n = 305) or the transient distressing PLEs group (n = 374). Random forest classification models were trained to distinguish persistent distressing PLEs from low-level PLEs, transient distressing PLEs from low-level PLEs, and persistent distressing PLEs from transient distressing PLEs. Models were trained using identified baseline predictors as input features (i.e., cognitive, neural [cortical thickness, resting-state functional connectivity], developmental milestone delays, internalizing symptoms, adverse childhood experiences).
resultsThe model distinguishing persistent distressing PLEs from low-level PLEs showed the highest accuracy (test sample accuracy = 69.33%; 95% CI, 61.29%-76.59%). The most important predictors included internalizing symptoms, adverse childhood experiences, and cognitive functioning. Models for distinguishing persistent PLEs from transient distressing PLEs generally performed poorly.
conclusionsModel performance metrics indicated that while most important factors overlapped across models (e.g., internalizing symptoms), adverse childhood experiences were especially important for predicting persistent distressing PLEs. Machine learning analyses proved useful for distinguishing the most clinically relevant group from the least clinically relevant group but showed limited ability to distinguish among clinically relevant groups that differed in PLE persistence.
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