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<p>The clinical high risk for psychosis (CHR-P) population is important for understanding disease progression and treatment; however, standard approaches to identifying CHR-P individuals are expensive and labor-intensive. Focusing on neurocognitive mechanisms that underlie individual psychosis symptoms (positive, negative, and disorganization) may improve screening and identification. The present study examines whether a behavioral task battery that assays symptom mechanisms can identify CHR-P individuals and predict risk severity. Participants (<i>N</i> = 621) were recruited from clinics and the community as part of the Computerized Assessment of Psychosis Risk (CAPR) consortium study. Structured clinical interviews, a dimensional risk calculator, and behavioral tasks were administered. Clinical interviews identified the following groups: (a) CHR-P (<i>n</i> = 273), (b) non-CHR-P individuals with limited psychosis like experiences (PLEs; <i>n</i> = 120), (c) participants with mental disorders and no PLEs (CLN; <i>n</i> = 82), and (d) healthy controls (HC; <i>n</i> = 146). Multinomial logistic regression indicated that the task battery differentiated groups (<i>p</i> &lt; 0.001), with utility for identifying CHR-P individuals (Sensitivity = 0.87, PPV = 0.51, NPV = 0.77), though with high false positives that varied based on comparison group (Specificity = 0.21–0.43). Tasks also predicted psychosis risk calculator scores (Adjusted <i>R</i><sup><i>2</i></sup> = 0.12), with the two unique predictors being positive symptom task variables associated with updating beliefs regarding environmental volatility. Overall, symptom mechanism tasks differentiated CHR-P individuals from control groups, suggesting their potential as novel screening tools. Using tasks to more efficiently identify CHR-P individuals (e.g., enrich samples), may lower barriers and identify individuals that may otherwise be missed.</p>

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Identifying individuals at clinical high risk for psychosis using a battery of tasks sensitive to symptom mechanisms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月23日

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  • Trevor F. Williams,
  • James M. Gold,
  • James A. Waltz,
  • Jason Schiffman,
  • Lauren M. Ellman,
  • Gregory P. Strauss,
  • Elaine F. Walker,
  • Scott W. Woods,
  • Albert R. Powers,
  • Joshua Kenney,
  • Minerva K. Pappu,
  • Philip R. Corlett,
  • Tanya Tran,
  • Steven M. Silverstein,
  • Richard E. Zinbarg,
  • Vijay A. Mittal

摘要

The clinical high risk for psychosis (CHR-P) population is important for understanding disease progression and treatment; however, standard approaches to identifying CHR-P individuals are expensive and labor-intensive. Focusing on neurocognitive mechanisms that underlie individual psychosis symptoms (positive, negative, and disorganization) may improve screening and identification. The present study examines whether a behavioral task battery that assays symptom mechanisms can identify CHR-P individuals and predict risk severity. Participants (N = 621) were recruited from clinics and the community as part of the Computerized Assessment of Psychosis Risk (CAPR) consortium study. Structured clinical interviews, a dimensional risk calculator, and behavioral tasks were administered. Clinical interviews identified the following groups: (a) CHR-P (n = 273), (b) non-CHR-P individuals with limited psychosis like experiences (PLEs; n = 120), (c) participants with mental disorders and no PLEs (CLN; n = 82), and (d) healthy controls (HC; n = 146). Multinomial logistic regression indicated that the task battery differentiated groups (p < 0.001), with utility for identifying CHR-P individuals (Sensitivity = 0.87, PPV = 0.51, NPV = 0.77), though with high false positives that varied based on comparison group (Specificity = 0.21–0.43). Tasks also predicted psychosis risk calculator scores (Adjusted R2 = 0.12), with the two unique predictors being positive symptom task variables associated with updating beliefs regarding environmental volatility. Overall, symptom mechanism tasks differentiated CHR-P individuals from control groups, suggesting their potential as novel screening tools. Using tasks to more efficiently identify CHR-P individuals (e.g., enrich samples), may lower barriers and identify individuals that may otherwise be missed.