Enhancing Psychosis Risk Prediction through Computational Cognitive Neuroscience

• Type of Material: Article
• Authors: James M. Gold, University of Maryland School of Medicine; Philip R. Corlett, Yale School of Medicine; Gregory P. Strauss, University of Georgia; Jason Schiffman, University of Maryland, Baltimore (UMB); Lauren M. Ellman, Temple University; Elaine Walker, Emory University; Albert R. Powers, Yale School of Medicine; Scott W. Woods, Yale School of Medicine; James A. Waltz, University of Maryland School of Medicine; Steven M. Silverstein, University of Rochester Medical Center; Vijay A. Mittal, Institute for Innovations in Developmental Sciences (DevSci)
• Language: English
• Date: 2020-11-01
• Publisher: Oxford Press
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2020.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1093/schbul/sbaa091
• Title of Journal or Parent Work: Schizophrenia Bulletin
• Volume: 46
• Issue: 6
• Start Page: 1346
• End Page: 1352
• Grant/Funding Information: This work was supported by the following funding sources: National Institute of Mental Health (NIMH) grants R01 MH120090 (Gold), R01 MH112613 (Ellman), R01 MH120091 (Ellman), R01 MH120092 (Strauss), R01 MH116039 (Strauss/Mittal), R21 MH119438 (Strauss), R01 MH112545 (Mittal), R01 MH1120088 (Mittal), U01 MH081988 (Walker), R01 MH120090 (Waltz), R01 MH112612 (Schiffman), and R01 MH120089 (Corlett/Woods).
• Keywords: schizophrenia prodrome; Psychotic Disorders; Risk Assessment; Cognitive Neuroscience; Prognosis; Humans; clinical high risk; conversion; Prodromal Symptoms; Disease Progression; Schizophrenia
• Research Categories: Health Sciences, Medicine and Surgery; Psychology, Clinical; Biology, Neuroscience

Abstract

Research suggests that early identification and intervention with individuals at clinical high risk (CHR) for psychosis may be able to improve the course of illness. The first generation of studies suggested that the identification of CHR through the use of specialized interviews evaluating attenuated psychosis symptoms is a promising strategy for exploring mechanisms associated with illness progression, etiology, and identifying new treatment targets. The next generation of research on psychosis risk must address two major limitations: (1) interview methods have limited specificity, as recent estimates indicate that only 15%-30% of individuals identified as CHR convert to psychosis and (2) the expertise needed to make CHR diagnosis is only accessible in a handful of academic centers. Here, we introduce a new approach to CHR assessment that has the potential to increase accessibility and positive predictive value. Recent advances in clinical and computational cognitive neuroscience have generated new behavioral measures that assay the cognitive mechanisms and neural systems that underlie the positive, negative, and disorganization symptoms that are characteristic of psychotic disorders. We hypothesize that measures tied to symptom generation will lead to enhanced sensitivity and specificity relative to interview methods and the cognitive intermediate phenotype measures that have been studied to date that are typically indicators of trait vulnerability and, therefore, have a high false positive rate for conversion to psychosis. These new behavioral measures have the potential to be implemented on the internet and at minimal expense, thereby increasing accessibility of assessments.