Research suggests that if we can identify individuals at-risk for these disorders early, we may be able to improve the course of illness and hopefully prevent illness onset all together. A first generation of studies suggest that the approach of identifying those at clinical high-risk (CHR), through the use of specialized interviews with help-seeking individuals (with attenuated psychosis symptoms) is a promising strategy for exploring mechanisms associated with illness progression, understanding etiology, and identifying new treatment targets. This work has two major limitations: 1) interview methods have limited specificity as only 15-20% of CHR individuals convert to psychosis, and 2) the expertise needed to make CHR diagnosis is only accessible in a handful of metropolitan centers, and requires extensively trained staff. Here, we aim to lay the foundation for a new approach to CHR assessment that will increase accessibility, and positive predictive value. We propose to develop a new psychosis symptom domain sensitive (PSDS) battery, prioritizing tasks that show correlations with the symptoms that define psychosis (actively tapping into psychotic disorder-specific processes, rather than to trait vulnerability signs) and relatedly, that are tied to the neurobiological systems and computational mechanisms implicated in these symptoms. To promote accessibility, we utilize inexpensive behavioral tasks that could be administered over the internet; this will set the stage for later research testing widespread screening in help-seeking as well as non-help seeking populations, that would identify those most in need of in-depth assessment. Before this can be accomplished however, it is necessary to determine which tasks are effective for predicting illness course and how this strategy compares to the first-generation prediction methods. We propose to recruit 500 CHR participants, 500 help-seeking individuals, and 500 healthy controls across 5 sites and in Aim 1, develop a PSDS battery risk calculator based on measures that prove to be most sensitive to imminent conversion. Further, the inclusion of a help-seeking comparison group is critical for translating the PSDS calculator into clinical practice, where the goal is to differentiate those at greatest risk for developing a psychotic disorder from others forms of psychopathology.
In Aim 2, we will compare the sensitivity and specificity of the PSDS risk-calculator to the North American Prodromal Study (NAPLS) risk-calculator (a gold-standard first-generation tool) in the prediction of psychosis conversion over a 2 year- period. Last, in Aim 3, the study will determine if the PSDS predicts functional outcomes over the course of 2 years. Predicting diagnosis is important but being able to provide early intervention to limit the disability characteristic of psychosis is a priority. This project will answer the preliminary questions necessary for a next-generation CHR battery, tied to illness mechanisms and powered by cutting-edge computational methods, that can be used to facilitate the earliest possible detection of psychosis risk.

Public Health Relevance

Early detection of young people at clinical high risk for psychosis offers a critical opportunity for early intervention to improve the course of illness, and perhaps even prevent onset entirely. Current interview- based methods for psychosis risk detection lack specificity, and are only available in a handful of research centers in the United States. The proposed study aims to improve accessibility and broaden impact of high risk screening by testing brief computerized measures, ultimately able to be administered on the internet, and to improve prediction by focusing on tasks specific to underlying mechanisms driving emerging psychotic symptoms.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Morris, Sarah E
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Yale University
Schools of Medicine
New Haven
United States
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