Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience

Abstract

<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>

Schizotypy 17 years on: Psychotic symptoms in midlife.

“…Evidence of such added value can be found in preliminary data suggesting the presence of perceptual aberrations (not to be confused with attenuated positive symptoms) is predictive of schizophrenia symptoms in ultra-high-risk research subjects (e.g., An & Bang, 2017). Furthermore, creative efforts to refine prediction of psychosis risk using computational approaches applied to neurocognitive processes (Gold et al, 2020) might consider incorporating perceptual aberrations within their framework. Finally, as noted previously, there are multiple indicators of schizotypy (Kwapil et al, 2018), and the present findings serve to complement the body of research that focuses on other indicators.…”

Translating RDoC to real-world impact in developmental psychopathology: A neurodevelopmental framework for application of mental health risk calculators

“…The priority has been to focus on the group of individuals who meet criteria for CHR syndrome. In doing so, they may leverage behavioral paradigms that distinguish CHR pathology from other forms of psychopathology that may manifest similarly yet require different treatment (Gold et al, 2020). For instance, tasks that elicit hedonic responses may differentiate schizophrenia from mood disorders.…”

Increased face detection responses on the mooney faces test in people at clinical high risk for psychosis

“…One potential way to improve clinical utility is to incorporate performance-based measures of specific neurocomputational processes that are linked to one or more aspects of schizophrenia. However, when CHR initiatives have incorporated performance-based assessments, they have often been traitlinked measures that are unlikely to be sensitive to, or predictive of, changes in mental status 4 . The incorporation of state-sensitive performance-based measures with established links to the pathophysiological mechanisms implicated in symptom genesis may allow for more objective and effective monitoring of changes in mental status that are predictive of an impending psychotic episode or a positive response to treatment 4,5 .…”

“…However, when CHR initiatives have incorporated performance-based assessments, they have often been traitlinked measures that are unlikely to be sensitive to, or predictive of, changes in mental status 4 . The incorporation of state-sensitive performance-based measures with established links to the pathophysiological mechanisms implicated in symptom genesis may allow for more objective and effective monitoring of changes in mental status that are predictive of an impending psychotic episode or a positive response to treatment 4,5 . While this approach to psychosis risk assessment is just emerging, there are already multiple studies of schizophrenia documenting that relatively brief and non-invasive behavioral and electrophysiological measures can tap into mechanisms thought to be involved in positive [6][7][8] , negative [9][10][11][12] , disorganized [13][14][15] or motor 16 symptoms, and that these measures are sensitive to changes in the severity level of the symptom cluster with which they are correlated 14,17,18 .…”