Friday, April 26, 2024
4/26/2024
PROJECT SUMMARY Psychotic disorders (PD) affect 3.5% of the population. They are impairing, chronic, and result in reduced disability-adjusted life years and premature death. Advances in assessment and treatment of PD are slowed by the need for identification and mechanistic understanding of biomarkers by which symptoms arise and persist. The mismatch negativity (MMN), an event-related potential elicited by expectancy violations, has been proposed as a biomarker in PD: its reliable reduction is associated with both psychotic and cognitive symptoms. However, mechanisms through which MMN is associated with such symptoms or indexes their course over time are not clear, limiting the clinical utility of this effect. Predictive coding theory (PC) attempts to rectify this by establishing links between neurobiological and clinical phenomena. PC posits a hierarchical organization of brain function whereby sensory input and prior expectations (priors) are integrated to inform perception. When inputs diverge from priors, the mismatch gives rise to prediction error (PE), which contributes to belief updating. Deficits in PE are thought to explain important aspects of psychotic symptoms and cognitive functioning; however, empirical support is sparse, and whether such PE reductions are associated with overly strong or weak reliance on priors is not clear. Computational work suggesting that MMN is a neural representation of sensory PE provides a framework for understanding mechanistic links between MMN and symptoms. Furthermore, oscillatory-based effective connectivity is a critical neural information-routing mechanism through which top-down priors and bottom-up PEs are conveyed, and can be quantified using oscillatory activity underlying MMN. PE has not been derived from MMN in PD, and effective connectivity underlying MMN reduction is not well understood. Importantly, emotion also plays a critical role in the development and maintenance of psychotic symptoms and impairs cognition. Quantifying PE from emotion-MMN (eMMN) allows us to elucidate mechanisms through which emotion exacerbates symptoms, lending explanatory value and utility to research in this area. Finally, though knowledge regarding course of illness is crucial to informing intervention, the utility of MMN and PE in predicting illness trajectories is unknown. Thus, this project aims to elucidate the neurobiological and computational mechanisms through which MMN reduction indexes clinical and cognitive symptoms in PD over time. This study capitalizes on a large (N=220), transdiagnostic cohort with PD and a never-psychotic group (N=252), followed over 3 timepoints. Computational models will be used to derive PE from MMN and eMMN, and effective connectivity to characterize feedforward (PE) and feedback (priors) oscillatory information flow. Overall, this study uses a well-replicated, biologically-based measure and theoretical framework to elucidate computational and neurobiological mechanisms underlying psychotic and cognitive symptoms and provide insight into their trajectories. This project will facilitate training for the applicant in 1) computational modeling, 2) time-frequency analyses and effective connectivity, 3) longitudinal analyses, and 4) activities for professional development.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
