Dysfunctional State Representations in Psychosis: From Neurophysiology to Neuroplasticity-based Treatment

University Of Minnesota, Minneapolis MN

Investigators

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Abstract

OVERALL - PROJECT SUMMARY The purpose of our P50 Conte Center is to develop, test, and apply computational models of disrupted state representation processes to behavioral and neural measures relevant to psychosis spectrum illness across mouse, macaque, and human experiments. Our premise is that, in order to respond adaptively to the environment, the brain must process information to develop accurate and stable representations of the current state of the world; this requires neural processes that: 1) estimate the current state of the world; 2) use reward-based feedback to learn to recognize new states; and 3) maintain state representation across time gaps. State representation processes rely on precise neural activity including timing synchrony between prefrontal and sensory systems and across prefrontal and subcortical networks, including striatal systems. Our overarching hypothesis is that a range of computationally identifiable microcognitive processes contribute to state representation disruptions in psychosis, that these vary dimensionally across individuals (potentially manifesting as computational subprofiles), and that their underlying neural contributors, once understood, could serve as targets for precision treatments. Informed by our Center’s recent findings from NMDAR-antagonism in macaques, from NMDAR ablation and dopamine (ant)agonism in mice, and from EEG+fMRI studies in humans with and without psychosis, we will examine variable patterns of behavior and neurophysiology during task performance, examining the interplay between state estimation, state maintenance, and state learning computations. Aim 1: Computational science. Informed by biologically-grounded theoretical models and successes in the previous cycle, we will continue to develop methods to measure computational parameters of state representation processes, integrating across two tasks. Each task probes distinct neurocognitive and neural system operations– cognitive control and reward-based decision-making– highly relevant to psychosis. Aim 2: Non-human animal experimental science. Applying these computational methods, and drawing on findings from our human studies, we will analyze the behavior and neurophysiology of state representation processes relevant to psychosis, in mice and in macaques, under normal conditions and after manipulations of NMDAR and dopaminergic systems. Aim 3: Clinical science. Applying these computational methods, and drawing on findings from our non-human animal studies, we will identify the computational dimensions relevant to psychosis and their neural system correlates in humans (via EEG studies and innovative within-subject Precision Functional Mapping) with a focus on interactions among CEN, SN, and DMN network dynamics; we will identify how computational subprofiles in humans can be altered in response to manipulations of NMDAR and dopaminergic systems.