Linking disruptions in state representation processes to neural signal impairment in nonhuman primates

PROJECT 3 - PROJECT SUMMARY PROJECT 3 will identify and characterize neurophysiological signals at cellular and network scales in nonhuman primates that carry out computations for state representation processes of state estimation, state maintenance, and state learning. We will induce a temporary state of brain dysfunction in macaques that is relevant to psychosis in humans by pharmacologically blocking NMDA receptors and stimulating DA receptor signaling, alone and in combination. We will then trace resulting state representation disruptions back to the distortions of cellular and network activity responsible for the computational errors, with the goal of characterizing the biological foundations of computational disruptions that are relevant to psychosis. We will train monkeys to perform a cognitive control and a reward-based decision-making task that each engage different aspects of state representation. We will then fit computational models to behavioral choice data to define and quantify the computations the brain performs to produce behavioral outputs. We will record neural activity during task performance in order to relate the model-defined computational patterns to patterns of electrical activity in neurons and networks. Neural recordings will focus on central executive (CEN), salience (SN), and default mode (DMN) network dynamics, in keeping with Center findings in people with psychosis. Neural activity will be measured via Neuropixel probes in multiple brain areas located within these networks simultaneously, with each probe capable of recording action potentials from up to hundreds of individually isolated neurons. We will also record neural activity within the dorsal striatum (dSTR) along with cortical areas to test the hypothesis that a 3-factor learning rule operates in corticostriatal networks to govern state learning over trials. Such learning rules, supported by recent theoretical and neurophysiological studies, postulate that synaptic plasticity is controlled by the interaction between neural signals reflecting synchrony (likely to activate NMDA receptors) and neural signals reflecting reinforcement (likely to activate DA receptors). We will test the hypothesis that impairments in state representation processes in psychosis emerge as a consequence of disrupted 3-factor learning, by linking computational impairments to neurophysiological distortions that occur downstream of reduced NMDA and increased DA synaptic function, both implicated as playing a pathogenic role in psychosis. This will reveal how computational disruptions derive from distortions of synaptic and neural function – critically essential knowledge we need, and currently lack, in order to guide a mechanistically-informed search for more effective personalized treatments for psychosis.