This 5 year proposal involving 3 universities explores the role of dopamine (DA) in the reinforcement learning working memory (WM), and decision making deficits of patients with schizophrenia (SZ) through behavioral experiments and computational modeling. While DA dysfunction is thought to be a fundamental aspect of SZ, rapid advances in the basic neuroscience understanding and modeling of the role of DA in reward processing remain to be translated into clinical research. The goal of this proposal is to provide an integrative account of how DA abnormalities may underlie the disabling cognitive/motivational deficits of SZ within a comprehensive computational framework that addresses the role of DA signaling in the basal ganglia and frontal cortex in action selection, learning, and WM. The experiments provide tests of model predictions of the types of impairments that result from the DA abnormalities that are thought to occur in SZ.
In Aims 1 -3, we test for deficits in processing of positive feedback signals that would be expected to result from abnormal increases of tonic DA across tasks tapping habit learning, decision making, and WM. We also test the novel, model based prediction that DA abnormalities in the orbital frontal cortex will result in impairment in processing the relative magnitudes of rewards and punishments with experiments designed where this will lead patients to perform at superior or inferior levels relative to controls.
Aim 4 addresses the impact of antipsychotic treatment through the testing of patients both before and after the initiation of treatment where we will test the hypothesis that treatments facilitate learning from negative reinforcement as predicted by the model. These behavioral results will then be explored using computational modeling to determine the fit of observed behavior to predictions based on models where we have explored the role of increasing and decreasing different aspects of DA signaling to approximate the hypothesized abnormalities in SZ. Relevance: Most SZ patients experience deficits in cognitive and motivational processing, resulting in significant disability. It is likely that abnormalities in the DA system may be involved in both types of impairments. Because all known antipsychotic treatments impact the DA system, the proposed work promises to increase understanding of how current treatments may have beneficial as well as possible adverse effects on these crucial areas of impairment.
|Waltz, James A (2017) The neural underpinnings of cognitive flexibility and their disruption in psychotic illness. Neuroscience 345:203-217|
|Maia, Tiago V; Frank, Michael J (2017) An Integrative Perspective on the Role of Dopamine in Schizophrenia. Biol Psychiatry 81:52-66|
|Collins, Anne G E; Albrecht, Matthew A; Waltz, James A et al. (2017) Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia. Biol Psychiatry 82:431-439|
|Collins, Anne G E; Ciullo, Brittany; Frank, Michael J et al. (2017) Working Memory Load Strengthens Reward Prediction Errors. J Neurosci 37:4332-4342|
|Pedersen, Mads Lund; Frank, Michael J; Biele, Guido (2016) The drift diffusion model as the choice rule in reinforcement learning. Psychon Bull Rev :|
|Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D et al. (2016) Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning. J Neurosci 36:1211-22|
|Huys, Quentin J M; Maia, Tiago V; Frank, Michael J (2016) Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci 19:404-13|
|Albrecht, Matthew A; Waltz, James A; Cavanagh, James F et al. (2016) Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients. PLoS One 11:e0152781|
|Strauss, Gregory P; Gold, James M (2016) A Psychometric Comparison of the Clinical Assessment Interview for Negative Symptoms and the Brief Negative Symptom Scale. Schizophr Bull 42:1384-1394|
|Collins, Anne Gabrielle Eva; Frank, Michael Joshua (2016) Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition 152:160-169|
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