FROM REINFORCEMENT LEARNING MODELS OF THE BASAL GANGLIA TO THE PATHOPHYSIOLOGY OF PSYCHIATRIC AND NEUROLOGICAL DISORDERS

By Tiago V. Maia, Michael J Frank. Nature Neuroscience, 2011

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Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.

Subfields: Cellular and Molecular Neuroscience, Clinical Psychology, Neurology

View Paper: https://www.semanticscholar.org/paper/cd15aee36d88704e7c923bd123bbc35788a98095

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Citations in the corpus

Computational psychiatry (2011, result): “Along with the pioneering earlier studies [35–40], there have been recent treatments and reports of work along these lines on schizophrenia [3,45,46], addiction [47], Parkinson’s disease, Tourette’s syndrome, and attention-deficit hyperactivity disorder [3].”
Cerebellar networks with the cerebral cortex and basal ganglia. (2013, background): “Disturbances of basal ganglia circuits are associated with a wide range of conditions including not only the motor disorders discussed above, but also disorders with non-motor components such as Tourette syndrome, attention-deficit/hyperactivity disorders and schizophrenia (for a review, see [87]).”
Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. (2014, background): “…Avoid-B performance (AvB) as the probability of choosing M over B. Notably, across a range of empirical studies with this task, manipulations that increase striatal dopamine enhance ChA and impair AvB, and vice versa for manipulations that decrease dopamine; for review, see Maia and Frank (2011).”
Dopamine, Affordance and Active Inference (2012, background): “Dopamine has been implicated in a bewildering variety of processes and pathologies in the human brain; ranging from cortical excitability to attentional deficits [5], [6]; from motor control to akinesia and set switching deficits in Parkinson’s disease [7], [8], [9]; from working memory to schizophr”
Computational psychiatry as a bridge from neuroscience to clinical applications (2016, background): “These parameters, which represent theoretically meaningful constructs, can then be compared across groups, correlated with symptom severity, etc 9 .”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “In recent years, the advances in neuroimaging, genomics and computational modelling have raised great expectations for such a stratified psychiatry (Maia and Frank, 2011; Montague et al., 2012; Stephan et al., 2006).”
A computational model of inhibitory control in frontal cortex and basal ganglia. (2011, background): “ntrol is in uenced by various neurotransmitters including dopamine (DA) and norepinephrine (NE). A wealth of evidence implicates DA with motivated learning and decision making (Salamone et al., 2009; Maia and Frank, 2011) while NE is implicated with saliency and attention (Aston-Jones and Cohen, 200”