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A Reinforcement-learning Account of Tourette Syndrome

Published online by Cambridge University Press:  23 March 2020

T. Maia*
Affiliation:
Faculdade de Medicina-Universidade de Lisboa, Instituto de Medicina Molecular, Lisbon, Portugal

Abstract

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Background

Tourette syndrome (TS) has long been thought to involve dopaminergic disturbances, given the effectiveness of antipsychotics in diminishing tics. Molecular-imaging studies have, by and large, confirmed that there are specific alterations in the dopaminergic system in TS. In parallel, multiple lines of evidence have implicated the motor cortico-basal ganglia-thalamo-cortical (CBGTC) loop in TS. Finally, several studies demonstrate that patients with TS exhibit exaggerated habit learning. This talk will present a computational theory of TS that ties together these multiple findings.

Methods

The computational theory builds on computational reinforcement-learning models, and more specifically on a recent model of the role of the direct and indirect basal-ganglia pathways in learning from positive and negative outcomes, respectively.

Results

A model defined by a small set of equations that characterize the role of dopamine in modulating learning and excitability in the direct and indirect pathways explains, in an integrated way: (1) the role of dopamine in the development of tics; (2) the relation between dopaminergic disturbances, involvement of the motor CBGTC loop, and excessive habit learning in TS; (3) the mechanism of action of antipsychotics in TS; and (4) the psychological and neural mechanisms of action of habit-reversal training, the main behavioral therapy for TS.

Conclusions

A simple computational model, thoroughly grounded on computational theory and basic-science findings concerning dopamine and the basal ganglia, provides an integrated, rigorous mathematical explanation for a broad range of empirical findings in TS.

Disclosure of interest

The author has not supplied his declaration of competing interest.

Type
Symposium: From prediction errors to disorders of compulsivity: A computational framework
Copyright
Copyright © European Psychiatric Association 2017
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