Dopaminergic medication reduces striatal sensitivity to negative outcomes in Parkinson's disease

Brónagh McCoy, Sara Jahfari, Gwenda Engels, Tomas Knapen, Jan Theeuwes

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Reduced levels of dopamine in Parkinson's disease contribute to changes in learning, resulting from the loss of midbrain neurons that transmit a dopaminergic teaching signal to the striatum. Dopamine medication used by patients with Parkinson's disease has previously been linked to behavioural changes during learning as well as to adjustments in value-based decision-making after learning. To date, however, little is known about the specific relationship between dopaminergic medication-driven differences during learning and subsequent changes in approach/avoidance tendencies in individual patients. Twenty-four Parkinson's disease patients ON and OFF dopaminergic medication and 24 healthy controls subjects underwent functional MRI while performing a probabilistic reinforcement learning experiment. During learning, dopaminergic medication reduced an overemphasis on negative outcomes. Medication reduced negative (but not positive) outcome learning rates, while concurrent striatal blood oxygen level-dependent responses showed reduced prediction error sensitivity. Medication-induced shifts in negative learning rates were predictive of changes in approach/avoidance choice patterns after learning, and these changes were accompanied by systematic striatal blood oxygen level-dependent response alterations. These findings elucidate the role of dopamine-driven learning differences in Parkinson's disease, and show how these changes during learning impact subsequent value-based decision-making.

Original languageEnglish
Pages (from-to)3605-3620
Number of pages16
JournalBrain : a journal of neurology
Volume142
Issue number11
Early online date11 Oct 2019
DOIs
Publication statusPublished - 1 Nov 2019

Funding

FundersFunder number
Seventh Framework Programme323413

    Keywords

    • Bayesian hierarchical modelling
    • Parkinson’s disease
    • dopamine
    • functional MRI
    • reinforcement learning

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