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 language | English |
|---|---|
| Pages (from-to) | 3605-3620 |
| Number of pages | 16 |
| Journal | Brain : a journal of neurology |
| Volume | 142 |
| Issue number | 11 |
| Early online date | 11 Oct 2019 |
| DOIs | |
| Publication status | Published - 1 Nov 2019 |
Funding
| Funders | Funder number |
|---|---|
| European Commission | |
| Seventh Framework Programme | 323413 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- Bayesian hierarchical modelling
- Parkinson’s disease
- dopamine
- functional MRI
- reinforcement learning
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