Dopamine‐agonists and impulsivity in Parkinson's disease: Impulsive choices vs. impulsive actions
Bibliographic record
Abstract
The control of impulse behavior is a multidimensional concept subdivided into separate subcomponents, which are thought to represent different underlying mechanisms due to either disinhibitory processes or poor decision-making. In patients with Parkinson's disease (PD), dopamine-agonist (DA) therapy has been associated with increased impulsive behavior. However, the relationship among these different components in the disease and the role of DA is not well understood. In this imaging study, we investigated in PD patients the effects of DA medication on patterns of brain activation during tasks testing impulsive choices and actions. Following overnight withdrawal of antiparkinsonian medication, PD patients were studied with a H2 ((15)) O PET before and after administration of DA (1 mg of pramipexole), while they were performing the delay discounting task (DDT) and the GoNoGo Task (GNG). We observed that pramipexole augmented impulsivity during DDT, depending on reward magnitude and activated the medial prefrontal cortex and posterior cingulate cortex and deactivated ventral striatum. In contrast, the effect of pramipexole during the GNG task was not significant on behavioral performance and involved different areas (i.e., lateral prefrontal cortex). A voxel-based correlation analysis revealed a significant negative correlation between the discounting value (k) and the activation of medial prefrontal cortex and posterior cingulate suggesting that more impulsive patients had less activation in those cortical areas. Here we report how these different subcomponents of inhibition/impulsivity are differentially sensitive to DA treatment with pramipexole influencing mainly the neural network underlying impulsive choices but not impulsive action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".