Dopaminergic Therapy Increases Go Timeouts in the Go/No-Go Task in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is characterized by resting tremor, rigidity, and bradykinesia. Dopaminergic medications such as L-dopa treat these motor symptoms, but can have complex effects on cognition. Impulse control is an essential cognitive function. Impulsivity is multifaceted in nature. Motor impulsivity involves the inability to withhold pre-potent, automatic, erroneous responses. In contrast, cognitive impulsivity refers to improper risk-reward assessment guiding behavior. Informed by our previous research, we anticipated that dopaminergic therapy would decrease motor impulsivity though it is well known to enhance cognitive impulsivity. We employed the Go/No-go paradigm to assess motor impulsivity in PD. Patients with PD were tested using a Go/No-go task on and off their normal dopaminergic medication. Participants completed cognitive, mood, and physiological measures. PD patients on medication had a significantly higher proportion of Go trial Timeouts (i.e., trials in which Go responses were not completed prior to a deadline of 750ms) compared to off medication (p = 0.01). No significant ON-OFF differences were found for Go trial or No-go trial response times, or for number of No-go errors. We interpret that dopaminergic therapy induces a more conservative response set, reflected in Go trial Timeouts in PD patients. In this way, dopaminergic therapy decreased motor impulsivity in PD patients. This is in contrast to the widely recognized effects of dopaminergic therapy on cognitive impulsivity leading in some patients to impulse control disorders. Understanding the nuanced effects of dopaminergic treatment in PD on cognitive functions such as impulse control will clarify therapeutic decisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".