Genetic variation in the dopamine system is associated with mixed‐strategy decision‐making in patients with Parkinson's disease
Bibliographic record
Abstract
Abstract Decision‐making during mixed‐strategy games requires flexibly adapting choice strategies in response to others' actions and dynamically tracking outcomes. Such decisions involve diverse cognitive processes, including reinforcement learning, which are affected by disruptions to the striatal dopamine system. We therefore investigated how genetic variation in dopamine function affected mixed‐strategy decision‐making in Parkinson's disease (PD), which involves striatal dopamine pathology. Sixty‐six PD patients (ages 49–85, Hoehn and Yahr Stages 1–3) and 22 healthy controls (ages 54–75) competed in a mixed‐strategy game where successful performance depended on minimizing choice biases (i.e., flexibly adapting choices trial by trial). Participants also completed a fixed‐strategy task that was matched for sensory input, motor outputs and overall reward rate. Factor analyses were used to disentangle cognitive from motor aspects within both tasks. Using a within‐subject, multi‐centre design, patients were examined on and off dopaminergic therapy, and genetic variation was examined via a multilocus genetic profile score representing the additive effects of three single nucleotide polymorphisms (SNPs) that influence dopamine transmission: rs4680 ( COMT Val 158 Met ), rs6277 ( C957T ) and rs907094 (encoding DARPP‐32 ). PD and control participants displayed comparable mixed‐strategy choice behaviour (overall); however, PD patients with genetic profile scores indicating higher dopamine transmission showed improved performance relative to those with low scores. Exploratory follow‐up tests across individual SNPs revealed better performance in individuals with the C957T polymorphism, reflecting higher striatal D2/D3 receptor density. Importantly, genetic variation modulated cognitive aspects of performance, above and beyond motor function, suggesting that genetic variation in dopamine signalling may underlie individual differences in cognitive function in PD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".