Dopamine modulates learning-related changes in dynamic striatal-cortical connectivity in Parkinson’s disease
Bibliographic record
Abstract
Abstract Learning from reinforcement is thought to depend on striatal dopamine inputs, which serve to update the value of actions by modifying connections in widespread cortico-striatal circuits. While considerable research has described the activity of individual striatal and midbrain regions in reinforcement learning, the broader role for dopamine in modulating network-level processes has been difficult to decipher. To examine whether dopamine modulates circuit-level dynamic connectivity during learning, we characterized the effects of dopamine on learning-related dynamic functional connectivity estimated from fMRI data acquired in patients with Parkinson’s disease. Patients with Parkinson’s disease have severe dopamine depletion in the striatum and are treated with dopamine replacement drugs, providing an opportunity to compare learning and network dynamics when patients are in a low dopamine state (off drugs) versus a high dopamine state (on drugs). We assessed the relationship between dopamine and dynamic connectivity while patients performed a probabilistic reversal learning task. We found that reversal learning altered dynamic network flexibility in the striatum and that this effect was dependent on dopaminergic state. We also found that dopamine modulated changes in connectivity between the striatum and specific task-relevant visual areas of inferior temporal cortex, providing empirical support for theories stipulating that value is updated through changes in cortico-striatal circuits. These results suggest that dopamine exerts a widespread effect on neural circuitry and network dynamics during reinforcement learning.
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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) | 0.001 | 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".