Drug-induced deactivation of inhibitory networks predicts pathological gambling in PD
Bibliographic record
Abstract
OBJECTIVE: Some patients with Parkinson disease (PD) develop pathological gambling when treated with dopamine agonists (DAs). However, little is known about DA-induced changes in neuronal networks that may underpin this drug-induced change in behavior in vulnerable individuals. In this case-control study, we aimed to investigate DA-induced changes in brain activity that may differentiate patients with PD with DA-induced pathological gambling (gamblers) from patients with PD without such a history (controls). METHODS: Following overnight withdrawal of antiparkinsonian medication, patients were studied with H₂(15)O PET before and after administration of DA (3 mg apomorphine) to measure changes in regional cerebral blood flow as an index of regional brain activity during a card selection game with probabilistic feedback. RESULTS: We observed that the direction of DA-related activity change in brain areas that are implicated in impulse control and response inhibition (lateral orbitofrontal cortex, rostral cingulate zone, amygdala, external pallidum) distinguished gamblers from controls. DA significantly increased activity in these areas in controls, while gamblers showed a significant DA-induced reduction of activity. CONCLUSIONS: We propose that in vulnerable patients with PD, DAs produce an abnormal neuronal pattern that resembles those found in nonparkinsonian pathological gambling and drug addiction. DA-induced disruption of inhibitory key functions--outcome monitoring (rostral cingulate zone), acquisition and retention of negative action-outcome associations (amygdala and lateral orbitofrontal cortex)--together with restricted access of those areas to executive control (external pallidum)--may well explain loss of impulse control and response inhibition in vulnerable patients with PD, thereby fostering the development of pathological gambling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".