Pathological gambling in patients with Parkinson's disease is associated with fronto‐striatal disconnection: A path modeling analysis
Bibliographic record
Abstract
BACKGROUND: Pathological gambling may occur in Parkinson's disease (PD) as a complication of dopaminergic therapy. Neuroimaging studies have suggested an abnormal dopamine transmission within the reward system, but the changes in the neural network characterizing PD patients with pathological gambling have never been investigated. METHODS: Thirty PD patients (15 with active gambling and 15 matched controls, on-medication) and 15 healthy subjects underwent brain perfusion single photon emission tomography at rest. The severity of gambling was assessed using the South Oaks Gambling Scale. Covariance analysis was applied to identify brain regions whose activity was associated with gambling severity. These regions were used as volume-of-interest to identify functionally interconnected areas using voxel-wise covariance analysis. A path model was defined by means of effective connectivity analysis within the Structural Equation Modeling framework. RESULTS: Gambling severity in PD was associated with a dysfunction of the brain network implicated in decision making, risk processing, and response inhibition, including the ventrolateral prefrontal cortex, anterior (ACC) and posterior cingulate cortex, medial prefrontal cortex, insula and striatum. PD gamblers showed a disconnection between the ACC and the striatum, while this interaction was very robust in both control groups. DISCUSSION: ACC-striatal disconnection may underlie a specific impairment of shifting behaviors after negative outcomes, possibly explaining why PD gamblers use to perseverate into risktaking behaviors despite self-destructive consequences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".