Evidence for GABA‐A receptor dysregulation in gambling disorder: correlation with impulsivity
Bibliographic record
Abstract
Abstract As a behavioural addiction, gambling disorder (GD) provides an opportunity to characterize addictive processes without the potentially confounding effects of chronic excessive drug and alcohol exposure. Impulsivity is an established precursor to such addictive behaviours, and GD is associated with greater impulsivity. There is also evidence of GABAergic dysregulation in substance addiction and in impulsivity. This study therefore investigated GABAA receptor availability in 15 individuals with GD and 19 healthy volunteers (HV) using [11C]Ro15‐4513, a relatively selective α5 benzodiazepine receptor PET tracer and its relationship with impulsivity. We found significantly higher [11C]Ro15‐4513 total distribution volume (VT) in the right hippocampus in the GD group compared with HV. We found higher levels of the ‘Negative Urgency’ construct of impulsivity in GD, and these were positively associated with higher [11C]Ro15‐4513 VT in the amygdala in the GD group; no such significant correlations were evident in the HV group. These results contrast with reduced binding of GABAergic PET ligands described previously in alcohol and opiate addiction and add to growing evidence for distinctions in the neuropharmacology between substance and behavioural addictions. These results provide the first characterization of GABAA receptors in GD with [11C]Ro15‐4513 PET and show greater α5 receptor availability and positive correlations with trait impulsivity. This GABAergic dysregulation is potential target for treatment.
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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 | 0.001 |
| 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) | 0.002 | 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".