Probing the role of dopamine at the intersection of addiction, decision making, and sex
Bibliographic record
Abstract
Addiction is an escalating, compulsive, and relapsing psychiatric disease that affects millions worldwide and exacts immense socioeconomic cost. As for potential pharmacotherapeutic targets, the dopamine system is of most interest, but it is unclear as to whether increasing or decreasing dopamine is the best approach. There is consensus that the cues associated with cocaine use and gambling (e.g., a crack pipe or flashing casino lights) are critical in driving disordered behaviour. It is also clear that the responsivity to drug and gambling cues is governed by dopamine and that there is considerable comorbidity between cocaine use and gambling disorder. Biological sex also plays a critical role in addiction but is confounded by the socioeconomic construct of gender in human studies, necessitating animal models. In the following thesis, we preclinically modeled the complex intersection of cocaine use and gambling-like behaviour by combining operant cocaine self-administration with the cued rat gambling task. While female and male rats gambled and took cocaine, we used chemogenetics to bidirectionally modulate the dopaminergic neurons projecting from the ventral tegmental area. We showed that chemogenetic inhibition of dopamine neurons decreased numerous addiction-like behaviours (e.g., risk-taking, impulsivity) in males, but surprisingly increased risk-taking in females. In both sexes, stimulation of the dopamine system had generally deleterious effects. Both inhibition and stimulation caused females and males to take more cocaine, but paradoxically prevented the increased risk-taking that results from cocaine self-administration. Drawing on the reward deficiency and incentive sensitization theories of addiction, this thesis furthermore proposes a biobehavioural framework through which the present findings may be understood.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".