Imaging Dopamine Transmission in Cocaine Dependence: Link Between Neurochemistry and Response to Treatment
Bibliographic record
Abstract
OBJECTIVE: Previous research has shown that dopamine signaling in the limbic striatum is crucial for selecting adaptive, motivated behavior and that disrupted dopamine transmission is associated with impulsive and maladaptive behavior. In humans, positron emission tomography (PET) imaging studies have shown that cocaine dependence is associated with the dysregulation of striatal dopamine signaling, which is linked to cocaine-seeking behavior. The goal of the present study was to investigate whether this association applies to the treatment setting. The authors hypothesized that dopamine signaling in the limbic striatum would be associated with response to a behavioral treatment that uses positive reinforcement to replace impulsive cocaine use with constructive personal goals. METHOD: Prior to treatment, cocaine-dependent subjects underwent two PET scans using [(11)C]raclopride, before and after the administration of a stimulant (methylphenidate), for measurement of striatal dopamine D(2/3) receptor binding and presynaptic dopamine release. RESULTS: Both of the outcome measures were lower in the volunteers who did not respond to treatment than in those who experienced a positive treatment response. CONCLUSIONS: These findings provide insight into the neurochemistry of treatment response and show that low dopamine transmission is associated with treatment failure. In addition, these data suggest that the combination of behavioral treatment with methods that increase striatal dopamine signaling might serve as a therapeutic strategy for cocaine dependence.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".