Repeated Electrical Stimulation of Reward-Related Brain Regions Affects Cocaine But Not “Natural” Reinforcement
Bibliographic record
Abstract
Drug addiction is associated with long-lasting neuronal adaptations including alterations in dopamine and glutamate receptors in the brain reward system. Treatment strategies for cocaine addiction and especially the prevention of craving and relapse are limited, and their effectiveness is still questionable. We hypothesized that repeated stimulation of the brain reward system can induce localized neuronal adaptations that may either potentiate or reduce addictive behaviors. The present study was designed to test how repeated interference with the brain reward system using localized electrical stimulation of the medial forebrain bundle at the lateral hypothalamus (LH) or the prefrontal cortex (PFC) affects cocaine addiction-associated behaviors and some of the neuronal adaptations induced by repeated exposure to cocaine. Repeated high-frequency stimulation in either site influenced cocaine, but not sucrose reward-related behaviors. Stimulation of the LH reduced cue-induced seeking behavior, whereas stimulation of the PFC reduced both cocaine-seeking behavior and the motivation for its consumption. The behavioral findings were accompanied by glutamate receptor subtype alterations in the nucleus accumbens and the ventral tegmental area, both key structures of the reward system. It is therefore suggested that repeated electrical stimulation of the PFC can become a novel strategy for treating addiction.
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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 | 0.000 |
| 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".