Administration of BDNF in the ventral tegmental area produces a switch from a nicotine‐non‐dependent D1R‐mediated motivational state to a nicotine‐dependent‐like D2R‐mediated motivational state
Bibliographic record
Abstract
Brain-derived neurotrophic factor (BDNF) has been implicated in the transition from a non-dependent motivational state to a drug-dependent and drug-withdrawn motivational state. Chronic nicotine can increase BDNF in the rodent brain and is associated with smoking severity in humans; however, it is unknown whether this increased BDNF is linked functionally to the switch from a nicotine-non-dependent to a nicotine-dependent state. We used a place conditioning paradigm to measure the conditioned responses to nicotine, showing that a dose of acute nicotine that non-dependent male mice find aversive is found rewarding in chronic nicotine-treated mice experiencing withdrawal. A single BDNF injection in the ventral tegmental area (in the absence of chronic nicotine treatment) caused mice to behave as if they were nicotine dependent and in withdrawal, switching the neurobiological substrate mediating the conditioned motivational effects from dopamine D1 receptors to D2 receptors. Quantification of gene expression of BDNF and its receptor, tropomyosin-receptor-kinase B (TrkB), revealed an increase in TrkB mRNA but not BDNF mRNA in the VTA in nicotine-dependent and nicotine-withdrawn mice. These results suggest that BDNF signalling in the VTA is a critical neurobiological substrate for the transition to nicotine dependence. The modulation of BDNF signalling may be a promising new pharmacological avenue for the treatment of addictive behaviour.
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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 | 0.001 |
| 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".