BDNF Signaling in the VTA Links the Drug-Dependent State to Drug Withdrawal Aversions
Bibliographic record
Abstract
Drug administration to avoid unpleasant drug withdrawal symptoms has been hypothesized to be a crucial factor that leads to compulsive drug-taking behavior. However, the neural relationship between the aversive motivational state produced by drug withdrawal and the development of the drug-dependent state still remains elusive. It has been observed that chronic exposure to drugs of abuse increases brain-derived neurotrophic factor (BDNF) levels in ventral tegmental area (VTA) neurons. In particular, BDNF expression is dramatically increased during drug withdrawal, which would suggest a direct connection between the aversive state of withdrawal and BDNF-induced neuronal plasticity. Using lentivirus-mediated gene transfer to locally knock down the expression of the BDNF receptor tropomyosin-receptor-kinase type B in rats and mice, we observed that chronic opiate administration activates BDNF-related neuronal plasticity in the VTA that is necessary for both the establishment of an opiate-dependent state and aversive withdrawal motivation. Our findings highlight the importance of a bivalent, plastic mechanism that drives the negative reinforcement underlying addiction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".