The Lateral Habenula to Ventral Tegmental Area Pathway is Required for Aversive Learning and Defensive Behaviors
Bibliographic record
Abstract
Summary The lateral habenula (LHb) provides aversive signals to the ventral tegmental area (VTA), but its contribution to learning and behavior remains poorly understood. Using a retrograde viral strategy, we targeted VTA-projecting LHb neurons and monitored calcium activity during active avoidance training. These neurons were activated by aversive stimuli and predictive cues as animals acquired avoidance responses and showed increased activity at movement onset during the tail suspension test (TST). Silencing LHb→VTA transmission impaired avoidance learning, prolonged escape latency, and reduced the persistence and vigor of active coping in the TST, without affecting baseline locomotion. Anatomical and ex vivo electrophysiology revealed that LHb terminals innervate both dopaminergic (TH⁺) and non-dopaminergic (TH⁻) VTA neurons, exhibiting session-specific synaptic adaptations during avoidance learning. Together, these findings identify the LHb→VTA pathway as a source of aversive predicting signals required for the acquisition of avoidance behavior and the persistence of active coping in aversive context. Highlights LHb→VTA neurons are required for aversive learning and adaptive avoidance These neurons respond to aversive stimuli, predictive cues, and movement onset Silencing the pathway impairs avoidance learning and reduces active coping in aversive contexts LHb→VTA inputs innervate DA and non-DA neurons and undergo learning-related plasticity
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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) | 0.003 | 0.001 |
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".