Unravelling the Role of Habenula Subnuclei on Avoidance Response: Focus on Activation and Neuroinflammation
Bibliographic record
Abstract
Understanding the mechanisms responsible for anxiety disorders is a major challenge. Avoidance behavior is an essential feature of anxiety disorders. The two-way avoidance test is a preclinical model with two distinct subpopulations-the good and poor performers-based on the number of avoidance responses presented during testing. It is believed that the habenula subnuclei could be important for the elaboration of avoidance response with a distinct pattern of activation and neuroinflammation. The present study aimed to shed light on the habenula subnuclei signature in avoidance behavior, evaluating the pattern of neuronal activation using FOS expression and astrocyte density using GFAP immunoreactivity, and comparing control, good and poor performers. Our results showed that good performers had a decrease in FOS immunoreactivity (IR) in the superior part of the medial division of habenula (MHbS) and an increase in the marginal part of the lateral subdivision of lateral habenula (LHbLMg). Poor performers showed an increase in FOS in the basal part of the lateral subdivision of lateral habenula (LHbLB). Considering the astroglial immunoreactivity, the poor performers showed an increase in GFAP-IR in the inferior portion of the medial complex (MHbl), while the good performers showed a decrease in the oval part of the lateral part of the lateral complex (LHbLO) in comparison with the other groups. Taken together, our data suggest that specific subdivisions of the MHb and LHb have different activation patterns and astroglial immunoreactivity in good and poor performers. This study could contribute to understanding the neurobiological mechanisms responsible for anxiety disorders.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".