Residual avoidance: a new consistent and repeatable readout of chronic stress-induced conflict anxiety reversible by antidepressant treatment
Bibliographic record
Abstract
Abstract Stress-related illnesses such as major depressive and anxiety disorders are characterized by maladaptive responses to stressful life events. Chronic stress-based animal models have provided critical insight into the understanding of these responses. Currently available assays measuring chronic stress-induced behavioral states in mice are limited in their design (short, not repeatable, sensitive to experimenter-bias) and often inconsistent. Using the Noldus PhenoTyper apparatus, we identified a new readout that repeatedly assesses behavioral changes induced by chronic stress in two mouse models i.e. chronic restraint stress (CRS) and chronic unpredictable mild stress (UCMS). The PhenoTyper test consists of overnight monitoring of animals’ behavior in home-cage setting before, during and after a 1hr light challenge applied over a designated food zone. We tested the reproducibility and reliability of the PhenoTyper test in assessing the effects of chronic stress exposure, and compared outcomes with commonly-used tests. While chronic stress induced heterogeneous profiles in classical tests, CRS- and UCMS-exposed mice showed a very consistent response in the PhenoTyper test. Indeed, CRS and UCMS mice continue avoiding the lit zone in favor of the shelter zone. This “residual avoidance” after the light challenge, lasted for hours beyond termination of the challenge, was not observed after acute stress and was consistently found throughout stress exposure in both models. Chronic stress-induced residual avoidance was alleviated by chronic imipramine treatment but not acute diazepam administration. This behavioral index should be instrumental for studies aiming to better understand the trajectory of chronic stress-induced deficits and potentially screen novel anxiolytics and antidepressants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".