Data-driven analysis identifies female-specific social modulation deficit after chronic social defeat stress
Bibliographic record
Abstract
Abstract Background Chronic social defeat stress is a widely used depression model in male mice. Several proposed adaptations extend this model to females with variable, often marginal effects. We examine the if widely used male-defined metrics of stress are suboptimal in females and reveal sex-specific adaptations. Methods Using a data-driven method we comprehensively classified social interaction behavior in 761 male and female mice after chronic social witness/defeat stress, examining social modulation of behavioral frequencies and associations with conventional metrics (i.e., social interaction (SI) ratio). Results Social stress induces distinct behavioral adaptation patterns in males and females. SI ratio leads to underpowered analyses in females with limited utility to differentiate susceptibility/resilience. Data-driven analyses reveal failure of social adaptation in stressed female mice that is captured in attenuated velocity change from no target to target tests (ΔVelocity) and validate this in three female social stress models. Combining SI ratio and ΔVelocity optimally differentiates susceptibility/ resilience in females and this metric reveals resilient-specific adaptation in a resilience-associated neural circuit in female mice. Conclusions We demonstrate that psychological or physical social defeat stress induces similar deficits in females that is qualitatively distinct from male deficits and inadequately sampled by male-defined metrics. We identify modulation of locomotion as a robust and easily implementable metric for rigorous research in female mice. Overall, our findings highlight the need to critically evaluate sex differences in behavior and implement sex-based considerations in preclinical model design.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".