bioRxivNeurosciencePreprint
Data-driven analysis identifies female-specific social modulation deficit after chronic social defeat stress
Heike SchuleriD, Rand S. EidiD, Serena Shuo WuiD, Yiu Chung Tse, Vedrana CvetkovskaiD, Joëlle LopeziD, Rosalie Quinn, Delong Zhou, Juliet MecciaiD, Laurence Dion‐AlbertiD, Caroline MénardiD, Shannon N. BennettiD, Catherine J. PeñaiD, Rosemary C. BagotiD
Abstract
BackgroundChronic 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. MethodsUsing 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). ResultsSocial 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 ({Delta}Velocity) and validate this in three female social stress models. Combining SI ratio and {Delta}Velocity optimally differentiates susceptibility/ resilience in females and this metric reveals resilient-specific adaptation in a resilience-associated neural circuit in female mice. ConclusionsWe 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.
- organism: mouse
- research focus: clustering
- research focus: differential expression
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 2
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