Mapping a behavior atlas throughout the lifespan under diverse internal and external conditions
Behavior comsists of modular components governed by inherent logic, its structural deconstruction is essential for mechanistic insight. Here, we establish a Hierarchical Behavioral Analysis Framework (HBAF) that decodes organizational principles of behavioral modules through high-dimensional data analysis. Using a spontaneous behavior paradigm, HBAF enables rapid, accurate behavioral state assessment and bridges theoretical behavioral models with multidimensional data analytics (Ye et al., 2025, Cell Reports). Through HBAF, we can accurately characterize the behavioral traits of male and female Shank 3b mice across three different genotypes (Liu et al., 2024, Neuroscience Bulletin). This approach revealed that male and female mice employ distinct strategies to evade threats, although their evasion abilities remain stable throughout their lifespan (Liu et al., 2022, BMC Biology).