Hierarchical behavioral analysis framework as a platform for standardized quantitative identification of behaviors

Behavior is composed of modules that operate based on inherent logic. Understanding behavior and its neural mechanisms is facilitated by clear structural behavioral analysis. Here, we developed a hierarchical behavioral analysis framework (HBAF) that efficiently reveals the organizational logic of these modules by analyzing high-dimensional behavioral data. By creating a spontaneous behavior atlas for male and female mice, we discovered that spontaneous behavior patterns are hardwired, with sniffing serving as the hub node for movement transitions. The sniffing-to-grooming ratio accurately distinguished the spontaneous behavioral states in a high-throughput manner. These states are influenced by emotional status, circadian rhythms, and lighting conditions, leading to unique behavioral characteristics, spatiotemporal features, and dynamic patterns. By implementing the straightforward and achievable spontaneous behavior paradigm, HBAF enables swift and accurate assessment of animal behavioral states and bridges the gap between a theoretical understanding of the behavioral structure and practical analysis using comprehensive multidimensional behavioral information.