Hierarchical Behavioral Analysis Framework (HBAF) 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 complex behavioral data through dimensionality reduction. By creating a spontaneous behavior atlas for male and female mice, we discovered that spontaneous behavior patterns are hard-wired, with sniffing serving as the central hub for movement transitions. Sniffing-to-grooming ratio accurately distinguished the spontaneous behavioral states in a high-throughput manner. These states are influenced by emotional states, circadian rhythms, and lighting conditions spontaneous behavior, leading to unique behavioral characteristics, spatiotemporal patterns, and dynamic formation. HBAF enables rapid and precise assessment of animal behavioral states based on straightforward spontaneous behaviors, bridging the gap between a theoretical understanding of behavioral structure and practical analysis, and aiding in understanding the neural mechanisms behind behavior.