Flexible time-series analysis: A dynamically aware method for inferring directed dependencies in behavioral data

Animal models remain essential in translational research, particularly in neuroscience, where behavioral assessments are used to investigate brain function and disease mechanisms. However, conventional statistical approaches, focused on frequencies, durations, and correlations, provide limited insight into the temporal organization and dependency structure of behavior. These methods treat behaviors as isolated or aggregated events, often overlooking the sequential, hierarchical, and dynamic nature of behavioral expression. This perspective advocates the adoption of analytical frameworks that integrate pattern-oriented and time-series dependency inference approaches. Tools such as THEME enable the detection of non-random, temporally structured behavioral sequences (T-patterns), revealing how behaviors are hierarchically organized over time. To further incorporate the possibility of assessing directed dependencies in behavioral sequences, tools such as Tigramite can be useful. Under specific assumptions, this tool allows the estimation of time-lagged, confounder-controlled, conditionally independent relationships between behaviors, representing them as dynamic interactions established over time. By combining these approaches, researchers can move beyond descriptive analyses toward generating hypotheses about the temporal organization and interdependence of behavioral events. Animal models of conditions such as drug addiction, autism spectrum disorder, anxiety disorders, and Alzheimer's disease, among others, in which traditional measures incompletely capture the richness of behavioral interactions, could benefit from this approach. Time-series inference methods may help identify candidate behavioral predictors and generate testable hypotheses about underlying pathophysiology. Overall, adopting this integrative, time-resolved analytical strategy may enable more comprehensive, reproducible, and biologically meaningful insights from animal models.