Integrating space, time, and individual differences in zebrafish behavioral research: a framework for translational neuroscience.

Zebrafish (Danio rerio) behavioral activity is commonly quantified using aggregate locomotor- and occupancy-based metrics that summarize specific outputs. While such approaches facilitate scalability and standardization, they often compress behavior into summary measures that obscure spatial structure, temporal evolution, and individual organization. In this review, we propose a multidimensional framework in which zebrafish behavior is organized across three interrelated dimensions: spatial organization, temporal dynamics, and inter-individual variability. We synthesize evidence showing that spatial preferences, trajectory structure, homebase-related behaviors, habituation, state transitions, and individual differences constitute complementary components of behavioral organization across space and time. Considering the strength of available evidence, we consider how these dimensions may be related to stress-axis activity, neuromodulatory systems, and pharmacological sensitivity. We also discuss computational and information-theoretic approaches, including entropy-based metrics, behavioral state segmentation, transition-probability modeling, clustering, and Hidden Markov Models, as tools for quantifying behavioral predictability and dynamics. Finally, we highlight the need for transparent and biologically grounded analytical choices that balance interpretability with scalable experimental designs. This framework may support future studies in moving beyond output-based behavioral summaries toward mechanistically informed multidimensional phenotyping, thereby strengthening the utility of zebrafish models in translational neuroscience.