Behavior decoding delineates seizure microfeatures and associated sudden death risks in mice
Behavior and motor manifestations are distinctive yet often overlooked features of epileptic seizures. Seizures can result in transient disruptions in motor control, often organized into specific behavioral patterns that can inform seizure types, onset zones, and outcomes. However, refined analysis of behaviors in epilepsy remains challenging in both clinical and preclinical settings. Current manual video inspection approaches are subjective, time-consuming, and often focus on gross and ambiguous descriptions of seizure behaviors, overlooking much of the intricate behavioral dynamics and action kinematics. Here, we utilized two machine learning-aided tools, DeepLabCut (DLC) and Behavior Segmentation of Open Field in DLC (B-SOiD), to decode previously underexplored behavior and action domains of epilepsy. We identified 63 interpretable behavior groups during seizures in a population of 32 genetically diverse mouse strains. Analysis of these behavior groups demonstrates significant differential behavior expression and complexity that can delineate distinct seizure states, unravel intrinsic seizure progression over time, and inform mouse strain backgrounds and genotypes. We also identified seizure behavior patterns and action/subaction kinematics that determine the risks of sudden unexpected death in epilepsy (SUDEP). These findings underscore the significant potential for translation into inpatient settings for video analysis in epilepsy monitoring units and outpatient settings via home surveillance devices and smartphones.