Abstracts

Rationale: Epilepsy is a chronic neurological disorder characterized by seizures and periods of unusual behaviors that affects an estimated 50 million people worldwide. Rodent models of epilepsy are essential for understanding the underlying mechanisms of this disorder and developing novel therapeutics. The gold standard assay to monitor for spontaneous seizures in rodent models of epilepsy is video/ electroencephalography (vEEG). vEEG in animals is low throughput, requiring specialized data acquisition systems, surgical implantation of electrodes, and expert data analysis. For these reasons, the field is often limited in its ability to fully characterize spontaneous seizure dynamics across a growing number of preclinical epilepsy models.

Methods: For this work, we ran a natural history study utilizing home-cage video data from two mouse models of Dravet Syndrome, a severe genetic epileptic encephalopathy. Dravet mice and wildtype littermates were weaned into video-integrated cages, where monitoring occurred from postnatal day 21 to postnatal day 50. To identify spontaneous seizures, machine-learning based algorithms were trained to detect loss of righting reflex in video, a reliable feature of loss of consciousness in mice that occurs consistently during episodes of seizure.



Results: Using this technology, we report spontaneous tonic clonic seizures and SUDEP over the early course disease. Furthermore, we demonstrate how digital biomarkers allow for multiplexing of phenotypic readouts, assessing the interplay between seizure, activity metrics, sleep wake cycle, etc. concurrently in the same cohort of mice.

Conclusions: This data emphasizes the potential wide-ranging impacts of this technology in phenotyping preclinical epilepsy rodent models and potentially increasing the translational relevance of findings.

Funding: Funding from BioMarin Pharmaceuticals Inc. and collaboration/ consortia