Aloe Carroll

Abstract Convulsive seizure behaviors are a hallmark feature of epilepsy, but automated detection of these events in freely moving animals is difficult. Here, we employed a high-resolution multi-camera array microscope with high-speed video acquisition and custom supervised machine learning (ML) for automated detection of larval zebrafish between 3- and 7-days post-fertilization (dpf).

Abstract Dravet syndrome (DS) is a severe genetic epilepsy primarily caused by de novo mutations in a voltage-activated sodium channel gene (SCN1A). Patients face life-threatening seizures that are largely resistant to available anti-seizure medications (ASM). Preclinical DS animal models are a valuable tool to identify candidate ASMs for these patients. Among these, scn1lab mutant zebrafish exhibiting spontaneous seizure-like activity are particularly amenable to large-scale drug screening.