Over 5 million children and adults in the United States have had a diagnosis of epilepsy or a seizure disorder. However, treatment options for the epilepsies remain inadequate, because many patients suffer from uncontrolled seizures and from the negative side effects of treatment. A major obstacle to the faster development of new anti-convulsant therapies is the fact that rigorous preclinical epilepsy research typically requires labor-intensive and expensive 24/7 video-EEG monitoring of seizures that rests on the subjective scoring of seizure phenotypes by human observers (as exemplified by the widely used Racine scale of behavioral seizures). We propose to test if it is possible to perform objective, inexpensive and automated phenotyping of mice in various mouse models of acquired and genetic epilepsies. The approach rests on the recent recognition that mouse behaviors are structured in stereotyped modules at sub-second timescales that are arranged according to specific rules. These characteristic behavioral modules, and the transitions between them, can be identified without observer bias by combined 3D imaging and machine learning (ML) -assisted analytic methods. We propose to adopt this novel ML-assisted 3D video analysis technology to epilepsy research, in order to test if it can be used to identify mice with chronic temporal lobe epilepsy (TLE) during inter-ictal and ictal periods in two distinct experimental TLE models, and under various experimental conditions. In addition, we will also test whether the approach is able to automatically detect not only the overtly epileptic mice in a genetic model of severe childhood epilepsy (homozygous voltage-gated sodium channel ?-subunit SCN1B-/- knock-out mice), but also distinguish the seemingly normal, non-epileptic, SCN1B+/- heterozygous mice from the wild-type controls. We anticipate that these results will have a potentially transformative effect on the field by demonstrating the feasibility and power of automated, objective, user-independent, inexpensive analysis of acquired and genetic epilepsy phenotypes.
There is an urgent need for new therapies for patients with uncontrolled epilepsy. The project will test if it is possible to objectively characterize epileptic phenotypes in mice using a breakthrough technology involving machine learning-assisted analysis of 3-dimensional video data of behavior. If successful, this innovative approach is expected to dramatically accelerate epilepsy research by enabling the objective, automated, inexpensive phenotyping of experimental animals to aid the testing of novel anticonvulsant therapies.
