# Automated Phenotyping in Epilepsy

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $422,740

## Abstract

There are 65 million people worldwide with epilepsy and 150,000 new cases of epilepsy are diagnosed in
the US annually. However, treatment options for epilepsy remain inadequate, with many patients suffering from
treatment-resistant seizures, cognitive comorbidities and the negative side effects of treatment. A major
obstacle to progress towards the development of new therapies is the fact that 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). Recently, the Datta lab showed that complex animal behaviors are structured in
stereotyped modules (“syllables”) at sub-second timescales and arranged according to specific rules
(“grammar”). These syllables can be detected without observer bias using a method called motion sequencing
(MoSeq) that employs video imaging with a 3D camera combined with artificial intelligence (AI)-assisted video
analysis to characterize behavior. Through collaboration between the Soltesz and Datta labs, exciting data
were obtained that demonstrated that MoSeq can be adapted for epilepsy research to perform objective,
inexpensive and automated phenotyping of mice in a mouse model of chronic temporal lobe epilepsy. Here we
propose to test and improve MoSeq further to address long-standing, fundamental challenges in epilepsy
research. This includes the development of an objective alternative to the Racine scale, testing of MoSeq as
an automated anti-epileptic drug (AED) screening method, and the development of human observer-
independent behavioral biomarkers for seizures, epileptogenesis, and cognitive comorbidities. In addition, we
plan to dramatically extend the epilepsy-related capabilities of MoSeq to include the automated tracking of
finer-scale body parts (e.g., forelimb and facial clonus) that are not possible with the current approach. Finally,
we propose to develop the analysis pipeline for MoSeq into a form that is intuitive, inexpensive, user-friendly
and thus easily sharable with the research community. 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 both acquired and genetic epilepsy phenotypes.

## Key facts

- **NIH application ID:** 10178133
- **Project number:** 5R01NS114020-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Sandeep R Datta
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $422,740
- **Award type:** 5
- **Project period:** 2019-09-30 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10178133

## Citation

> US National Institutes of Health, RePORTER application 10178133, Automated Phenotyping in Epilepsy (5R01NS114020-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10178133. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*