# Tracking pre-seizure dynamics to predict and control seizures

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $424,616

## Abstract

Epileptic seizures are unpredictable events that significantly reduce quality of life.
Predicting when the next seizure would occur could both prepare persons with
epilepsy and their caregivers, and potentially aid in the treatment of seizures.
Animal models of epilepsy provide an opportunity to explore the nature of brain
activity in the period leading up to seizures. Using both mouse and rat models of
generalized absence epilepsy, we have found a specific build up of thalamic
neural spiking activity for several seconds before each seizure. This novel
electrophysiological signature occurs in the absence of any overt epileptiform
EEG activity. We propose to identify the neural circuits that are responsible for
pre-seizure activity using high-density multi-channel silicon probes to record
broadly across seizure-generating networks in the mouse. We will also measure
calcium ion levels, a readout of neural activity, in neuronal cell bodies and their
output axons using fluorescent calcium indicators (GCaMPs) and multiphoton
microscopy to capture a highly complementary component of pre-seizure activity
with high spatial resolution. Neural activity data will be collected together with
EEG, locomotion signals, sensory-evoked responses, and pupil diameter to
create a comprehensive multimodal stream of pre-seizure activity. This
information will be fed into unbiased machine learning approaches to develop
predictive algorithms. We will directly test coupling strength within
thalamocortical pre-seizure networks by conducting network-level and targeted
single-cell recordings in acute brain slices. To determine a specific role of pre-
seizure networks in generating seizures, we will test whether chemogenetic or
optogenetic silencing of key pre-seizure network elements reduces seizure
incidence or severity. Finally, we will test whether we can use seizure-predictive
signals to intervene in real-time and prevent seizures before they take hold.
Together, these experiments will provide proof of concept for a novel therapeutic
approach: targeting the pre-seizure state to improve seizure control.

## Key facts

- **NIH application ID:** 10400963
- **Project number:** 5R01NS117150-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Surya Ganguli
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $424,616
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10400963

## Citation

> US National Institutes of Health, RePORTER application 10400963, Tracking pre-seizure dynamics to predict and control seizures (5R01NS117150-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10400963. Licensed CC0.

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