Tracking pre-seizure dynamics to predict and control seizures

NIH RePORTER · NIH · R01 · $424,537 · view on reporter.nih.gov ↗

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
10122043
Project number
1R01NS117150-01A1
Recipient
STANFORD UNIVERSITY
Principal Investigator
Surya Ganguli
Activity code
R01
Funding institute
NIH
Fiscal year
2020
Award amount
$424,537
Award type
1
Project period
2020-09-30 → 2025-05-31