Interictal EEG to guide surgical planning
Our typical approach to planning surgery for patients with drug-resistant epilepsy involves admitting patients to the hospital and waiting days to weeks for multiple spontaneous seizures. This time-consuming process exposes patients to substantial morbidity, and often fails to localize seizure generators. We propose to use the abundant interictal data – between seizures – generated during this process to improve our ability to localize seizure generators and guide surgical planning.
We use multiple approaches to probing the interictal EEG, including:
- Determining the spatiotemporal dynamics of interictal spikes
- Studying brain networks
- Measuring the brain’s response to electrical stimulation
Quantitative EEG to improve the initial diagnosis and treatment of epilepsy
How do brain networks differ between healthy people and people with epilepsy? Answering this question can greatly improve how we care for patients with suspected epilepsy. 8-10% of people will experience at least one lifetime seizure, and even more will have events concerning for seizures. Clinicians diagnose epilepsy based on patients’ descriptions of concerning events, and it is often challenging to determine whether a reported event was a seizure or a mimic. To augment clinical diagnosis, epileptologists manually review EEG data, which often appears normal to the human eye even in patients who have epilepsy. Enhanced methods to diagnose epilepsy via automated EEG review will improve patient care and reduce harm.
We use quantitative analysis of EEG networks to improve how we:
- Diagnose epilepsy
- Classify epilepsy as focal or generalized
- Guide initial treatment
- Predict who will develop hard-to-treat seizures