Epilepsy is the world’s most common, serious brain disorder, affecting nearly 50 million people worldwide. For one-third of patients, seizures remain poorly controlled despite maximal medical management. In these patients, seizures often arise from a localized brain region, and neurosurgical interventions are the most effective treatment option. Critical to successful intervention is accurate identification of the core tissue responsible for generating seizures (i.e., the epileptogenic zone).
Two main interictal biomarkers to identify the epileptogenic zone have been proposed: (a) interictal discharges or spikes, and (b) high frequency oscillations or ripples. While both signals have been extensively studied, neither performs accurately enough to guide treatment decisions. Spikes are specific for epilepsy, but too spatially diffuse to identify the epileptogenic zone. Ripples are spatially focal but represent both pathologic and physiologic processes. We address these limitations by focusing on the simultaneous occurrence of a spike and ripple, “spike ripple” discharges, as an improved biomarker for the epileptogenic zone. Spike ripples commonly occur in patients with epilepsy, improve the spatial specificity of spikes for the epileptogenic zone, and disentangle physiologic from pathologic ripples.
Our current work is focused on:
(i) developing a fully automated spike-ripple detector and compare its clinical utility to predict surgical outcome to spikes and ripples alone (in collaboration with the Data Analysis Lab at Boston University)
(ii) identifying the biological mechanisms that generate spike-ripple discharges using novel voltage imaging techniques in animal models combined with computational models (in collaboration with the Han Lab at Boston University); and
(iii) develop principled neurostimulation protocols to disrupt the core epileptogenic network that generate spike-ripples.
Findings to date:
We have developed reliable semiautomated and automated approaches to detect spike ripples in scalp EEG recordings.
[Chu et al, 2017]; [Nadalin et al, 2021]
In Rolandic epilepsy, we found that spike ripples predict seizure risk better than spikes alone.
[Thorn et al, 2020]
We have shown that spike ripples predict seizure risk in two surreptitious rodent models of epilepsy.
[Shi et al, 2022]
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