EEG Biomarkers Derived from Dynamical Network Models Enable Rapid Paths to Accurate Diagnosis and Effective Treatment of Epilepsy

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SUMMARY Epilepsy is a neurological disorder that is marked by sudden recurrent episodes of abnormal electrical activity in the brain, known as seizures. This disease plagues more than 60 million people globally, with the same burden of disease as breast cancer in women and lung cancer in men. First line of treatment for patients with epilepsy are anti-epileptic drugs (AEDs). If AEDs are not effective in suppressing seizures, then patients may consider alternative treatments including surgical resection of the epileptogenic zone in the brain, electrical brain stimulation, or vagus nerve stimulation. With several treatment options available, one may think that epilepsy is under control. However, this is far from true. Accurately diagnosing epilepsy and then finding an effective treatment can take years to a lifetime, during which patients and families suffer from the stigma of epilepsy, side- effects of ineffective AEDs, extensive and costly hospital stays, poor outcomes of irreversible surgical treatment, and/or less than satisfactory stimulation therapies whose efficacies are physiologically unmeasurable. We propose a program to establish novel EEG biomarkers and computational tools that will enable rapid and accurate diagnosis of epilepsy followed by a rapid path to an effective treatment. Such a program entails major advances in conceptual knowledge of how epileptic cortical networks behave and change during stimulation treatment that will be gleaned from dynamic network modeling (DNM) of EEG. There are many challenges with diagnosing and treating epilepsy that unfolds as one considers the clinical workflow beginning with a patient’s first seizure. First an accurate diagnosis of epilepsy can take months to years, where scalp EEG can be leveraged to confirm diagnosis. However, the gold standard is to look for EEG abnormalities that are indicators of epilepsy (e.g., spikes), which are often not captured or misread. Second, it takes months to years to find effective AED treatment as there is no physiological measure of drug efficacy. For these two pain points, we will leverage a new biomarker that our lab discovered from intracranial EEG called the source-sink metric which is designed to capture pathological network properties that are always present only in epilepsy patients. For 30% of the patient population, no AEDs work, and their alternative treatments include surgical treatment of the epileptogenic zone (EZ) and electrical stimulation therapy. However surgical success rates for drug resistant patients averages 50%, and there is currently no measure of efficacy of neurostimulation treatment, leaving half of treated patients nonresponsive. For these drug resistant patients, we will leverage the source-sink index, derived from DNMs and EEG, to help more accurately localize the EZ to improve surgical success rates, and to track efficacy of stimulation treatment from the FDA approved RNS device. The proposed R35 will address major challenges with novel EEG biomarkers stemming from dynamic network models. Is successful, the program will lead to breakthrough technologies enabling getting to accurate diagnosis and optimal treatment for all epilepsy patients more rapidly (from years to weeks), including the underserved drug-resistant cohort.