Electrophysiological Source Imaging of Partial Epilepsy

Project Summary The long-term goal of the research program is to develop and establish a novel electrophysiological source imaging technology to localize and image epileptogenic brain tissues aiding pre-surgical planning in focal (partial) epilepsy. Epilepsy is a common neurological disease impacting more than 3.4 million patients in the US and 70 million globally. The standard clinical routine heavily relies on using intracranial EEG (iEEG) implanted into the brain to determine seizure onset zone, to aid in the localization of epileptogenic zone (EZ), despite the limited coverage of iEEG electrodes and invasive nature of the multiple-day procedure. There is a clinical need to develop a noninvasive neuroimaging approach based on electrophysiological recordings that can reliably image and delineate the EZ from relevant epilepsy biomarkers. In this research project, we propose to establish a novel unsupervised machine learning framework that will be able to process unmarked, continuous, and long-term EEG recordings of focal epilepsy patients to detect high frequency oscillations (HFOs), automatically identify the pathological HFOs (pHFOs, i.e., HFOs riding on spikes), and localize and image epileptogenic brain activity from the identified pHFOs. The proposed techniques will be rigorously validated in over 120 focal epilepsy patients against clinical findings from iEEG recordings and surgical resection outcomes. Our specific aims are: Aim 1. Development and evaluation of a novel unsupervised machine learning technique to identify pathological HFOs from continuous EEG recordings. This aim will establish a novel unsupervised machine learning approach for automatically identifying pHFOs originated from epileptogenic activity, by incorporating data-driven feature learning methods. Aim 2. Development and validation of a novel brain tensor decomposition imaging approach for HFO source imaging. We will develop a novel source imaging framework imaging epileptic sources in temporal, spectral, and spatial domains. This aim will establish a novel data-driven source imaging approach for accurate mapping and localization of EZ from scalp recorded pHFOs. Aim 3: Validation of scalp-detected pHFOs by iEEG-identified pHFOs, with clinical findings of EZ in focal epilepsy patients. We will test the hypothesis that the scalp-EEG identified pHFO events reflect the essential features of iEEG-identified pHFOs, and that both are indicative of the EZ determined from clinical iEEG and confirmed by surgical resection outcome. This aim will establish the relationship between scalp-identified pHFOs with iEEG-identified pHFOs and the underlying epileptogenic networks. The successful completion of the proposed research will establish a novel unsupervised machine learning technology to detect and identify pathological HFOs from continuous and long-term scalp EEG recordings, and localize and image the underlying epileptogenic zone noninvasively and accurately. The establishment of such novel technology promises to significantly improve the clinical management of focal drug- resistant epilepsy, which is currently limited, benefiting numerous patients and the healthcare system.