Device-Independent Acquisition and Real Time Spatiotemporal Analysis of Clinical Electrophysiology Data

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Abstract We propose to develop a device-independent, real-time software platform (“MNE-CE”) that will significantly increase the ease and efficiency of acquiring, monitoring, analyzing, and integrating various types of clinical electrophysiolog- ical data (electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), and ste- reotactic EEG (SEEG). Data for epilepsy diagnosis are today obtained and analyzed with a variety of software packages, requiring a significant investment of time in training to use these systems. Data integration is difficult and often qualitative. Our unified approach will not only significantly reduce the cost of training, collecting and analyzing the various types of data, but it anticipates changes in clinical practice by enabling seamless integration of all modal- ities and by enabling new approaches in surgical management. MNE-CE is based on the MNE and MNE-X we have developed during the past 15 years. MNE-X has an architecture that enables the acquisition and analysis of MEG data using any existing MEG systems. MNE-CE will format incoming electrographic data from any recording device for EEG, ECoG, SEEG and MEG, store the data, carry out preprocessing for noise rejections and signal condition- ing, display the incoming data in real time, and carry out the data analysis at the source level (active tissues) instead of the sensor level unlike most of the existing software. Its real-time capability will provide immediate feedback to clinicians, enabling them to use this information for improving surgical management, for example by using the esti- mated locations of epileptogenic tissue to guide the insertion of depth or SEEG electrodes. Accurate identification of the propagation pathway may lead to reduction in volume of resection by specifying the propagation initiation site or the fibers in the pathway to be resected. This project will be carried out synergistically at three institutions led by three PIs who have worked together for many years with complementary expertise. The PIs will work with well- established epileptologists, radiologists and neurosurgeons for coordinating the clinical evaluation. Aim 1: The MGH team will design and develop MNE-CE. The PI at Cleveland is one of the authors of another popular software plat- form (“Brainstorm”). They will work together on this MNE-CE development. Aim 2: The Cleveland team will evalu- ate MNE-CE on SEEG and SEEG/MEG data from adult epilepsy patients. The evaluation will be initially done on the archived data, replaying the data and treating them as incoming data from a virtual EEG/ECoG instrument. The re- sults will be feedback to the MGH development team. As MNE-E matures, it will be used as an add-on to the exist- ing hardware for collecting data during actual clinical measurements, without replacing the existing FDA-approved systems. These results will be used to iteratively improve MNE-CE. Aim 3: The same procedure will be carried out at BCH on EEG, ECoG, SEEG and MEG data in pediatric patients. BCH team will also test whether MNE-CE can reveal abnormal propagation patterns of epileptiform activity in patients with a metabolic disorder.