Project Summary The goal of this project is to develop and validate a novel electrophysiological source imaging (ESI) approach based on biophysically constrained deep neural networks (BioDNN), to significantly improve surgical planning in drug resistant focal epilepsy patients. Epilepsy affects about 70 million people worldwide. For approximately 33% of the 3.4 million Americans with epilepsy, seizures are not controlled by medications alone. Epilepsy surgery is the most viable option for curing drug resistant focal epilepsy, only if seizure sources can be accurately localized and safely removed. There is a clinical need to innovate technological tools for better surgical planning of focal epilepsy. We propose in this project a novel ESI technology based on biophysically constrained deep neural network (BioDNN) to provide accurate, robust, and objective spatio-temporal estimates of the underlying epileptogenic zone (EZ). Of innovation is that the trained neural network, is capable of imaging brain sources without the need to tune the model’s hyper-parameters by an operator for every new instance of data, thus making the technique objective and easy-to-use in clinical settings. Our specific aims are: Aim 1. Establishing and Validating the BioDNN for Imaging Epileptogenic Tissue from EEG Inter-ictal Epileptiform Discharges (IEDs) of Focal Epilepsy Patients. We will establish, optimize and validate the proposed BioDNN for imaging EZ from IEDs in EEG in 200 focal drug resistant epilepsy (DRE) patients, in comparison to clinical “ground truth". Aim 2. Developing and Validating the BioDNN Model for Imaging Epileptogenic Tissue from MEG Inter-ictal Epileptiform Discharges of Focal Epilepsy Patients. We will develop and optimize the BioDNN model for imaging EZ from MEG IEDs and validate the MEG-BioDNN model and compare with the EEG-BioDNN model in 80 focal DRE patients in comparison to clinical “ground truth. Aim 3. Developing and Validating the BioDNN Model for Imaging Epileptogenic Tissue from Ictal EEG of Focal Epilepsy Patients. We will develop the BioDNN for imaging the SOZ from scalp ictal EEG and validate it from high density ictal EEG recordings in 120 focal DRE patients, in comparison to clinical “ground truth”. The successful completion of the proposed research will establish a novel machine learning technology to non-invasively localize and image underlying epileptogenic tissue from interictal and ictal electrophysiological biomarkers. The establishment of such a novel technology promises to significantly improve the precision of intracranial EEG electrodes implantation and aid surgical planning, leading to significant improvement in surgical outcomes, and benefiting numerous drug resistant epilepsy patients. 1