Imaging epilepsy sources with biophysically constrained deep neural networks

Active Project Grant Awarded Last action

Award ID
R01NS127849
Recipient
Awarding agency
Obligated
$3,010,924

Award facts

Completion date 85% complete
Place of performance
Pittsburgh, PA 15213, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
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

Transactions

Action Obligated Date
Amendment $593,749
Amendment $601,854
Amendment $585,640
Amendment $585,640
Original award $644,041

Subawards

Subrecipient Amount Awarded
Mayo Clinic Imaging Epilepsy Sources with Biophysically Constrained Deep Neural Networks $122,309
University Of Pittsburgh - Of The Commonwealth System Of Higher Education $93,252
Mayo Clinic $122,309
University Of Pittsburgh - Of The Commonwealth System Of Higher Education The University of Pittsburgh (UP) team will conduct the standard clinical studies on patients with Drug Resistant Epilepsy (DRE), obtain high quality data as… $83,749
Mayo Clinic Conduct the clinical studies and obtain good quality data in deindentified digital format to CMU. Assist in interpretation of the clinical data during the… $118,640
University Of Pittsburgh - Of The Commonwealth System Of Higher Education The University of Pittsburgh (UP) team will conduct the standard clinical studies on patients with Drug Resistant Epilepsy (DRE), obtain high quality data as… $107,314
Mayo Clinic Ambulance The Mayo Clinic team will conduct the clinical studies and obtain good quality data as described below, and will provide the clinical data in deidentified… $122,307

Quick answers

Who received award R01NS127849?

R01NS127849 was awarded to Carnegie-Mellon University by National Institute of Neurological Disorders and Stroke (National Institutes of Health) on June 15, 2023.

How much is award R01NS127849 worth?

$3,010,924 has been obligated on R01NS127849.

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