Imaging Epilepsy Sources with Biophysically Constrained Deep Neural Networks

使用生物物理约束的深度神经网络对癫痫源进行成像

• 批准号: 10655833
• 负责人: BIN HE
• 金额: $ 64.4万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655833
• 关键词: Affect; American; Biological Markers; Biophysics; Brain; Brain imaging; Brain region; Chronic; Clinical; Clinical Management; Computer Simulation; Computer software; Data; Development; Drug resistance; Electrodes; Electroencephalography; Electrophysiology (science); Epilepsy; Formulation; Goals; Healthcare Systems; Image; Imaging technology; Implanted Electrodes; Intervention; Intractable Epilepsy; Machine Learning; Magnetoencephalography; Modeling; Monitor; Neural Network Simulation; Non-linear Models; Operative Surgical Procedures; Outcome; Partial Epilepsies; Patients; Performance; Persons; Pharmaceutical Preparations; Procedures; Research; Scalp structure; Seizures; Source; Techniques; Technology; Time; Tissues; Training; biophysical properties; clinical practice; computerized tools; deep neural network; density; epileptiform; imaging approach; imaging capabilities; implantation; improved; infection risk; innovation; neural; neural network; new technology; novel; open source; spatiotemporal; surgery outcome; technological innovation; tool

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

项目概要 该项目的目标是开发和验证一种新型电生理源成像 (ESI) 方法 基于生物物理约束的深度神经网络（BioDNN），显着改善手术计划 耐药性局灶性癫痫患者约占全球 7000 万人的 33%。 在 340 万患有癫痫症的美国人中，仅靠药物治疗无法控制癫痫发作。 只有能够准确定位癫痫发作源，才是治疗耐药性局灶性癫痫的最可行选择 并安全地切除，临床需要创新技术工具以更好地进行病灶手术规划。 我们在这个项目中提出了一种基于生物物理约束的深层神经的新型 ESI 技术。 网络（BioDNN）提供准确、稳健和客观的底层时空估计 癫痫发生区（EZ）的创新之处在于经过训练的神经网络能够对脑源进行成像。 无需操作员为每个新数据实例调整模型的超参数，因此 使该技术在临床环境中客观且易于使用，我们的具体目标是： 目标 1. 建立。 验证 BioDNN 对 EEG 发作间期癫痫样放电 (IED) 中的致癫痫组织进行成像 我们将建立、优化和验证拟议的用于 EZ 成像的 BioDNN。 200 名局灶性耐药性癫痫 (DRE) 患者脑电图中的 IED 与临床“基本事实”目标 2 进行比较。 开发和验证用于对发作间期癫痫样 MEG 致癫痫组织进行成像的 BioDNN 模型 我们将开发和优化用于 EZ 成像的 BioDNN 模型。 MEG IED 并验证 MEG-BioDNN 模型，并与 80 个焦点 DRE 中的 EEG-BioDNN 模型进行比较 目标 3. 开发和验证用于成像的 BioDNN 模型。 来自局灶性癫痫患者发作期脑电图的致癫痫组织我们将开发用于成像的 BioDNN。 来自头皮发作期脑电图的 SOZ 并通过 120 名局灶性 DRE 患者的高密度发作期脑电图记录对其进行验证， 与临床“真实情况”的比较 拟议研究的成功完成将建立一种新颖的方法。 机器学习技术可对发作间期的致癫痫组织进行非侵入性定位和成像 这种新技术的建立有望显着提高治疗效果。 提高颅内脑电图电极植入的精度并辅助手术计划，从而产生显着的效果 改善手术效果，造福众多耐药性癫痫患者。 1