Real-time visualization and precision targeting in transcranial magnetic stimulation

经颅磁刺激的实时可视化和精确定位

基本信息

批准号：
10195450
负责人：
Lipeng Ning
金额：
$ 27.73万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-01 至 2023-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10195450
关键词：
Adult Affect Algorithms Anatomy Boundary Elements Brain Brain Diseases Brain Mapping Brain region Clinical Clinical Research Complex Computational algorithm Computer software Computing Methodologies Data Data Set Devices Disease Elements Fiber Functional Magnetic Resonance Imaging Geometry Grant Helmet Hour Human Lateral Left Major Depressive Disorder Maps Medical Imaging Mental disorders Methods Modeling Monitor Movement Network-based Neuronavigation Neurons Obsessive-Compulsive Disorder Outcome Patients Physiological Positioning Attribute Prefrontal Cortex Property Resolution Scalp structure Site Speed Structure Surface System Techniques Time Tissues Training Transcranial magnetic stimulation Treatment Efficacy User-Computer Interface Visualization Work automated algorithm base cingulate cortex connectome deep learning deep neural network electric field imaging platform improved neglect network architecture neural network neural network architecture neuroimaging neuroregulation novel simulation simulation software tool treatment planning virtual virtual reality white matter

项目摘要

Project Summary Title: Real-time visualization and precision targeting in transcranial magnetic stimulation Transcranial magnetic stimulation (TMS) is a non-invasive device-based neuromodulation technique for probing neuronal networks and treating mental disorders such as major depressive disorder (MDD) and Obsessive- Compulsive Disorder (OCD). The treatment efficacy of TMS relies on placing TMS coils to accurately stimulate the underlying disease-related brain target. Since TMS-evoked electric field (E-field) is affected by complex tissue structures and brain geometry, it relies on using computational algorithms, such as boundary element modeling (BEM) and finite element modeling (FEM), to estimate the stimulation site. But the relative long computation time of these methods is a limitation for real-time visualization of the stimulation target during brain mapping and for computing the optimal coil position for treatment planning. In this grant, we propose to develop a deep-neural-network based method to accelerate E-field prediction. We will develop a novel deep-neural- network architecture to predict E-field by using training data computed using the FEM algorithm with anisotropic tissue conductivity. Then, we will integrate the trained neural network into a 3DSlicer software module for real- time E-field visualization. Moreover, we will develop a computational algorithm to search for the optimal coil position to maximize the stimulation of a selected brain target within a clinically feasible time. The outcome of this grant will transform deep-learning techniques into a useful tool to enhance the application of TMS in clinical research.

项目概要标题：经颅磁刺激中的实时可视化和精确定位经颅磁刺激 (TMS) 是一种基于非侵入性设备的神经调节技术，用于探测神经元网络和治疗精神障碍，如重度抑郁症（MDD）和强迫症强迫症（OCD）。 TMS的治疗效果依赖于放置TMS线圈的精确刺激潜在的与疾病相关的大脑目标。由于 TMS 诱发电场 (E-field) 受到复杂的影响组织结构和大脑几何形状，它依赖于使用计算算法，例如边界元建模（BEM）和有限元建模（FEM），以估计刺激部位。但相对较长这些方法的计算时间限制了大脑过程中刺激目标的实时可视化映射并计算治疗计划的最佳线圈位置。在这笔赠款中，我们建议开发一种基于深度神经网络的加速电场预测的方法。我们将开发一种新颖的深度神经网络通过使用具有各向异性的 FEM 算法计算的训练数据来预测电场的网络架构组织电导率。然后，我们将训练好的神经网络集成到 3DSlicer 软件模块中以进行实时时间电场可视化。此外，我们将开发一种计算算法来搜索最佳线圈在临床可行的时间内最大限度地刺激选定的大脑目标。结果这笔赠款将把深度学习技术转化为有用的工具，以增强 TMS 在临床中的应用研究。