Near real-time system for high-resolution computationalTMS navigation

用于高分辨率计算 TMS 导航的近实时系统

• 批准号: 10558627
• 负责人: Aapo Nummenmaa
• 金额: $ 76.85万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10558627
• 关键词: 3-Dimensional; Acceleration; Anatomy; Applications Grants; Area; Benchmarking; Boundary Elements; Brain; Clinical; Clinical Research; Computing Methodologies; Data; Development; Dose; Electromagnetics; Electromyography; Functional Magnetic Resonance Imaging; Generations; Geometry; Goals; Head; Hot Spot; Human; Human Volunteers; Individual; Individual Differences; Location; Magnetic Resonance Imaging; Major Depressive Disorder; Manufacturer; Maps; Measures; Memory; Methods; Modeling; Motor; National Institute of Mental Health; Neuronavigation; Neurosciences Research; Optics; Patient Schedules; Patients; Performance; Peripheral; Physiologic pulse; Positioning Attribute; Prefrontal Cortex; Publishing; Real-Time Systems; Research; Resolution; Scalp structure; Skin; Source; Specific qualifier value; Speed; Surface; System; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Transcranial magnetic stimulation; automated segmentation; clinical application; clinical effect; commercial application; cortex mapping; cranium; electric field; gray matter; healthy volunteer; improved; innovation; magnetic field; neurosurgery; novel; operation; physical model; prototype; repetitive transcranial magnetic stimulation; response; translational potential; treatment-resistant depression; volunteer; white matter

Project Summary/Abstract: Transcranial Magnetic Stimulation (TMS) is a widely used technology for non-invasive modulation of human brain activity. TMS induces electric fields (E-fields) in the intracranial tissue by means of time-varying magnetic fields (electromagnetic induction) that results in the possibility of obtaining suprathreshold stimulation intensities safely and with little discomfort to the subjects. Clinical applications of TMS include major depressive disorder (MDD) and treatment resistant depression (TRD) in which repetitive TMS (rTMS) is administered to Dorsolateral Prefrontal Cortex (DLPFC) with well-demonstrated efficacy. Both in clinical and basic neuroscience research applications, it is important that the stimulation is accurately targeted to the desired region(s). The E-field distribution that is induced by the TMS pulse in the intracranial tissue is the key physical quantity that can be used to delineate which areas are stimulated and which are not. This is especially important for non-motor regions such as the DLPFC because a direct peripheral measure (e.g., electromyographic response) cannot be used to guide the stimulation. To date, computationally estimated E- field distributions have been used in “online” commercial neuronavigation systems to guide the TMS coil positioning, but the currently available systems offer only spherically symmetric head models that cannot properly take into account the individual differences in tissue geometries and may result in substantial targeting and dosing errors. On the other hand, the most accurate computational methods for E-field estimation are too slow to enable near real time operation. Therefore, no technique exists that has the computational efficiency to enable neuronavigation applications while at the same time incorporating high level of anatomical detail and numerical accuracy. To remove this efficiency vs. accuracy dilemma that is currently posing a critical barrier for development of more quantitative TMS approaches, we propose to use our recently developed Boundary Element Method (BEM) based computational strategy accelerated by the Fast Multipole Method (FMM) that is suitable for both online and offline application scenarios. Our approach starts with developing an automatic segmentation and surface generation pipeline to obtain accurate representations of the tissue conductivity boundaries using individual MRI data. We will subsequently develop and experimental TMS neuronavigation system that utilizes the fast BEM-FMM method. The purpose of this system is to render the E-field distributions on top of the 3D brain anatomy and to guide the operator to position the TMS coil and associated E-field “hot spot” to the desired location. We will interface the computational engine with a commercial TMS navigator to demonstrate translational potential for clinical research and ultimately to therapeutic/clinical applications. Finally, we will validate the computational neuronavigation accuracy with an anthropomorphic head phantom and evaluate our system in healthy volunteers and in volunteer neurosurgical patients that are scheduled for invasive direct cortical mapping that will be used as a gold standard.

项目摘要/摘要： 经颅磁刺激（TMS）是一种用于人类非侵入性调制的广泛使用的技术 大脑活动。 TMS通过随时间变化的磁性诱导颅内组织中的电场（电子场） 田地（电磁诱导），导致可能获得上验刺激的可能性 安全的强度和对受试者的不适感。 TMS的临床应用包括主要 抑郁症（MDD）和抗治疗抑郁症（TRD），其中重复TMS（RTMS）为 以明确的效率施加到背外侧前额叶皮层（DLPFC）。在临床和 基本的神经科学研究应用，必须将刺激准确针对 所需区域。颅内组织中TMS脉冲诱导的电子场分布是关键 可以用来描绘哪些区域被刺激而不是哪些区域的物理量。尤其是 对于非运动区域（例如DLPFC）来说很重要，因为直接的外围测量值（例如， 肌电图反应）不能用于指导刺激。迄今为止，计算估计的E- 现场分布已在“在线”商业神经巡航系统中用于指导TMS线圈 定位，但是当前可用的系统仅提供球形对称的头模型 正确考虑组织几何形状的个体差异，并可能导致实质性的靶向 和给药错误。另一方面，用于电子场估计的最准确的计算方法也是 慢速启用接近实时操作。因此，没有任何具有计算效率的技术 同时启用神经驱动应用，同时结合高水平的解剖细节和 数值准确性。消除这种效率与准确性困境，目前正构成关键的障碍 开发更定量的TMS方法，我们建议使用我们最近开发的边界 基于元素方法（BEM）的计算策略通过快速多极方法（FMM）加速 适用于在线和离线应用程序方案。我们的方法始于开发自动 分割和表面产生管道以获得组织电导率的准确表示 使用单个MRI数据的边界。随后，我们将开发和实验性TMS神经道态 使用快速BEM-FMM方法的系统。该系统的目的是渲染电子场分布 在3D脑解剖结构之上，并指导操作员将TMS线圈和相关的电子场定位为“热 点”到所需的位置。我们将将计算引擎与商业TMS导航器连接到 证明了临床研究的翻译潜力，并最终用于治疗/临床应用。 最后，我们将使用拟人化的头幻象来验证计算神经元的精度 并评估我们的健康志愿者和计划的志愿神经外科患者的系统 侵入性直接皮质映射将用作黄金标准。