Charge-Based Brain Modeling Engine with Boundary Element Fast Multipole Method

采用边界元快速多极子法的基于电荷的脑建模引擎

• 批准号: 10735946
• 负责人: Sergey N Makaroff
• 金额: $ 76.88万
• 依托单位: WORCESTER POLYTECHNIC INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-19 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735946
• 关键词: Action Potentials; Adopted; Affect; Algorithms; Anatomy; Animals; Anisotropy; Astacoidea; Axon; Biological; Boundary Elements; Brain; Cell membrane; Cells; Cephalic; Cerebellum; Cerebrum; Charge; Clarkia; Clinical; Clinical Data; Computer Hardware; Computer software; Computers; Data; Deep Brain Stimulation; Deposition; Development; Devices; Electric Stimulation; Electroconvulsive Therapy; Electromagnetics; Elements; Extracellular Space; Fatty acid glycerol esters; Fiber; Fishes; Generations; Geometry; Head; Hour; Implant; Interneurons; Magnetic Resonance Imaging; Magnetism; Magnetoencephalography; Meninges; Methods; Modality; Modeling; Modernization; Muscle; Neurons; Numeric Rating Scale; Patients; Population; Procambarus; Protocols documentation; Resolution; Scalp structure; Skin; Speed; Stream; Surface; Thinness; Time; Tissues; Transcranial magnetic stimulation; Turtles; algorithmic methodologies; bone; brain based; cranium; density; dosage; electrical potential; improved; innovation; meter; microstimulation; millimeter; multi-scale modeling; myelination; neural; novel; open source; predictive modeling; response; simulation; source localization

ABSTRACT All major open-source brain modeling packages currently available (e.g., SimNIBS, DUNEuro, SciRun, ROAST) as well as their commercial counterparts (e.g., Sim4Life, Ansys Maxwell, COMSOL) use the electric potential- based Finite Element Method (FEM) for electromagnetic modeling. FEM has been continuously improved over the past 60 years, is simple to implement and can model averaged tissue anisotropy. At the same time, FEM may have some intrinsic weaknesses specifically affecting high-definition brain modeling. The present proposal aims to develop and disseminate a novel alternative brain modeling engine. In contrast to FEM which uses the electric potential, it operates with the primary (bio)physical quantity – surface (and volumetric) induced electric charge density. To model charge interactions, it naturally employs the modern Fast Multipole Method (FMM) instead of FEM. For piecewise homogeneous biological media of any complexity, only surface charges at bound- aries are present. Their interactions are most accurately described by the boundary element method (BEM). This combination of BEM and FMM is the new proposed BEM-FMM charge engine. The principal advantage of BEM- FMM is its numerically unconstrained spatial field resolution. AIM 1. Improve and complete the BEM-FMM modeling engine. Sub-aims: (i) major speed up of the BEM-FMM engine; (ii) new adaptive mesh refinement algorithm; (iii) new volumetric anisotropic co-solver, (iv) computing activating function with unconstrained numer- ical resolution and; (v) full-scale numerical verification against established FEM solvers SimNIBS and DUNEuro at meso (submillimeter) scale. AIM 2. BEM-FMM testbed for non-invasive recordings and stimulation. 2A. Develop BEM-FMM source localization stream for EEG/MEG recordings. We will construct and validate an improvement over currently existing BEM EEG/MEG source localization software suites using BEM-FMM. We will deliver a ready-to-use testbed with twenty head models and EEG/MEG experimental data. 2B. Develop a BEM-FMM modeling stream with extracerebral compartments for noninvasive stimulation. For enhanced resolution, we will automatically add fine-resolution major extracerebral compartments into existing segmenta- tions pipelines based on anatomical rules. We will then deliver the ready-to-use BEM-FMM testbed targeting TES and ECT (electroconvulsive therapy) where their effect might be critical for the correct dosage prediction and correct targeting. AIM 3. BEM-FMM testbed for invasive electrical stimulation. 3A. Validate BEM-FMM testbed for modeling activating function in animal axons. Verification for a giant inter-neuronal axon of cray- fish Procambarus clarkia via electrical/magnetic stimulation and compound action potential generation for paral- lel fibers in turtle Pseudemys Scripta Elegans cerebellum will be done. 3B. Verify BEM-FMM testbed for mod- eling DBS responses. Using retrospective clinical data, we will develop a BEM-FMM algorithm for patient- specific multipolar DBS and evaluate whether the model predictions align with clinical observations.

摘要 目前可用的所有主要开源大脑建模软件包（例如，SimNIBS、DUNEuro、SciRun、ROAST） 以及它们的商业对应物（例如，Sim 4Life、Ansys麦克斯韦、COMSOL）使用电势- 基于有限元法的电磁建模。FEM不断改进， 在过去的60年里，它易于实现，并且可以模拟平均组织各向异性。同时，FEM 可能有一些内在的弱点，特别是影响高清晰度的大脑建模。现时的建议 旨在开发和推广一种新的替代大脑建模引擎。与使用 电位，它与主要（生物）物理量-表面（和体积）感应电 电荷密度为了模拟电荷相互作用，它自然采用了现代快速多极方法（FMM）。 而不是FEM。对于任何复杂性的分段均匀生物介质，只有在边界处的表面电荷， 白羊座是存在的。它们的相互作用是最准确地描述了边界元法（BEM）。这 BEM和FMM的组合是新提出的BEM-FMM电荷引擎。BEM的主要优点是- FMM是其数值无约束的空间场分辨率。AIM 1.改进和完善BEM-FMM 建模引擎子目标：（一）BEM-FMM引擎的主要加速;（二）新的自适应网格细化 算法;（iii）新的体积各向异性协同求解器，（iv）计算无约束数值的激活函数， 的解决方案和;（五）全面的数值验证对建立有限元求解器SimNIBS和DUNEuro 在中（亚毫米）尺度上。AIM 2. BEM-FMM测试平台，用于无创记录和刺激。2A. 为EEG/MEG记录开发BEM-FMM源定位流。我们将构建并验证一个 使用BEM-FMM改进了当前现有的BEM EEG/MEG源定位软件套件。我们 将提供一个随时可用的测试平台，其中包含20个头部模型和EEG/MEG实验数据。2B.开发一个 BEM-FMM建模流与脑外区室的非侵入性刺激。加强 分辨率，我们将自动添加精细分辨率的主要脑外隔室到现有的段， 基于解剖学规则来选择管道。然后，我们将提供现成的BEM-FMM测试平台， TES和ECT（电休克治疗），其效果可能对正确的剂量预测至关重要 正确的定位。AIM 3. BEM-FMM侵入性电刺激测试台。3A.双BEM-FMM 动物轴突激活功能建模试验台。证实了一个巨大的神经元间轴突的cray- 通过电/磁刺激和复合动作电位产生的鱼类克氏原螯虾的并行- 将在龟Pseudemys Scripta Elegans小脑中的lel纤维进行了研究。3B.验证BEM-FMM测试台是否适用于模块 刺激DBS反应。使用回顾性临床数据，我们将开发一个BEM-FMM算法，用于患者- 特定多极DBS，并评估模型预测是否与临床观察结果一致。

项目成果

Miniaturized Dual Antiphase Patch Antenna Radiating into the Human Body at 2.4 GHz.

小型双反相贴片天线，以 2.4 GHz 频率辐射人体。

• DOI: 10.1109/jerm.2023.3247959
• 发表时间: 2023
• 期刊: IEEE journal of electromagnetics, RF and microwaves in medicine and biology
• 作者: Adams,JohnathanW;Chen,Louis;Serano,Peter;Nazarian,Ara;Ludwig,Reinhold;Makaroff,SergeyN
• 通讯作者: Makaroff,SergeyN