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

摘要 目前可用的所有主要开源大脑建模包(例如，SimNIBS、DUNEuro、SciRun、Roast) 以及它们的商业同行(例如，Sim4Life、Ansys Maxwell、COMSOL)使用电势- 基于有限元的电磁建模方法。有限元不断得到改进。 过去60年，实现起来很简单，并且可以对平均组织各向异性进行建模。同时，对有限元模型进行了分析 可能有一些固有的弱点，特别是影响高清晰度大脑建模。目前的建议 旨在开发和传播一种新的替代大脑建模引擎。与有限元方法相比，有限元方法使用 电势，它与初级(生物)物理量--表面(和体积)感应电一起工作 电荷密度。为了模拟电荷相互作用，它自然使用了现代快速多极子方法(FMM) 而不是有限元。对于任何复杂程度的分段均匀生物介质，只有表面电荷的边界- 白羊座在座。它们之间的相互作用用边界元方法(BEM)来描述是最准确的。这 BEM和FMM的结合是新提出的BEM-FMM充气发动机。BEM的主要优势是- FMM是它的数值无约束空间场分辨率。目标1.改进和完善BEM-FMM 建模引擎。子目标：(I)BEM-FMM引擎的主要加速；(Ii)新的自适应网格细化 算法；(Iii)新的体积各向异性联合求解器；(Iv)无约束数激活函数的计算。 (V)对已建立的有限元解算器SimNIBS和DUNEuro进行全面数值验证 在中(亚毫米)尺度。目的2.无创记录和刺激的BEM-FMM试验台。2a. 开发用于脑电/脑磁图记录的BEM-FMM源定位流。我们将构建并验证一个 使用BEM-FMM对现有的BEM EEG/MEG源定位软件套件进行改进。我们 将提供一个有20个头部模型和EEG/MEG实验数据的即用型试验台。2B。发展成为 BEM-FMM模拟用于非侵入性刺激的脑外隔室流。对于增强版 分辨率，我们将自动将精细分辨率的主要脑外分区添加到现有节段a- 基于解剖学规则的管道。然后，我们将交付现成的BEM-FMM测试目标 TES和ECT(电休克疗法)，它们的效果可能对正确的剂量预测至关重要 和正确的目标。目的3.BEM-FMM有创电刺激试验台。3A。验证BEM-FMM 动物轴突激活功能模拟试验台。CRAY的一个巨大神经元间轴突的验证- 电/磁刺激和复合动作电位产生并列动作电位的克氏原甲鱼 乌龟小脑中的LEL纤维将被做。3B。验证BEM-FMM测试床的MOD- Eling DBS报道。利用回溯的临床数据，我们将为患者开发一种BEM-FMM算法- 并评估模型预测是否与临床观察相符。

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.