NeuroSimNIBS: Integrated electric field and neuronal response modeling for transcranial electric and magnetic stimulation

非侵入性脑刺激（NIBS）设备越来越多地用于治疗心理健康 迹象。经颅磁刺激（TMS）是FDA批准用于治疗抑郁症，强迫症， 强迫症和烟瘾两种形式的经颅电刺激（TES）被批准 精神病治疗，以及：电休克治疗（ECT）抑郁症和紧张症和颅 电疗刺激（CES）治疗焦虑和失眠。此外，其他TES的临床试验正在进行中。 范例，以及TMS的新适应症。然而，这些干预措施有其局限性， 对TMS的可变反应，阈下TES的低功效，以及ECT的认知副作用。一个促成 因素是缺乏对刺激所涉及的神经元素和人群的了解，无论是在 一般和个体患者中。这妨碍了刺激剂量的合理选择，以靶向特异性 神经元素或解释解剖学上的个体差异。例如，TMS强度是个性化的， 基于运动皮层刺激，其与前额叶皮层中的典型目标的相关性有限。此外，委员会认为， ECT和其他形式的TES中的电流幅度根本不是个体化的，因此解剖学差异 导致大脑内的不同刺激强度。这与侵入性方法形成对比，例如， 脑刺激，其中神经目标接合的建模是手术计划的既定部分， 剂量选择该项目的目标是开发计算工具来模拟，量化和可视化 TMS和TES对个体患者大脑中神经元的直接影响。模拟的影响将包括 TMS或TES电场对神经元的阈下极化和阈上激活 （E场），其包括与随后的脑回路调制的关键机械链接。目标1： 实现皮质神经元以及皮质和皮质下有髓鞘轴突和位置的高保真模型 他们在个人的头部模型。神经模型将具有优化的形态和膜动力学 以代表层和大脑区域特定的神经元，并将与现有的实验数据进行验证。以来 计算大量神经元的响应的计算需求是禁止的，目标2是 开发和验证神经反应的计算有效估计，以进行模拟 对于具有有限计算资源的普通用户来说是可访问的。最后，目标3是使这些模拟 和评估工具广泛提供给研究人员和临床医生， 量化到SimNIBS软件包中进行电场模拟，以创建一个称为 NeuroSimNIBS。这种用户友好的软件将使研究人员和临床医生开发一个更好的 了解TMS和TES对个体大脑的影响。最终，NeuroSimNIBS可以用于 个性化刺激参数，合理规划实验和治疗， 一致的神经调节

Devices for non-invasive brain stimulation (NIBS) are increasingly used for treatment of mental health indications. Transcranial magnetic stimulation (TMS) is FDA-cleared for the treatment of depression, obsessive compulsive disorder, and smoking addiction. Two forms of transcranial electric stimulation (TES) are approved for psychiatric treatment, as well: electroconvulsive therapy (ECT) for depression and catatonia and cranial electrotherapy stimulation (CES) for anxiety and insomnia. Further, there are ongoing clinical trials of other TES paradigms, as well as new indications for TMS. However, these interventions have limitations including the variable response to TMS, low efficacy of subthreshold TES, and cognitive side-effects of ECT. A contributing factor is the lack of understanding of the neural elements and populations engaged by the stimulation, both in general and within an individual patient. This precludes rational selection of the stimulation dose to target specific neural elements or to account for individual differences in anatomy. For example, TMS intensity is individualized based on motor cortex stimulation, which has limited relevance to typical targets in prefrontal cortex. Moreover, the current amplitude in ECT and other forms of TES is not individualized at all, and anatomical differences thus result in variable stimulation strengths within the brain. This is in contrast to invasive approaches, such as deep brain stimulation, where modeling of neural target engagement is an established part of surgical planning and dose selection. The goal of this project is to develop computational tools to simulate, quantify, and visualize the direct effects of TMS and TES on neurons in the brains of individual patients. The modeled effects will include both subthreshold polarization and suprathreshold activation of neural elements by the TMS or TES electric field (E-field), which comprise the critical mechanistic link to subsequent brain circuit modulation. Aim 1 is to implement high-fidelity models of cortical neurons as well as cortical and subcortical myelinated axons and place them in individual head models. The neural models will have morphologies and membrane dynamics optimized to represent layer- and brain-region-specific neurons, and will be validated with existing experimental data. Since the computational demands to calculate the response of a large population of neurons are prohibitive, Aim 2 is to develop and validate computationally efficient estimators of the neural responses to make the simulations accessible for the average user with limited computational resources. Finally, Aim 3 is to make these simulation and estimation tools widely available to researchers and clinicians by integrating the neural response quantifications into the SimNIBS software package for E-field simulation to create an integrated tool termed NeuroSimNIBS. This user-friendly software will enable researchers and clinicians to develop a better understanding of the effect of TMS and TES on individual brains. Ultimately, NeuroSimNIBS could be used to individualize the stimulation parameters and rationally plan experiments and therapies for more effective and consistent neuromodulation.