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

非侵入性脑刺激(NIB)设备越来越多地用于心理健康治疗 有迹象表明。经颅磁刺激(TMS)被FDA批准用于治疗抑郁症、强迫症 强迫症和烟瘾。批准了两种形式的经颅电刺激(TES) 也用于精神治疗：抑郁症、紧张症和颅脑的电休克治疗(ECT) 治疗焦虑和失眠的电疗刺激(CES)。此外，还有其他经皮冠状动脉介入治疗的临床试验正在进行 TMS的范例以及新的适应症。然而，这些干预措施有局限性，包括 对TMS的不同反应，阈值下TES的低效，以及ECT的认知副作用。贡献者 因素是缺乏对刺激所涉及的神经元素和人群的了解，这两种情况下 一般的和个别病人的。这排除了针对特定靶点的刺激剂量的合理选择 或者是为了解释解剖学上的个体差异。例如，TMS强度是个性化的 基于运动皮质的刺激，这与前额叶皮质的典型靶点相关性有限。此外， ECT和其他形式的TES的电流幅度根本不是个体化的，因此解剖学上的差异 导致大脑内不同的刺激强度。这与深度等侵入性方法形成了鲜明对比 脑刺激，其中神经靶点参与的建模是手术计划和 剂量选择。这个项目的目标是开发计算工具来模拟、量化和可视化 TMS和TES对个体患者大脑神经元的直接影响。模拟的效果将包括 TMS或TES电场对神经元的阈值下极化和阈值上激活 (e-field)，它构成了随后大脑电路调制的关键机制链接。目标1是 实现皮质神经元以及皮质和皮质下有髓轴突和部位的高保真模型 他们在单独的头部模型中。神经模型的形态和膜动力学将得到优化。 来表示层和大脑区域特定的神经元，并将用现有的实验数据进行验证。自.以来 计算一大群神经元的响应的计算需求是令人望而却步的，目标2是 开发和验证神经反应的计算效率估计器，以进行模拟 计算资源有限的普通用户可以访问。最后，目标3是进行这些模拟 以及通过整合神经反应而广泛适用于研究人员和临床医生的评估工具 量化到SimNIBS软件包中，用于电场模拟，以创建名为 神经模拟NIBS。这款用户友好的软件将使研究人员和临床医生能够开发出更好的 了解TMS和TES对个体大脑的影响。最终，NeuroSimNIBS可以用于 使刺激参数个体化，合理规划实验和治疗方案，以便更有效和更有效地 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.