Validating and optimizing personalized current flow simulations across the human lifespan using in-vivo magnetic resonance current density imaging

计算机模拟已成为表征和优化经颅电刺激（tES）在人脑中引起的电场分布的重要工具。模拟还可以实现个性化的刺激方法，控制不同头部和大脑解剖结构对个体场分布的影响，它们是研究单位（RU）研究策略的一个组成部分。然而，由于模拟基于头部解剖和组织电导率的潜在不确定信息来估计电场，验证其准确性是非常重要的。重要的是，理论分析和有创电极记录在选定的接受手术的人类患者中表明，场模拟的准确性在个体之间可能存在很大差异。这就产生了一种风险，即估计的场和记录的生理反应之间的潜在联系被模糊了。在RU的项目10 （P10）中，我们将首次将磁共振电流密度成像（MRCDI）应用于一大批受试者，以便系统地、无创地验证RU中使用的电场模拟。计划中的工作将利用我们最近在MR采集方案，优化tES硬件和分析方法方面的全面工作，这有助于MRCDI的成熟，并为其在人类中的大规模应用做好准备。我们将首先收集40名健康参与者的MRCDI数据，这些参与者来自RU中使用的所有目标区域。我们将在新的贝叶斯分析框架中使用这些数据来系统地优化个性化头部模型的组织电导率。采用贝叶斯框架将以原则性的方式揭示估计电导率的不确定性，并深入了解哪些电导率从MRCDI优化中受益。作为第二步，我们的目标是将这种方法扩展到使用贝叶斯模型选择不同解剖复杂性的头部模型的比较。最后，我们将测试解剖学特征（颅骨厚度、脑脊液体积）和选定的人口统计学变量（年龄、性别）对个体水平模拟准确性的影响。这些结果将用于开发精度更高的优化水头建模管道，该管道将在RU项目P1-9的事后分析中实施，并提供开源以供广泛使用。作为上述工作的补充，我们将简化目前需要专业知识的MRCDI获取程序。改进后的程序将在RU的四个项目中进行试点测试，使MRCDI为更广泛的使用做好准备。特别是，这项工作将为MRCDI在潜在的RU第二阶段的所有项目中的普遍使用做准备，其中老年颅骨成分和大脑解剖结构的变化可能需要进一步调整模拟，以确保准确的现场估计。

Computer simulations have become an important tool for characterizing and optimizing the electric field distribution induced by transcranial electric stimulation (tES) in the human brain. Simulations also enable personalized stimulation approaches that control for the impact of different head and brain anatomies on the individual field distribution, and they are an integral component of the research strategy of the research unit (RU). However, as simulations estimate the electric fields based on potentially uncertain information about head anatomy and tissue conductivities, validating their accuracy is highly important. Critically, theoretical analyses and invasive electrode recordings in selected human patients undergoing surgery showed that the accuracy of field simulations can vary strongly between individuals. This generates the risk that potential associations between the estimated fields and the recorded physiological responses are obscured.In project 10 (P10) of the RU, we will for the first time apply magnetic resonance current density imaging (MRCDI) to a large group of subjects in order to systematically and non-invasively validate the electric field simulations used in the RU. The planned work will leverage our recent, comprehensive work on MR acquisition schemes, optimized tES hardware and analytical methods, which helped to mature MRCDI and make it ready for the envisioned large-scale application in humans. We will start by collecting MRCDI data of 40 healthy participants for all target regions used in the RU. We will use this data in a new Bayesian analysis framework to systematically optimize the tissue conductivities of the personalized head models. Employing a Bayesian framework will reveal the uncertainty of the estimated conductivities in a principled manner, and give insight into which conductivities benefit from the optimization by MRCDI. As second step, we aim to extend this approach towards the comparison of head models of varying anatomical complexity using Bayesian model selection. Finally, we will test the impact of anatomical features (skull thickness, CSF volume) and selected demographic variables (age, sex) on the simulation accuracy at the individual level. These results will be used for the development of an optimized head modelling pipeline with improved accuracy which will be implemented in the post-hoc analyses in projects P1-9 of the RU and also provided open source for broad use.Complementing the above work, we will streamline the MRCDI acquisition procedures that currently require expert knowledge. The improved procedures will be pilot-tested within four projects of the RU, making MRCDI ready for a broader usage. In particular, this work will prepare MRCDI for its general use across all projects in the potential second phase of the RU, where changes in skull composition and brain anatomy at old age might require further adaptations of the simulations to ensure accurate field estimates.