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)在人脑中产生的电场分布的重要工具。模拟还允许个性化的刺激方法，控制不同的头部和大脑解剖对个别场分布的影响，它们是研究单位(RU)研究战略的一个组成部分。然而，由于模拟基于关于头部解剖和组织传导性的潜在不确定信息来估计电场，因此验证其准确性是非常重要的。最重要的是，理论分析和选定的接受手术的人类患者的侵入性电极记录表明，场模拟的准确性在不同的人之间可能存在很大差异。在RU的项目10(P10)中，我们将首次将磁共振电流密度成像(MRCDI)应用于一大群受试者，以便系统地和非侵入性地验证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、优化的TES硬件和分析方法方面的全面工作，这些工作有助于成熟MRCDI，并为其在人体上的大规模应用做好准备。我们将从收集RU使用的所有目标区域的40名健康参与者的MRCDI数据开始。我们将在一个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。使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，并洞察哪些电导率受益于MRCDI的优化。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来比较不同解剖复杂性的头部模型。最后，我们将测试解剖特征(颅骨厚度、脑脊液体积)和选定的人口统计学变量(年龄、性别)对个体水平模拟精度的影响。为配合上述工作，我们将简化目前需要专业知识的MRCDI采集程序，以配合上述工作。改进后的程序将在研究组的四个项目中进行试点测试，使MRCDI准备好更广泛地使用。特别是，这项工作将为MRCDI在RU潜在的第二阶段的所有项目中的普遍使用做好准备，在该阶段，老年头骨成分和大脑解剖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.