Individualized closed-loop transcranial alternating current stimulation

这项建议的主要目的是促进经颅交流电刺激(TACS)从基础研究的探索性方法转变为神经科学的标准工具。过去项目期的结果表明，解剖学或生理学上的微小变化可能会导致结果的重大变化。首先，为了针对特定的大脑区域，必须考虑到人头及其多个组织隔间的个体解剖结构。其次，个体头骨的传导性在不同受试者之间差异很大，应该通过比较取决于头骨传导性的脑电反应和没有这种相关性的脑磁图反应来估计。第三，为了实现夹带作用，必须考虑到目标脑振荡的个别频率。因此，我们将开发一种自适应个体参数的闭环刺激系统。这一目标要求开发新的混合有限元方法，用于HD-TCS和使用应用友好的六面体网格的EEG/MEG源组合分析，以便在无需手动调整的情况下自动分割MRI图像，并在避免模型误差的同时允许最高的数值精度。此外，我们将应用高频调幅(AM)TAC，以便通过简单的低通滤波器分离伪影和生理大脑反应，用于在线处理脑电数据。将开发用于在线预处理和在线学习的新算法。新方法将在人工神经网络中进行模拟，然后在人类脑电和脑磁图研究中进行评估。最后，这些新方法将被应用于测试通过闭合环TAC对大脑振荡的调制是否可以反过来调制人类注意力的空间和时间方面。这最终将证明大脑振荡和认知之间存在因果联系。

The major aim of this proposal is to promote transcranial alternating current stimulation (tACS) from an exploratory method of basic research to a standard tool in neuroscience. Results of the past project period have shown that small changes in anatomy or physiology can result in significant changes of outcomes. First of all, the individual anatomy of the human head with its multiple tissue compartments has to be taken into account in order to target a specific brain region. Second, the individual conductivity of the skull varies severely across subjects and shall be estimated by comparing the EEG responses that depend upon skull conductivity with MEG responses that show no such dependency. Third, the individual frequency of the targeted brain oscillations has to be accounted for in order to achieve entrainment. Therefore, we will develop a closed-loop stimulation system which adapts itself to the parameters of individual subjects. This aim requires the development of new mixed finite element methods for hd-tCS and combined EEG/MEG source analysis using application-friendly hexahedral meshes in order to work on automatically segmented MRI images without manual adjustments and to allow highest numerical accuracies while avoiding model errors. In addition, we will apply amplitude-modulated (AM) tACS with a high-frequency carrier frequency in order to separate the artefact and the physiological brain response for on-line processing of EEG data via a simple low-pass filter. New algorithms for on-line pre-processing and on-line learning will be developed. The new methods will be simulated in artificial neural networks before they will be evaluated in human EEG and MEG studies. Finally, the new methods will be applied to test whether modulations of brain oscillations via closed-loop tACS can in turn modulate spatial and temporal aspects of human attention. This would ultimately demonstrate a causal link between brain oscillations and cognition.