Individualized closed-loop transcranial alternating current stimulation

该提案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发展为神经科学的标准工具。过去项目期间的结果表明，解剖学或生理学的微小变化可能导致结局的显著变化。首先，必须考虑具有多个组织隔室的人类头部的个体解剖结构，以便靶向特定的大脑区域。其次，个体颅骨电导率在受试者之间变化很大，应通过比较依赖于颅骨电导率的EEG响应与不显示这种依赖性的MEG响应来估计。第三，为了实现夹带，必须考虑目标脑振荡的个体频率。因此，我们将开发一种闭环刺激系统，它适应于个体受试者的参数。这一目标需要开发新的混合有限元方法，用于hd-tCS和组合EEG/MEG源分析，使用应用友好的六面体网格，以便在自动分割的MRI图像上工作，而无需手动调整，并允许最高的数值精度，同时避免模型误差。此外，我们将应用振幅调制（AM）的tACS与高频载波频率，以分离的伪影和生理脑反应的在线处理的EEG数据通过一个简单的低通滤波器。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EEG和MEG研究中进行评估。最后，新的方法将被应用于测试是否通过闭环tACS的脑振荡的调制可以反过来调制人类注意力的空间和时间方面。这将最终证明大脑振荡和认知之间的因果关系。

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.