Transcranial temporal interference stimulation (tTIS) has been reported to be effective in stimulating deep brain structures in experimental studies. However, a computational framework for optimizing the tTIS strategy and simulating the impact of tTIS on the brain is still lacking, as previous methods rely on predefined parameters and hardly adapt to additional constraints. Here, we propose a general framework, namely multi-objective optimization via evolutionary algorithm (MOVEA), to solve the nonconvex optimization problem for various stimulation techniques, including tTIS and transcranial alternating current stimulation (tACS). By optimizing the electrode montage in a two-stage structure, MOVEA can be compatible with additional constraints (e.g., the number of electrodes, additional avoidance regions), and MOVEA can accelerate to obtain the Pareto fronts. These Pareto fronts consist of a set of optimal solutions under different requirements, suggesting a trade-off relationship between conflicting objectives, such as intensity and focality. Based on MOVEA, we make comprehensive comparisons between tACS and tTIS in terms of intensity, focality and maneuverability for targets of different depths. Our results show that although the tTIS can only obtain a relatively low maximum achievable electric field strength, for example, the maximum intensity of motor area under tTIS is 0.42V /m, while 0.51V /m under tACS, it helps improve the focality by reducing 60% activated volume outside the target. We further perform ANOVA on the stimulation results of eight subjects with tACS and tTIS. Despite the individual differences in head models, our results suggest that tACS has a greater intensity and tTIS has a higher focality. These findings provide guidance on the choice between tACS and tTIS and indicate a great potential in tTIS-based personalized neuromodulation. Code will be released soon.
翻译:据报告,在实验性研究中,透明时间干扰刺激(tTIS)在刺激深度大脑结构方面是有效的;然而,由于以前的方法依赖于预先确定的参数,很难适应额外的限制,因此仍然缺乏一个优化tTIS战略和模拟tTIS对大脑影响的计算框架;在这里,我们提议了一个总体框架,即通过演进算法(MOVEA)实现多目标优化,以解决各种刺激技术(包括tTIS和跨周期交替电流刺激(tACS)的非电离层优化问题。通过在两阶段结构中优化电极与电极的比对齐,MOVEA可以与额外的限制兼容(例如,电极电极、额外的避险区域),而MOVA可以加速获得Pareto战线。这些Pareto战线是一套不同要求下的最佳解决方案,表明在强度和调频、强度1 和调调调时,我们快速对TACS和TTI的比对电极值进行综合比较。S在强度、焦度和机动性S的比值下,在深度中,显示我们的电极值和可实现水平目标的比值,在深度下,在深度中,我们方的比标值为低的比标值显示的比。