Wang Mo, Lou Kexin, Liu Zeming, Wei Pengfei, Liu Quanying
Department of Biomedical Engineering, Southern University of Science and Technology, China.
Department of Biomedical Engineering, Southern University of Science and Technology, China; School of Electrical Engineering and Computer Science, University of Queensland, Australia.
Neuroimage. 2023 Oct 15;280:120331. doi: 10.1016/j.neuroimage.2023.120331. Epub 2023 Aug 19.
Designing a transcranial electrical stimulation (tES) strategy requires considering multiple objectives, such as intensity in the target area, focality, stimulation depth, and avoidance zone. These objectives are often mutually exclusive. In this paper, we propose a general framework, called multi-objective optimization via evolutionary algorithm (MOVEA), which solves the non-convex optimization problem in designing tES strategies without a predefined direction. MOVEA enables simultaneous optimization of multiple targets through Pareto optimization, generating a Pareto front after a single run without manual weight adjustment and allowing easy expansion to more targets. This Pareto front consists of optimal solutions that meet various requirements while respecting trade-off relationships between conflicting objectives such as intensity and focality. MOVEA is versatile and suitable for both transcranial alternating current stimulation (tACS) and transcranial temporal interference stimulation (tTIS) based on high definition (HD) and two-pair systems. We comprehensively compared tACS and tTIS in terms of intensity, focality, and steerability for targets at different depths. Our findings reveal that tTIS enhances focality by reducing activated volume outside the target by 60%. HD-tTIS and HD-tDCS can achieve equivalent maximum intensities, surpassing those of two-pair tTIS, such as 0.51 V/m under HD-tACS/HD-tTIS and 0.42 V/m under two-pair tTIS for the motor area as a target. Analysis of variance in eight subjects highlights individual differences in both optimal stimulation policies and outcomes for tACS and tTIS, emphasizing the need for personalized stimulation protocols. These findings provide guidance for designing appropriate stimulation strategies for tACS and tTIS. MOVEA facilitates the optimization of tES based on specific objectives and constraints, advancing tTIS and tACS-based neuromodulation in understanding the causal relationship between brain regions and cognitive functions and treating diseases. The code for MOVEA is available at https://github.com/ncclabsustech/MOVEA.
设计经颅电刺激（tES）策略需要考虑多个目标，例如目标区域的强度、聚焦性、刺激深度和避让区域。这些目标往往相互排斥。在本文中，我们提出了一个通用框架，称为通过进化算法进行多目标优化（MOVEA），它可以解决在设计tES策略时没有预定义方向的非凸优化问题。MOVEA通过帕累托优化实现多个目标的同时优化，单次运行后即可生成帕累托前沿，无需手动调整权重，并且可以轻松扩展到更多目标。这个帕累托前沿由满足各种要求的最优解组成，同时尊重强度和聚焦性等相互冲突目标之间的权衡关系。MOVEA具有通用性，适用于基于高清（HD）和双对系统的经颅交流电刺激（tACS）和经颅颞干扰刺激（tTIS）。我们在强度、聚焦性和不同深度目标的可操控性方面全面比较了tACS和tTIS。我们的研究结果表明，tTIS通过将目标区域外的激活体积减少60%来增强聚焦性。高清tTIS和高清tDCS可以实现等效的最大强度，超过双对tTIS，例如以运动区域为目标时，高清tACS/高清tTIS下为0.51 V/m，双对tTIS下为0.42 V/m。对八名受试者的方差分析突出了tACS和tTIS在最佳刺激策略和结果方面的个体差异，强调了个性化刺激方案的必要性。这些发现为设计适用于tACS和tTIS的刺激策略提供了指导。MOVEA有助于基于特定目标和约束条件对tES进行优化，推动基于tTIS和tACS的神经调节在理解脑区与认知功能之间的因果关系以及治疗疾病方面的发展。MOVEA的代码可在https://github.com/ncclabsustech/MOVEA获取。
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