Designing a transcranial electrical stimulation (TES) strategy requires considering multiple objectives, such as intensity in the target area, focality, stimulation depth, and avoidance zone, which are often mutually exclusive. A computational framework for optimizing different strategies and comparing trade-offs between these objectives is currently lacking. In this paper, we propose a general framework called multi-objective optimization via evolutionary algorithms (MOVEA) to address the non-convex optimization problem in designing TES strategies without 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 performed a comprehensive comparison between tACS and tTIS in terms of intensity, focality, and steerability for targets at different depths.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 in treating diseases. The code for MOVEA is available at https://github.com/ncclabsustech/MOVEA.
翻译:使用进化算法的多目标优化(MOVEA)进行人脑高清颅内电刺激
设计颅内电刺激(TES)策略需要考虑多个目标,例如在目标区域中的强度、聚焦度、刺激深度和回避区域等,这些目标经常是相互矛盾的。目前缺乏一种计算框架来优化不同策略并比较这些目标之间的权衡。本文提出了一种称为多目标优化进化算法(MOVEA)的通用框架,以解决设计TES策略中的非凸优化问题,而无需预定义方向。MOVEA通过Pareto优化,使多个目标同时最优化,在单次运行后生成Pareto前沿,而无需手动权重调整,并且便于扩展到更多目标。该Pareto前沿包含满足各种要求的最优解,同时遵守冲突目标之间的制约关系,例如强度和聚焦度。MOVEA功能灵活,适用于基于高清和双对系统的颅内交替电流刺激(tACS)和颅内时间干涉刺激(tTIS)。我们在不同深度的目标上对tACS和tTIS的强度、聚焦度和可操纵性进行了全面比较。MOVEA促进了基于特定目标和约束的TES优化,推进了tTIS和tACS基于神经调制的认知功能和疾病治疗的研究。MOVEA的代码可在https://github.com/ncclabsustech/MOVEA获得。