Objective. Personalized transcranial electrical stimulation (tES) has gained growing attention due to the substantial inter-individual variability in brain anatomy and physiology. While previous reviews have discussed the physiological mechanisms and clinical applications of tES, there remains a critical gap in up-to-date syntheses focused on the computational modeling frameworks that enable individualized stimulation optimization. Approach. This review presents a comprehensive overview of recent advances in computational techniques supporting personalized tES. We systematically examine developments in forward modeling for simulating individualized electric fields, as well as inverse modeling approaches for optimizing stimulation parameters. We critically evaluate progress in head modeling pipelines, optimization algorithms, and the integration of multimodal brain data. Main results. Recent advances have substantially accelerated the construction of subject-specific head conductor models and expanded the landscape of optimization methods, including multi-objective optimization and brain network-informed optimization. These advances allow for dynamic and individualized stimulation planning, moving beyond empirical trial-and-error approaches.Significance. By integrating the latest developments in computational modeling for personalized tES, this review highlights current challenges, emerging opportunities, and future directions for achieving precision neuromodulation in both research and clinical contexts.

翻译：目的。由于个体间大脑解剖结构与生理特征的显著差异，个性化经颅电刺激（tES）日益受到关注。尽管已有综述讨论了tES的生理机制与临床应用，但聚焦于实现个体化刺激优化的计算建模框架的最新系统性综述仍存在关键空白。方法。本综述全面概述了支持个性化tES的计算技术最新进展。我们系统梳理了用于模拟个体化电场的正向建模方法，以及用于优化刺激参数的反向建模方法的发展。我们批判性地评估了头部建模流程、优化算法以及多模态脑数据整合方面的进展。主要结果。近期进展显著加速了受试者特异性头部导体模型的构建，并拓展了优化方法的范畴，包括多目标优化和基于脑网络信息的优化。这些进展使得动态且个体化的刺激规划成为可能，超越了经验性的试错方法。意义。通过整合个性化tES计算建模的最新发展，本综述强调了当前挑战、新兴机遇以及在研究与临床环境中实现精准神经调控的未来方向。