Computational Modeling for Personalized Transcranial Electrical Stimulation: Theory, Tools, and Applications
🤖 AI Summary
Inter-individual anatomical and physiological variability in the brain leads to highly heterogeneous responses to transcranial electrical stimulation (tES), hindering its precise clinical and research application. To address the lack of systematic reviews on computational modeling for individualized tES, this study proposes a multimodal neuroimaging–integrated framework for constructing personalized head models and develops a brain-network-guided, multi-objective inverse optimization method for dynamic, subject-specific electric field parameter planning. The approach combines high-fidelity forward electric field simulation, conductive tissue modeling, and multi-source data–driven optimization algorithms. Compared with conventional empirical protocols, the framework significantly improves electric field simulation accuracy and optimization efficiency. It advances tES from population-averaged toward individualized neuromodulation paradigms and establishes a critical computational foundation for closed-loop, adaptive tES systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Developing computational models for personalized transcranial electrical stimulation optimization
Addressing individual variability in brain anatomy through computational frameworks
Integrating multimodal brain data for precision neuromodulation in tES
Innovation
Methods, ideas, or system contributions that make the work stand out.
Computational modeling for personalized tES
Forward and inverse modeling approaches
Multi-objective brain network optimization
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