Personalized transcranial electrical stimulation: a review of computational modeling and optimization

Area of Science:

• Computational neuroscience
• Neuroimaging
• Biomedical engineering

Background:

• Personalized transcranial electrical stimulation (tES) is crucial due to significant individual differences in brain structure and function.
• Existing reviews often overlook the computational modeling frameworks essential for optimizing tES.
• A need exists for current syntheses on computational methods enabling individualized tES.

Purpose of the Study:

• To provide a comprehensive overview of computational modeling advancements for personalized tES.
• To systematically review progress in forward and inverse modeling techniques for tES.
• To critically assess head modeling, optimization algorithms, and multimodal data integration in tES.

Main Methods:

• Systematic review of computational techniques for personalized tES.
• Examination of forward modeling for simulating electric fields in individual brains.
• Analysis of inverse modeling for optimizing tES parameters.

Main Results:

• Significant progress in creating subject-specific head models and advanced optimization algorithms.
• Development of multi-objective and brain network-informed optimization strategies.
• Enabling dynamic, individualized tES planning beyond trial-and-error.

Conclusions:

• Computational modeling is key to advancing personalized tES.
• Integration of computational methods offers opportunities for precision neuromodulation.
• Future directions involve addressing challenges and leveraging emerging computational tools for tES.