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Group optimization methods for dose planning in tES.

Ricardo Salvador¹, Junhong Zhou², Brad Manor²

¹Neuroelectrics, Barcelona, Spain.

Journal of Neural Engineering

|August 6, 2025

Summary

This summary is machine-generated.

A new group-level optimization framework for transcranial electrical stimulation (tES) offers a scalable alternative to personalized models. This approach improves electric field (E-field) targeting and reduces variability without requiring individual MRI scans.

Keywords:

computational models dose parameter transcranial electrical stimulation

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Area of Science:

Neuroscience
Biomedical Engineering
Computational Modeling

Background:

Optimizing transcranial electrical stimulation (tES) requires precise electric field (E-field) distributions for therapeutic efficacy.
Individualized MRI-based head models are crucial but limit scalability in clinical and research settings.

Purpose of the Study:

To develop and evaluate a novel group-level optimization framework for tES parameters.
To overcome the limitations of personalized MRI-based models by using multiple representative head models.

Main Methods:

Computational modeling utilizing multiple representative head models to minimize group-level error.
Evaluation through leave-one-out cross-validation on data from 54 subjects.
Analysis of effectiveness, generalizability, and predictive utility of anatomical characteristics.

Main Results:

Group-optimized protocols significantly outperformed standard template-based approaches in paired analyses.
Group-based solutions demonstrated systematically better targeting and reduced poor outcomes compared to templates.
Correlations between anatomical features and E-field parameters were identified, enabling further optimization.

Conclusions:

The proposed group optimization framework is a scalable and robust alternative to personalized tES models.
This approach enhances the feasibility and accessibility of model-driven tES protocols.
It eliminates the need for a priori selection of a single representative template.