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Group optimization methods for dose planning in tES.

R Salvador¹, J Zhou², B Manor²

¹Neuroelectrics, Barcelona, Spain.

Biorxiv : the Preprint Server for Biology

|April 1, 2025

Summary

This summary is machine-generated.

A new group-level optimization framework for transcranial electrical stimulation (tES) using representative head models improves electric field precision and reduces variability. This approach enhances the scalability and accessibility of model-driven tES protocols in research and clinical settings.

Keywords:

computational models dose parameter transcranial electrical stimulation

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Area of Science:

Neuroscience
Computational Modeling
Medical Physics

Background:

Optimizing transcranial electrical stimulation (tES) requires precise electric field (E-field) distributions, often achieved using individualized magnetic resonance imaging (MRI)-derived head models.
Personalized MRI-based models present scalability limitations in clinical and research applications.
Inter-individual variability in tES outcomes necessitates robust optimization strategies.

Purpose of the Study:

To develop and evaluate a novel group-level optimization framework for tES parameters.
To enhance the scalability and accessibility of model-driven tES protocols.
To reduce inter-individual variability in E-field distributions and therapeutic outcomes.

Main Methods:

Utilized computational modeling with multiple representative head models to optimize tES parameters.
Employed a group-level optimization approach to minimize E-field errors across a population.
Validated the framework using leave-one-out cross-validation on data from 54 subjects.

Main Results:

Group optimization significantly outperformed protocols based on standard templates or random individual models.
Demonstrated a notable reduction in outcome variability across participants.
Identified predictive relationships between anatomical features and E-field parameters, enabling strategic model selection.

Conclusions:

The proposed group optimization framework offers a scalable and robust alternative to personalized MRI-based tES modeling.
This approach enhances the feasibility and accessibility of model-driven tES for broader clinical and research use.
Insights into anatomical-E-field correlations can further refine future optimization strategies.