Unlocking the Potential of Group Optimization for Transcranial Electrical Stimulation

Author: Ricardo Salvador

Transcranial electrical stimulation (tES), including methods like transcranial direct current stimulation (tDCS), has revolutionized our approach to non-invasively modulating brain activity. Yet, one major challenge remains: how do we optimize stimulation parameters—such as electrode placements and currents—to ensure effective and consistent therapeutic outcomes across diverse individuals?

The Challenge of Personalization in tES

Typically, achieving precision in tES involves using personalized head models derived from each individual's MRI data, but this personalization presents challenges in terms of scalability, especially in large clinical trials or broader therapeutic applications. Not everyone can readily undergo an MRI, and even when they can, technical limitations like image quality issues, timing, budgetary constraints, or certain medical conditions can make personalization unfeasible.

What is Group Optimization and Why Does It Matter?

In our recent study, we've proposed an innovative solution: group-level optimization. Instead of relying solely on individualized MRI models or generic templates, our approach involves using a carefully selected group of representative head models from a target population. By employing computational modeling techniques, we optimize stimulation parameters that best fit the group as a whole. The key idea? We minimize the overall error in stimulation effectiveness across multiple representative models, aiming for maximum consistency and effectiveness within the group.

Group Optimization vs. Traditional Templates: A Computational Modeling Breakthrough

Our findings demonstrate that this group optimization approach significantly outperforms traditional non-personalized templates, reducing variability and improving the generalization of results. More intriguingly, we discovered that certain simple anatomical features—like head perimeter measurements—can predict the effectiveness of a given stimulation setup, offering the potential for further refinement without the need for complex imaging.

The implications of this methodology are profound. Clinicians and researchers now have a powerful, scalable alternative to full personalization, enabling effective and accessible tES interventions in broader clinical and research settings.

Real-World Applications of Group Optimization in Clinical Studies

This new development has already been used in some work from Neuroelectrics. A recent study showed considerable improvement in patients with major depressive disorder (MDD) using a non-personalized stimulation protocol derived from group optimization. Additionally, within the Neurotwin project, group-based protocols tailored by head dimensions are currently being employed in a clinical study involving patients with Alzheimer’s disease (AD). More studies leveraging this innovative technique are forthcoming.

Interested in learning more about how group optimization can improve tES outcomes? Read our full preprint to explore the details and discover the exciting possibilities of computationally-driven brain stimulation.