Group optimization methods for dose planning in tES.

Optimizing transcranial electrical stimulation (tES) parameters-including stimulator settings and electrode placements, using magnetic resonance imaging-derived head models is essential for achieving precise electric field (E-field) distributions, enhancing therapeutic efficacy, and reducing inter-individual variability. However, the dependence on individually personalized MRI-based models limits their scalability in some clinical and research contexts. To overcome this limitation, we propose a novel group-level optimization framework employing multiple representative head models.The proposed optimization approach utilizes computational modeling based on multiple representative head models, selected to minimize group-level error compared to baseline (no stimulation). This method effectively balances focal stimulation intensity within targeted brain regions while minimizing off-target effects. We evaluated our method through computational modeling and leave-one-out cross-validation using data from 54 subjects, and analyzed the effectiveness, generalizability, and predictive utility of anatomical characteristics.Group-optimized protocols significantly outperformed standard template-based approaches when within-subject variability was accounted for using paired analyzes. Although average performance differences appeared modest in aggregate comparisons, paired statistical tests revealed that group-based solutions yielded systematically better targeting across participants. Additionally, group protocols consistently reduced the occurrence of poor outcomes observed with some templates. Correlations between anatomical features (e.g. head perimeter and tissue volumes) and E-field parameters revealed predictive relationships. This insight enables further optimization improvements through the strategic selection of representative head models that are electro-anatomically similar to the target subjects. Importantly, this approach eliminates the need forselection of a single representative template, offering a scalable and more flexible alternative when individualized MRI-based models are not available.The proposed group optimization framework provides a scalable and robust alternative to personalized approaches, substantially enhancing the feasibility and accessibility of model-driven tES protocols in diverse clinical and research environments.