Reducing Cranial Morphology Error in F3 Scalp Localization: Comparison of Heuristic and Regression-Based Methods

Accurate localization of the F3 scalp position is essential for targeting the dorsolateral prefrontal cortex in repetitive transcranial magnetic stimulation (rTMS), particularly when neuronavigation is unavailable. However, simplified scalp-based heuristic methods may lead to clinically relevant targeting errors due to substantial interindividual variability in cranial morphology. In this context, this study evaluates the geometric accuracy of commonly used and newly proposed scalp-based approaches for localizing the F3 position within the International 10-20 system. Specifically, it examines the impact of cranial morphological variability on localization error and assesses whether individualized regression-based methods can improve targeting accuracy compared to traditional heuristic techniques.

The intrinsic geometric accuracy of five F3 localization methods was systematically compared: BeamF3, Adjusted BeamF3, the Scalp Geometry-based Parameter (SGP) method, NeurallyF3, and a newly developed approach EPlacementF3. A total of 250 anatomically realistic and morphologically diverse 3D head models (125 female, 125 male), generated using the HumanShape platform, were analyzed. Localization error was quantified as the Euclidean distance between each estimated F3 position and the reference coordinate defined by the International 10-20 system.

BeamF3 and Adjusted BeamF3 showed the highest localization errors (mean >5.5 mm and >8 mm, respectively), while the SGP method improved accuracy (≈2.4 mm). In contrast, NeurallyF3 and EPlacementF3 achieved submillimetric errors across sexes. EPlacementF3 demonstrated the best overall performance, with the lowest mean error (∼0.2 mm) and consistent results in the independent validation dataset. These findings highlight the strong influence of cranial morphology on heuristic methods and the superior robustness of individualized regression-based approaches.

Commonly used heuristic methods do not reliably recover the true F3 position across heterogeneous cranial morphologies. In contrast, individualized regression-based approaches substantially reduce morphology-related error, supporting more accurate and scalable MRI-free targeting for rTMS and other non-invasive brain stimulation applications.