Optimizing TMS dosimetry: evaluating the effective electric field as a novel metric.

Objective. This study introduces the effective electric field (E_eff) as a novel observable for transcranial magnetic stimulation (TMS) numerical dosimetry.E_effrepresents the electric field component aligned with the local orientation of cortical and white matter (WM) neuronal elements. To assess the utility ofE_effas a predictive measure for TMS outcomes, we evaluated its correlation with TMS induced muscle responses and compared it against conventional observables, including the electric (E-)field magnitude, and its components normal and tangential to the cortical surface.Approach.Using a custom-made software for TMS dosimetry, theE_effis calculated combining TMS dosimetric results from an anisotropic head model with tractography data of gray and white matter (GM and WM). To test the hypothesis thatE_effhas a stronger correlation with muscle response, a proof-of-concept experiment was conducted. Seven TMS sessions, with different coil rotations, targeted the primary motor area of a healthy subject. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle.Main results.TheE_efftrend for the seven TMS coil rotations closely matched the measured MEP response, displaying an ascending pattern that peaked and then symmetrically declined. In contrast, theE-field magnitude and its components tangential (E_tan) and normal (E_norm) to the cortical surface were less responsive to coil orientation changes.E_effshowed a strong correlation with MEPs (r= 0.8), while the other observables had a weaker correlation (0.5 forE_normand below 0.2 forE-field magnitude andE_tan).Significance.This study is the first to evaluateE_eff, a novel component of the TMS inducedE-field. Derived using tractography data from both white and GM,E_effinherently captures axonal organization and local orientation. By demonstrating its correlation with MEPs, this work introducesE_effas a promising observable for future TMS dosimetric studies, with the potential to improve the precision of TMS applications.