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

: Objective. This study introduces the effective electric field (Eeff) as a novel observable for transcranial magnetic stimulation (TMS) numerical dosimetry.Eeffrepresents the electric field component aligned with the local orientation of cortical and white matter (WM) neuronal elements. To assess the utility ofEeffas 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, theEeffis 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 thatEeffhas 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.TheEefftrend 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 (Etan) and normal (Enorm) to the cortical surface were less responsive to coil orientation changes.Eeffshowed a strong correlation with MEPs (r= 0.8), while the other observables had a weaker correlation (0.5 forEnormand below 0.2 forE-field magnitude andEtan).Significance.This study is the first to evaluateEeff, a novel component of the TMS inducedE-field. Derived using tractography data from both white and GM,Eeffinherently captures axonal organization and local orientation. By demonstrating its correlation with MEPs, this work introducesEeffas a promising observable for future TMS dosimetric studies, with the potential to improve the precision of TMS applications.