Background: Navigated repetitive TMS (nrTMS) is widely used for non-invasive mapping of cortical functions. Methodological improvement might be achieved by optimizing coil positioning based on electric-field modeling and augmented reality (AR)-guided neuronavigation to enhance spatial targeting accuracy and stimulation-induced language errors. Therefore, we compared electric-field-optimized, AR-guided nrTMS with conventional nrTMS using manually planned coil positioning. Methods: Twenty-eight healthy subjects underwent two MRI-guided left hemispheric nrTMS language mapping sessions. Each session used 10 Hz stimulation at a 100% resting motor threshold applied for 1.5 s per region of interest (ROI) during a synchronized object naming task. ROIs were defined according to the Corina cortical parcellation system. Manually defined and electric-field-optimized coil placements obtained using SimNIBS (v4.1.0) were applied; the optimized session was assisted by AR goggles. The primary outcome was the quantitative and categorical differences in cortical regions mapped as language-eloquent. Resting-state fMRI was acquired to provide a reference for comparing nrTMS-derived language maps. Outcomes: Electric-field-optimized nrTMS did not result in an increase in positively mapped ROIs. A different distribution of language errors was observed between sessions. Manual mapping roughly followed the extracted resting-state language and motor networks, whereas electric-field-optimized mapping might correspond less. Optimized coil positions were not always practically feasible. AR guidance improved target location accuracy. Conclusions: While AR was a useful addition to the TMS experiment, electric-field optimization did not translate into significant behavioral differences. However, altered distribution of language errors can give insight into underlying neurophysiological processes of rTMS.
Ritter et al. (Tue,) studied this question.