Reducing target E-field variability in repetitive TMS through online motion compensation

Precise targeting and dosing are critical for neurophysiological effectivity of repetitive transcranial magnetic stimulation (rTMS), particularly in clinical applications such as treating major depressive disorder (MDD). While neuronavigation enables accurate, individualized coil positioning, even small deviations in coil placement, e.g. during extended stimulation protocols, can significantly alter the induced electric field (E-field). In this study, we use continuous neuronavigational monitoring during stimulation to quantify motion-induced E-field variability at the target and introduce a novel methodology for compensating it.

We analyzed coil-target movement parameters in a sample of 200 rTMS sessions conducted in 20 adults with MDD, evaluating position, rotation and main axes of movement. In addition, we simulated induced E-fields within a target-ROI and quantified variability within- and across-sessions. To mitigate movement-related variability, we developed an algorithm which enables real-time adjustment of stimulator output based on current coil position and interpolation of precomputed E-fields.

Our results show that E-field variability in this sample was primarily driven by coil displacement along the scalp-normal and rotation. Lateral movement played a minor role. Using the new stimulation amplitude adjustment strongly reduced target E-field variability. Mean E-field coefficient of variation was reduced within-session by 41% (2.85%-1.67%) and across-sessions by 74% (6.77%-1.73%).

This study presents the first quantitative analysis of motion during rTMS treatment sessions and a practical method to compensate for it. Given its low computational cost, the proposed approach is well suited for clinical implementation, potentially enhancing treatment reliability, particularly in individuals prone to motion.