Predicting the hotspot location and motor threshold prior to transcranial magnetic stimulation using electric field modelling

Abstract

Objective. To investigate whether the motor threshold (MT) and the location of the motor hotspot in transcranial magnetic stimulation (TMS) can be predicted with computational models of the induced electric field. Approach. Individualized computational models were constructed from structural magnetic resonance images of ten healthy participants, and the induced electric fields were determined with the finite element method. The models were used to optimize the location and direction of the TMS coil on the scalp to produce the largest electric field at a predetermined cortical target location. The models were also used to predict how the MT changes as the magnetic coil is moved to various locations over the scalp. To validate the model predictions, the motor evoked potentials were measured from the first dorsal interosseous (FDI) muscle with TMS in the ten participants. Both computational and experimental methods were preregistered prior to the experiments. Main results. Computationally optimized hotspot locations were nearly as accurate as those obtained using manual hotspot search procedures. The mean Euclidean distance between the predicted and the measured hotspot locations was approximately 1.3 cm with a 0.8 cm bias towards the anterior direction. Exploratory analyses showed that the bias could be removed by changing the cortical target location that was used for the prediction. The results also indicated a statistically significant relationship (p < 0.001) between the calculated electric field and the MT measured at several locations on the scalp. Significance. The results show that the individual TMS hotspot can be located using computational analysis without stimulating the subject or patient even once. Adapting computational modelling would save time and effort in research and clinical use of TMS.