Spatial Recruitment Model for Motor Potentials Evoked By Transcranial Magnetic Stimulation (TMS) for In-Silico Testing and Tuning of Closed-Loop Procedures

Abstract

The primary motor cortex has a prominent role in transcranial magnetic stimulation (TMS). It is one of few locations that provide directly observable responses, and its physiology serves as model or reference for almost all other TMS targets, e.g., through the motor threshold and spatial targeting relative to its position. It furthermore sets the safety limits for the entire brain. Its easily detectable responses have led to closed-loop methods for a range of aspects, e.g., for automated thresholding, amplitude tracking, and targeting. The high variability of brain stimulation methods would substantially benefit from fast unbiased closed-loop methods. However, the development of more potent methods would early on in the design phase require proper models that allowed tuning and testing with sufficient conditions but without a high number of experiments, which are time-consuming and expensive or even impossible at the needed scale. On the one hand, theoretical researchers without access to experiments miss realistic spatial response models of brain stimulation to develop better methods. On the other hand, subjects should potentially not be exposed to early closed-loop-methods without sufficient prior testing. After all, initial, poorly tuned feed-back as needed for closed-loop operation is known to demonstrate erratic behavior. To bridge this gap, we developed a digital-twin-style population model that generates motor evoked potentials in response to virtual stimuli and includes statistical information on spatial (coil position and orientation) as well as pulse-strength-dependent neural recruitment in the population to represent inter- and intra-individual variability. The population data were extracted from a combination of datasets of healthy and depressed subjects to reach large numbers and wide coverage. The model allows users to simulate different subjects and millions of runs for software-in-the loop testing. The model provides all code open-source to stimulate further development.