Adaptive Recruitment Curve Analysis Using Bayesian Modeling

Study Summary

The purpose of this study is to better understand how electrical or magnetic stimulation affect the nervous system by optimizing the way researchers measure muscle responses. The relationship between stimulation intensity and muscle response is described by "neural recruitment curves," which are critical for monitoring the state of the nervous system during therapies like transcranial magnetic stimulation (TMS) and spinal cord stimulation (SCS). This study tests a new, real-time computational approach based on our previously developed methods (Hierarchical Bayesian models) to estimate these recruitment curves more efficiently. The primary goal is to use this model to dynamically guide the experiment, automatically selecting the optimal stimulation intensities to test. The investigators hypothesize that this optimized approach will accurately estimate the entire recruitment curve, or specific targets components of it like the motor threshold, using significantly fewer samples than standard methods. By reducing the number of measurements required, this approach aims to decrease experimental time and minimize participant burden, making future TMS and SCS therapies and experiments more feasible and efficient.

Primary Outcome

Number of stimuli required for the compared methods to reach a pre-defined error threshold relative to the ground truth, computed from recruitment curves fitted after sampling using aggregated data.

Interventions

• OTHER Algorithm: Uniform Sampling
• OTHER Algorithm: hbMEP-adaptive algorithm (version 1)
• OTHER Algorithm: hbMEP-adaptive algorithm (version 2)
• OTHER ML-PEST
• DEVICE MagPro X100 Transcranial Magnetic Stimulation