Simulating progressive motor neuron degeneration and collateral reinnervation in motor neuron diseases using a dynamic muscle model based on human single motor unit recordings

Objective. To simulate progressive motor neuron loss and collateral reinnervation in motor neuron diseases (MNDs) by developing a dynamic muscle model based on human single motor unit (MU) surface-electromyography (EMG) recordings. Approach. Single MU potentials recorded with high-density surface-EMG from thenar muscles formed the basic building blocks of the model. From the baseline MU pool innervating a muscle, progressive MU loss was simulated by removal of MUs, one-by-one. These removed MUs underwent collateral reinnervation with scenarios varying from 0% to 100%. These scenarios were based on a geometric variable, reflecting the overlap in MU territories using the spatiotemporal profiles of single MUs and a variable reflecting the efficacy of the reinnervation process. For validation, we tailored the model to generate compound muscle action potential (CMAP) scans, which is a promising surface-EMG method for monitoring MND patients. Selected scenarios for reinnervation that matched observed MU enlargements were used to validate the model by comparing markers (including the maximum CMAP and a motor unit number estimate (MUNE)) derived from simulated and recorded CMAP scans in a cohort of 49 MND patients and 22 age-matched healthy controls. Main results. The maximum CMAP at baseline was 8.3 mV (5th-95th percentile: 4.6 mV-11.8 mV). Phase cancellation caused an amplitude drop of 38.9% (5th-95th percentile, 33.0%-45.7%). To match observations, the geometric variable had to be set at 40% and the efficacy variable at 60%-70%. The Delta maximum CMAP between recorded and simulated CMAP scans as a function of fitted MUNE was -0.4 mV (5th-95th percentile = -4.0 - +2.4 mV). Significance. The dynamic muscle model could be used as a platform to train personnel in applying surface-EMG methods prior to their use in clinical care and trials. Moreover, the model may pave the way to compare biomarkers more efficiently, without directly posing unnecessary burden on patients.