Beyond snapshots: A dynamic framework for continuous motor unit tracking to enhance neurophysiological assessments

Abstract

Tracking individual motor units (MUs) across multiple testing visits is critical for understanding neuromuscular adaptations and disease progression. However, existing methods fail to account reliably for spatiotemporal variations in motor unit action potentials (MUAPs) and lack validation for longitudinal tracking. Here, we present a robust computational framework for continuous MU tracking that integrates automatic estimation of electrode grid displacements and compensation for MUAP shape variations. Our algorithm demonstrated superior tracking accuracy, capturing 63% and 75% of MUs in the tibialis anterior and medial gastrocnemius, respectively, substantially outperforming state-of-the-art methods. By applying this approach to investigate estradiol-driven neuromuscular plasticity as a model system, we reveal previously undetectable modulations in recruitment thresholds and firing rates, linking hormonal fluctuations to motor unit behavior. Beyond advancing fundamental neuromuscular research, our approach holds promise for enhancing biomarker sensitivity in clinical trials, particularly in conditions such as amyotrophic lateral sclerosis, where reliable MU tracking is paramount. These findings establish a new standard for non-invasive motor unit tracking, unlocking opportunities for longitudinal neurophysiological assessment and evaluation of therapeutics.