Spinal Recurrent Inhibition Shapes the Dynamics of TMS-induced Motor-Evoked Potentials: A Computational Modeling Study

Abstract

Motor-evoked potentials (MEPs) recorded via surface electromyography (EMG) from peripheral muscles following transcranial magnetic stimulation (TMS) of the motor cortex reflect the integrity of the entire corticospinal pathway and are widely used in both basic neuroscience and clinical practice. However, the relative contributions of spinal and peripheral mechanisms to the observed MEP waveform remain poorly understood, partly because computational models that capture individual MEP characteristics are lacking. Here, we present a biologically plausible and computationally efficient model of the descending motor pathway, spanning the spinal cord and hand muscles, that can be fitted to individual MEP waveforms across a range of TMS intensities. The model successfully reproduces individual MEP waveforms, accounting for approximately 90% of the observed variance in waveforms across 10 healthy participants. Crucially, we demonstrate that recurrent inhibition of Renshaw cells in the spinal cord is indispensable for reproducing the fine temporal structure of MEP waveforms, even when input-output curve fitting appears adequate without it. Beyond waveform reproduction, the fitted model provides interpretable estimates of latent neural dynamics and subject-specific pathway parameters, including motor neuron size distribution, synaptic receptor balance, axonal conduction delay, and hand muscle refractoriness, that are consistent with known biological ranges. These results suggest that individual MEP waveforms, when analyzed using a biologically grounded model, carry substantially more information about spinal and peripheral motor pathway integrity than conventional amplitude-based measures alone.