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Computational Insights Into Neuromodulation: How Electric Fields Shape Motor Neuron Excitability

Aug 2026 · IEEE transactions on neural systems and rehabilitation engineering · Vol 34, pp. 3958-3969 · 0 citations · 60 references
Medicine

TL;DR

It is suggested that low-intensity EEFs produce modest, morphology-dependent modulation of MN electrophysiological properties, and that variability in neuronal structure and orientation may help explain discrepancies across previous experimental and modeling studies.

Abstract

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative disease characterized by the selective loss of spinal motor neurons (MN). Trans-spinal direct current stimulation (tsDCS) has emerged as a promising noninvasive neuromodulation technique that could provide neuroprotection and slow down disease progression. However, the mechanisms by which tsDCS affects individual MNs remain poorly understood. This study uses computational modeling to explore how low-intensity extracellular electric fields (EEFs), generated by tsDCS, influence the electrophysiological behavior of MNs. Morphologically realistic, multi-compartment models of neonate mouse alpha-MNs were developed in the NEURON simulation environment. Simulations were conducted under different EEF magnitudes, polarities and orientations. At the population level, consistent directional effects on excitability-related properties were limited and depended on stimulation condition. However, analyses of response magnitude showed that EEFs could modulate several properties, including resting membrane potential and rheobase, even when the direction of change varied across neurons. This heterogeneity was associated with neuronal morphology and its alignment with the applied field, with dendritic length and asymmetry affecting sensitivity. Overall, the results suggest that low-intensity EEFs produce modest, morphology-dependent modulation of MN electrophysiological properties, and that variability in neuronal structure and orientation may help explain discrepancies across previous experimental and modeling studies.

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