A statistical damage model for coupled mechano-electrophysiological axonal injury

Zexuan Chen, Liqun Tang, Bao Yang, Yiping Liu, Zhenyu Jiang, Licheng Zhou and Zejia Liu

PLOS Computational Biology, 2026, vol. 22, issue 10, 1-27

Abstract: Deformation-induced injury of white matter nerve bundles is prevalent in central nervous system injuries, leading to a series of neurological dysfunctions. To describe the effect of deformation injury on the physiological electrical signals of neurons, several axonal electrophysiological damage models based on the Hodgkin-Huxley equations have been proposed. However, none of these models can fully characterize the results of white matter nerve stretching experiments under different rates and amplitudes. Building on previous work by peers, we propose a novel axonal deformation damage model that comprehensively considers the coupled effects of sodium and potassium channel damage on gating variable rate constants, as well as the differences in mechanical properties and injury among individual axons during deformation—with these differences continuously modeled by a statistical distribution. The proposed model can not only reasonably describe the sodium left-shift phenomenon (a key post-injury feature) but also accurately reproduce the neuro-electrophysiological responses of nerve fascicles under various stretching conditions. This new neuro-electrophysiological model for nerve fascicle injury exhibits good universality and provides a mechanical mechanism-based reference for future clinical treatment strategies of nerve injury.Author summary: Deformation-induced injury of white matter nerve bundles is a common pathology in traumatic central nervous system injuries. Existing models disagree on ion channel injury mechanisms and insufficiently match classic experimental data, requiring refinement. To address this, we modified existing models by innovatively integrating the comprehensive effects of sodium and potassium channel damage on gating rates, and introducing a statistical distribution for micro-axonal injury heterogeneity within nerve bundles. Results show that our model effectively compensates for the limitations of existing models. It not only provides a more unified and detailed explanatory framework for neural deformation injury but also lays a research foundation from a statistical perspective, promising to support traumatic brain injury clinical assessment and neuroprotective strategies development with mechanical basis.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014837

DOI: 10.1371/journal.pcbi.1014837

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