27: Identifying voltage-gated ion channel dysfunction from extracellular C-fiber recordings

Topic: Basic Science
Subtopic: Non-specific to any diagnosis (including studies in healthy animals), Developing methodology/technology, Electrophysiology, Artificial Intelligence/Machine Learning

Background and Aims: Up to 10% of people suffer from painful neuropathy (1), yet mechanisms remain unclear, limiting treatment development (2). Dysfunction of voltage-gated ion channels in peripheral C-fibers is one candidate mechanism (3,4). Inflammation, metabolic factors, and mutations can alter channel gating and ionic currents, thereby explaining increased excitability of C-fibers observed in painful neuropathy (5,6). Because voltage-gated ion channels shape the waveform of nerve action potentials (7,8), we hypothesize that channel alterations produce signatures in extracellular recordings of these potentials. Via microneurography, such recordings could be conducted in awake humans. However, identification of these signatures is experimentally difficult, as channel alterations in humans cannot be selectively manipulated or isolated from co-occurring changes in other channels, which potentially mask existing signatures.

Here, we aimed to develop a computational approach to identify extracellular signatures of defined channel alterations. If detectable in microneurographic recordings, such signatures could help identify ion-channel dysfunction in neuropathic pain.

Methods: We extended an established computer model of peripheral unmyelinated C-fibers (9,10) implemented in the NEURON simulation environment (11) to include a biophysically grounded extracellular recording mechanism (12). This extension enabled simulation of extracellular action potentials as recorded by a microneurography electrode positioned adjacent to the nerve fiber. We implemented a gain-of-function mutation affecting Nav1.7 associated with paroxysmal extreme pain disorder (PEPD) by shifting the fast inactivation gate by 20 mV towards depolarized potentials. The effect of the mutation-induced changes in ionic currents and excitability on the extracellular action potential waveform was compared against the baseline model to assess whether channel-induced alterations produced distinguishable extracellular waveforms.

Results: Applying the developed computational approach, simulations of a C-fiber model incorporating a PEPD-related gain-of-function mutation in Nav1.7 produced changes in extracellular action potential waveforms relative to baseline. These results demonstrate that our method can resolve channel-specific effects on extracellular waveforms and can help identify ion-channel-dependent signatures in extracellular recordings.

Conclusions: We developed a computational framework to identify extracellular waveform signatures arising from specific ion-channel alterations. This circumvents a key limitation of purely experimental approaches: in human subjects, changes in individual channels cannot be isolated from co-occurring changes in other channels. Applying this to a PEPD-related Nav1.7 gain-of-function mutation, we identified mutation-dependent changes in extracellular waveforms relative to a physiological baseline, suggesting that channel-specific signatures are detectable under controlled computational conditions.

Our work opens a computational path to link ion-channel dysfunction to signatures in microneurography recordings. Whether these signatures remain detectable despite biological and measurement variability has yet to be determined. Future work will extend the analysis to additional channel pathophysiologies and validate whether the predicted effects are detectable in human recordings.

References: 1. Colloca, L et al. (2017). Neuropathic pain. Nat Rev Dis Primers 3, 17002. Doi: 10.1038/nrdp.2017.2
2. Yeung AM et al. (2022). Painful Diabetic Neuropathy: The Need for New Approaches. J Diabetes Sci Technol 18(1):159-67. Doi: 10.1177/19322968221132252
3. Waxman SG (2023). Targeting a Peripheral Sodium Channel to Treat Pain. N Engl J Med 389(5):466-69. Doi: 10.1056/NEJMe2305708
4. Felix, R., Corzo-Lopez, A., & Sandoval, A. (2025). Voltage-Gated Ion Channels in Neuropathic Pain Signaling. Life 15(6), 888. Doi: 10.3390/life15060888
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7. Everill B, Rizzo MA, Kocsis JD (1998). Morphologically identified cutaneous afferent DRG neurons express three different potassium currents in varying proportions. J Neurophysiol 79(4):1814-24. Doi: 10.1152/jn.1998.79.4.1814
8. Dib-Hajj SD, Geha P, Waxman SG (2017). Sodium channels in pain disorders: pathophysiology and prospects for treatment. Pain 158(Suppl 1): S97–S107. Doi: 10.1097/j.pain.0000000000000854
9. Tigerholm J et al. (2014). Modeling activity-dependent changes of axonal spike conduction in primary afferent C-nociceptors. J Neurophysiol 111(9):1721-35. Doi: 10.1152/jn.00777.2012
10. Maxion A et al. (2023). A modelling study to dissect the potential role of voltage-gated ion channels in activity-dependent conduction velocity changes as identified in small fiber neuropathy patients. Front Comput Neurosci 14;17:1265958. Doi: 10.3389/fncom.2023.1265958
11. Carnevale NT, Hines ML (2006). The NEURON Book. Cambridge: Cambridge University Press.
12. Hagen E et al. (2018). Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Front Neuroinform 12:92. Doi: 10.3389/fninf.2018.00092

Presenting Author: David Berling
Submitting Author: David Berling

Senior Author: Jenny Tigerholm

IASP 2026 Global Year theme of Neuropathic Pain: Yes

• Jenny Tigerholm: Jun.-Prof. Dr. - Scientific Center for Neuropathic Pain Aachen SCN-AACHEN, Uniklinik RWTH Aachen University; Joint Research Center for Computational Biomedicine, Uniklinik RWTH Aachen University