Motoneuron excitability dysfunction in ALS: Pseudo-mystery or authentic conundrum?

J Physiol

Department of Neuroscience, Cell Biology, and Physiology, Boonshoft School of Medicine and College of Science and Mathematics, Wright State University, Dayton, OH, USA.

Published: November 2022

In amyotrophic lateral sclerosis (ALS), abnormalities in motoneuronal excitability are seen in early pathogenesis and throughout disease progression. Fully understanding motoneuron excitability dysfunction may lead to more effective treatments. Yet decades of research have not produced consensus on the nature, role or underlying mechanisms of motoneuron excitability dysfunction in ALS. For example, contrary to Ca excitotoxicity theory, predictions of motoneuronal hyper-excitability, normal and hypo-excitability have also been seen at various disease stages and in multiple ALS lines. Accordingly, motoneuron excitability dysfunction in ALS is a disputed topic in the field. Specifically, the form (hyper, hypo or unchanged) and what role excitability dysfunction plays in the disease (pathogenic or downstream of other pathologies; neuroprotective or detrimental) are currently unclear. Although several motoneuron properties that determine cellular excitability change in the disease, some of these changes are pro-excitable, whereas others are anti-excitable, making dynamic fluctuations in overall 'net' excitability highly probable. Because various studies assess excitability via differing methods and at differing disease stages, the conflicting reports in the literature are not surprising. Hence, the overarching process of excitability degradation and motoneuron degeneration is not fully understood. Consequently, the discrepancies on motoneuron excitability dysfunction in the literature represent a substantial barrier to our understanding of the disease. Emerging studies suggest that biological variables, variations in experimental protocols, issues of rigor and sampling/analysis strategies are key factors that may underlie conflicting data in the literature. This review highlights potential confounding factors for researchers to consider and also offers ideas on avoiding pitfalls and improving robustness of data.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669170PMC
http://dx.doi.org/10.1113/JP283630DOI Listing

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