A Statistically-Robust Model of the Axomyelin Unit under Normal Physiologic Conditions with Application to Disease States.

Despite tremendous progress in characterizing the myriad cellular structures in the nervous system, a full appreciation of the interdependent and intricate interactions between these structures is as yet unfulfilled. Indeed, few more so than the interaction between the myelin internode and its ensheathed axon. More than a half-century after the ultrastructural characterization of this axomyelin unit, we lack a reliable understanding of the physiological properties, the significance and consequence of pathobiological processes, and the means to gauge success or failure of interventions designed to mitigate disease. Herein, we highlight shortcomings in the most common statistical procedures used to characterize the myelin g ratio, with particular emphasis on the underlying principles of simple linear regression. These shortcomings lead to insensitive detection and/or ambiguous interpretation of normal physiology, disease mechanisms and remedial methodologies. To address these problems, we syndicate insights from early seminal myelin studies and use a statistical model of the axomyelin unit that is established in Gow (2025). Herein, we develop and demonstrate a statistically-robust analysis pipeline with which to examine and interpret axomyelin physiology and pathobiology in two disease states, experimental autoimmune encephalomyelitis and the rumpshaker mouse model of leukodystrophy. On a cautionary note, our pipeline is a relatively simple and streamlined approach that is not necessarily a panacea for all g ratio analyses. Rather, it approximates a minimum effort needed to elucidate departures from normal physiology and to determine if more comprehensive studies may lead to deeper insights.