Physiological Dynamics in Demyelinating Diseases: Unraveling Complex Relationships through Computer Modeling

Abstract

Despite intense research, few treatments are available for most neurological disorders. Demyelinating diseases are no exception. This is perhaps not surprising considering the multifactorial nature of these diseases, which involve complex interactions between immune system cells, glia and neurons. In the case of multiple sclerosis, for example, there is no unanimity among researchers about the cause or even which system or cell type could be ground zero. This situation precludes the development and strategic application of mechanism-based therapies. We will discuss how computational modeling applied to questions at different biological levels can help link together disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism. By making testable predictions and revealing critical gaps in existing knowledge, such models can help direct research and will provide a rigorous framework in which to integrate new data as they are collected. Nowadays, there is no shortage of data; the challenge is to make sense of it all. In that respect, computational modeling is an invaluable tool that could, ultimately, transform how we understand, diagnose, and treat demyelinating diseases.

Four in vivo g‐ratio‐weighted imaging methods: Comparability and repeatability at the group level

“…A fiber with a g ‐ratio around 0.6 has been theoretically shown to provide maximal speed of conducting neural signals in peripheral nerves [Rushton, ] but g ‐ratio values have been reported to be larger in the CNS in experimental data [Guy et al, ; Mason et al, ]. It has been suggested that in the healthy condition axons and their microscopic substructures (e.g., their g ‐ratio) are finely tuned biological devices and that changes of their composition can lead to clinical syndromes [Coggan et al, ]. This makes the g ‐ratio a functionally important property of axons and as such a relevant marker in patients and healthy volunteers alike.…”

Evaluation of the response to electric pulp testing in multiple sclerosis patients without a history of trigeminal neuralgia: a case-control study

“…Coggan in his study showed tongue somatosensory evoked potentials are an efficient method for evaluating the afferent trigeminal nerve in MS patients. He claimed it is more sensitive than MRI to detect early damage [ 33 ].…”

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

“…Signal transmission along different axonal fibres can be regulated and synchronised by varying the degree of myelination, and therefore the g-ratio, to optimize cognitive function, sensory integration and motor skills ( Fields, 2015 ). As the central nervous system appears to communicate at physical limits to constrain metabolic demands ( Salami et al, 2003 ; Hartline and Colman, 2007 ; Coggan et al, 2015 ), small deviations from the optimal g-ratio value (0.6−0.8, ( Rushton, 1951 ; Chomiak and Hu, 2009 )) may have strong functional impact.

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