Abstract
The conduction velocity (CV) of action potentials along axons is a key neurophysiological property central to neural communication. The ability to estimate CV in humans in vivo from non-invasive MRI methods would therefore represent a significant advance in neuroscience. However, there are two major challenges that this paper aims to address: (1) Much of the complexity of the neurophysiology of action potentials cannot be captured with currently available MRI techniques. Therefore, we seek to establish the variability in CV that can be captured when predicting CV purely from parameters that have been reported to be estimatable from MRI: inner axon diameter (AD) and g-ratio. (2) errors inherent in existing MRI-based biophysical models of tissue will propagate through to estimates of CV, the extent to which is currently unknown. Issue (1) is investigated by performing a sensitivity analysis on a comprehensive model of axon electrophysiology and determining the relative sensitivity to various morphological and electrical parameters. The investigations suggest that 85% of the variance in CV is accounted for by variation in AD and g-ratio. The observed dependency of CV on AD and g-ratio is well characterised by the previously reported model by Rushton. Issue (2) is investigated through simulation of diffusion and relaxometry MRI data for a range of axon morphologies, applying models of restricted diffusion and relaxation processes to derive estimates of axon volume fraction (AVF), AD and g-ratio and estimating CV from the derived parameters. The results show that errors in the AVF have the biggest detrimental impact on estimates of CV, particularly for sparse fibre populations (AVF<0.3). For our equipment set-up and acquisition protocol, CV estimates are most accurate (below 5% error) where AVF is above 0.3, g-ratio is between 0.6 and 0.85 and AD is high (above 4μm). CV estimates are robust to errors in g-ratio estimation but are highly sensitive to errors in AD estimation, particularly where ADs are small. We additionally show CV estimates in human corpus callosum in a small number of subjects. In conclusion, we demonstrate accurate CV estimates are possible in regions of the brain where AD is sufficiently large. Problems with estimating ADs for smaller axons presents a problem for estimating CV across the whole CNS and should be the focus of further study.
Highlights
The conduction velocity (CV) of action potentials along axons is a key neurophysiological property upon which neural communication depends
Errors in g-ratio estimates are mostly uniformly low, but slightly higher errors where g-ratio is high and axon volume fraction (AVF) is low. This corresponds to higher errors in the myelin water fraction (MWF)/ myelin volume fractions (MVF) estimates for these parameter values. g-ratio estimates are shown to have an increasing negative bias as g-ratio increases
There is a distinct profile of highest CV estimates in the body compared to the genu, which is consistent across most subjects
Summary
The conduction velocity (CV) of action potentials along axons is a key neurophysiological property upon which neural communication depends. Simple relationships between axon morphology and CV have been derived from early electrophysiological and theoretical literature (Gasser and Grundfest, 1939; Hursh, 1939; Huxley and St€ampeli, 1949; Rushton, 1951; Smith and Koles, 1970; Waxman and Bennett, 1972; Moore et al, 1978; Tolhurst and Lewis, 1992) (see Waxman (1980) for a review). An alternative model derived by Waxman and Bennett (1972) models CV as a simple linear function of outer fibre diameter: v∝D∝d=g. The constant of proportionality of this relationship usually falls in the range of 5.5–6.0 m sÀ1=μm and is often used as a simple way of predicting CV from fibre diameter (Caminiti et al, 2013; Tomasi et al, 2012; Innocenti et al, 2014)
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