Estimating axon conduction velocity in vivo from microstructural MRI

Mark Drakesmith*, Robbert Harms, Suryanarayana Umesh Rudrapatna, Greg D. Parker, C. John Evans, Derek K. Jones

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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). 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 ). 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.
Original languageEnglish
Article number116186
Number of pages19
JournalNeuroimage
Volume203
DOIs
Publication statusPublished - Dec 2019

Keywords

  • conduction velocity
  • Axon diameter
  • Myelin
  • G-ratio
  • Diffusion MRI
  • Relaxometry MRI
  • Tissue micro-structure
  • Biophysical modelling
  • White matter
  • Axons
  • Action potentials
  • Optic nerve
  • Corpus callosum
  • NEURITE ORIENTATION DISPERSION
  • MYELIN G-RATIO
  • NERVE-FIBERS
  • INTERNODAL LENGTH
  • SPINAL-CORD
  • DIAMETER
  • DIFFUSION
  • BRAIN
  • DENSITY
  • SENSITIVITY

Cite this

Drakesmith, M., Harms, R., Rudrapatna, S. U., Parker, G. D., Evans, C. J., & Jones, D. K. (2019). Estimating axon conduction velocity in vivo from microstructural MRI. Neuroimage, 203, [116186]. https://doi.org/10.1016/j.neuroimage.2019.116186
Drakesmith, Mark ; Harms, Robbert ; Rudrapatna, Suryanarayana Umesh ; Parker, Greg D. ; Evans, C. John ; Jones, Derek K. / Estimating axon conduction velocity in vivo from microstructural MRI. In: Neuroimage. 2019 ; Vol. 203.
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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). 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 ). 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.",
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author = "Mark Drakesmith and Robbert Harms and Rudrapatna, {Suryanarayana Umesh} and Parker, {Greg D.} and Evans, {C. John} and Jones, {Derek K.}",
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Drakesmith, M, Harms, R, Rudrapatna, SU, Parker, GD, Evans, CJ & Jones, DK 2019, 'Estimating axon conduction velocity in vivo from microstructural MRI', Neuroimage, vol. 203, 116186. https://doi.org/10.1016/j.neuroimage.2019.116186

Estimating axon conduction velocity in vivo from microstructural MRI. / Drakesmith, Mark; Harms, Robbert; Rudrapatna, Suryanarayana Umesh; Parker, Greg D.; Evans, C. John; Jones, Derek K.

In: Neuroimage, Vol. 203, 116186, 12.2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Estimating axon conduction velocity in vivo from microstructural MRI

AU - Drakesmith, Mark

AU - Harms, Robbert

AU - Rudrapatna, Suryanarayana Umesh

AU - Parker, Greg D.

AU - Evans, C. John

AU - Jones, Derek K.

PY - 2019/12

Y1 - 2019/12

N2 - 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). 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 ). 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.

AB - 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). 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 ). 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.

KW - conduction velocity

KW - Axon diameter

KW - Myelin

KW - G-ratio

KW - Diffusion MRI

KW - Relaxometry MRI

KW - Tissue micro-structure

KW - Biophysical modelling

KW - White matter

KW - Axons

KW - Action potentials

KW - Optic nerve

KW - Corpus callosum

KW - NEURITE ORIENTATION DISPERSION

KW - MYELIN G-RATIO

KW - NERVE-FIBERS

KW - INTERNODAL LENGTH

KW - SPINAL-CORD

KW - DIAMETER

KW - DIFFUSION

KW - BRAIN

KW - DENSITY

KW - SENSITIVITY

U2 - 10.1016/j.neuroimage.2019.116186

DO - 10.1016/j.neuroimage.2019.116186

M3 - Article

VL - 203

JO - Neuroimage

JF - Neuroimage

SN - 1053-8119

M1 - 116186

ER -