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Cited by 83Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

Estimating axon conduction velocity in vivo from microstructural MRI

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Drakesmith, Mark, Harms, Robbert, Rudrapatna, Suryanarayana Umesh, Parker, Greg D., Evans, C. John, Jones, Derek K.·2019·NeuroImage
DOI 10.1016/j.neuroimage.2019.116186PMC PMC6854468

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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<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.

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  • In combination with other measurements and explicit modeling assumptions, such features may eventually support estimates of conduction velocity.

    “In combination with other measurements and explicit modeling assumptions, such features may eventually support estimates of conduction velocity (Drakesmith et al., 2019).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Drakesmith et al., 2019

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  1. Aboitiz F. Scheibel a.B. Fisher R.S. Zaidel E. Fiber composition of the human corpus callosum Brain Res. 598 1992 143 153 http://www.ncbi.nlm.nih.gov/pubmed/1486478http://www.ncbi.nlm.nih.gov/pubmed/20734063 1486477
  2. Agrawal D. Hawk R. Avila R.L. Inouye H. Kirschner D.A. Internodal myelination during development quantitated using X-ray diffraction J. Struct. Biol. 168 2009 521 526 10.1016/j.jsb.2009.06.019 19576284 · DOI
  3. Akaike H. Information theory and the maximum likelihood principle 2nd International Symposium on Information Theory 1973
  4. Alexander D.C. Hubbard P.L. Hall M.G. Moore E.A. Ptito M. Parker G.J.M. Dyrby T.B. Orientationally invariant indices of axon diameter and density from diffusion MRI Neuroimage 52 2010 1374 1389 http://www.ncbi.nlm.nih.gov/pubmed/20580932http://linkinghub.elsevier.com/retrieve/pii/S1053811910007755 20580932
  5. Andersson J.L. Sotiropoulos S.N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging Neuroimage 125 2016 1063 1078 https://linkinghub.elsevier.com/retrieve/pii/S1053811915009209 26481672 PMC4692656
  6. Arancibia-Cárcamo I.L. Ford M.C. Cossell L. Ishida K. Tohyama K. Attwell D. Node of Ranvier length as a potential regulator of myelinated axon conduction speed eLife 6 2017 1 15 http://elifesciences.org/lookup/doi/10.7554/eLife.23329 PMC5313058 28130923 · DOI
  7. Assaf Y. Basser P.J. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain Neuroimage 27 2005 48 58 http://www.ncbi.nlm.nih.gov/pubmed/15979342 15979342
  8. Assaf Y. Blumenfeld-Katzir T. Yovel Y. Basser P.J. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI Magn. Reson. Med. 59 2008 1347 1354 http://www.ncbi.nlm.nih.gov/pubmed/18506799 18506799 PMC4667732
  9. Barazany D. Jones D. Assaf Y. AxCaliber 3D Proceedings of the International Society for Magnetic Resonance in Medicine 2011 Montréal Canada 76
  10. Berman S. Filo S. Mezer A. Modeling conduction delays in the corpus callosum using MRI-measured g-ratio Neuroimage 195 2019 128 139 https://www.biorxiv.org/content/early/2018/11/28/479881https://linkinghub.elsevier.com/retrieve/pii/S1053811919302022 30910729
  11. Brinkmann B.G. Agarwal A. Sereda M.W. Garratt A.N. Müller T. Wende H. Stassart R.M. Nawaz S. Humml C. Velanac V. Radyushkin K. Goebbels S. Fischer T.M. Franklin R.J. Lai C. Ehrenreich H. Birchmeier C. Schwab M.H. Nave K.A. Neuregulin-1/ErbB signaling serves distinct functions in myelination of the peripheral and central nervous system Neuron 59 2008 581 595 18760695 PMC2628490
  12. Budd J.M. Kovács K. Ferecskó A.S. Buzás P. Eysel U.T. Kisvárday Z.F. Neocortical axon arbors trade-off material and conduction delay conservation PLoS Comput. Biol. 2010 PMC2837396 20300651
  13. Burcaw L.M. Fieremans E. Novikov D.S. Mesoscopic structure of neuronal tracts from time-dependent diffusion Neuroimage 114 2015 18 37 10.1016/j.neuroimage.2015.03.061 25837598 PMC4446209 · DOI
  14. Caminiti R. Carducci F. Piervincenzi C. Battaglia-Mayer a. Confalone G. Visco-Comandini F. Pantano P. Innocenti G.M. Diameter, length, speed, and conduction delay of callosal axons in macaque monkeys and humans: comparing data from histology and magnetic resonance imaging diffusion tractography J. Neurosci. 33 2013 14501 14511 http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.0761-13.2013 24005301 PMC6618375 · DOI
  15. Carr C.E. Konishi M. Axonal delay lines for time measurement in the owl's brainstem Proc. Natl. Acad. Sci. 1988 PMC282419 3186725
  16. Chiappiniello A. Reggioli V. Tarducci R. Catani M. Dell'Acqua F. Axonal distributions: a simulation study to estimate Diffusion MRI signal contributions in white matter Proceedings of the International Society for Magnetic Resonance in Medicine 2018 5250 Paris, France
  17. Daducci A. Dal Palù A. Lemkaddem A. Thiran J.P. COMMIT: convex optimization modeling for microstructure informed tractography IEEE Trans. Med. Imaging 34 2015 246 257 25167548
  18. De Santis S. Jones D.K. Roebroeck A. Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter Neuroimage 130 2016 91 103 10.1016/j.neuroimage.2016.01.047 26826514 PMC4819719 · DOI
  19. Dell'Acqua F. Dallyn R. Chiappiniello A. Beyh A. Tax C. Jones D.K. Catani M. Temporal Diffusion Ratio (TDR): a Diffusion MRI technique to map the fraction and spatial distribution of large axons in the living human brain Proceedings of the International Society for Magnetic Resonance in Medicine 2019 Montréal Canada 64
  20. Deoni S.C. Transverse relaxation time (T 2) mapping in the brain with off-resonance correction using phase-cycled steady-state free precession imaging J. Magn. Reson. Imaging 30 2009 411 417 http://www.ncbi.nlm.nih.gov/pubmed/19629970 10.1002/jmri.21849 19629970 · DOI
  21. Deoni S.C.L. High-resolution T1 mapping of the brain at 3T with driven equilibrium single pulse observation of T1 with high-speed incorporation of RF field inhomogeneities (DESPOT1-HIFI) J. Magn. Reson. Imaging 26 2007 1106 1111 http://www.ncbi.nlm.nih.gov/pubmed/17896356 17896356
  22. Deoni S.C.L. Rutt B.K. Arun T. Pierpaoli C. Jones D.K. Gleaning multicomponent T1 and T2 information from steady-state imaging data Magn. Reson. Med. 60 2008 1372 1387 http://www.ncbi.nlm.nih.gov/pubmed/19025904 19025904
  23. Drakesmith M. Rudrapatna S.U. de Santis S. Jones D.K. Estimating axon diameter distributions beyond the physical limits of acquisition capabilities Proceedings of the International Society for Magnetic Resonance in Medicine 2018 5235 Paris, France
  24. Drobnjak I. Zhang H. Ianu A. Kaden E. Alexander D.C. PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: insight from a simulation study Magn. Reson. Med. 75 2016 688 700 http://www.ncbi.nlm.nih.gov/pubmed/25809657 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4975609 10.1002/mrm.25631 25809657 PMC4975609 · DOI
  25. Duval T. Lévy S. Stikov N. Cohen-Adad J. Stikov N. Campbell J. Mezer A. Witzel T. Keil B. Smith V. Wald L.L. Klawiter E. Lévy S. Cohen-Adad J. g-Ratio weighted imaging of the human spinal cord in vivo Neuroimage 145 2017 11 23 http://www.ncbi.nlm.nih.gov/pubmed/27664830http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5179300 27664830 PMC5179300
  26. Ellerbrock I. Mohammadi S. Four in vivo g-ratio-weighted imaging methods: comparability and repeatability at the group level Hum. Brain Mapp. 39 2018 24 41 10.1002/hbm.23858 29091341 PMC6866374 · DOI
  27. Eyal G. Verhoog M.B. Testa-Silva G. Deitcher Y. Lodder J.C. Benavides-Piccione R. Morales J. DeFelipe J. de Kock C.P. Mansvelder H.D. Segev I. Unique membrane properties and enhanced signal processing in human neocortical neurons eLife 2016 PMC5100995 27710767
  28. Fieremans E. Burcaw L.M. Lee H.H. Lemberskiy G. Veraart J. Novikov D.S. In vivo observation and biophysical interpretation of time-dependent diffusion in human white matter Neuroimage 129 2016 414 427 10.1016/j.neuroimage.2016.01.018 26804782 PMC4803645 · DOI · Paper page
  29. Ford M.C. Alexandrova O. Cossell L. Stange-Marten A. Sinclair J. Kopp-Scheinpflug C. Pecka M. Attwell D. Grothe B. Tuning of Ranvier node and internode properties in myelinated axons to adjust action potential timing Nat. Commun. 6 2015 8073 http://www.nature.com/ncomms/2015/150825/ncomms9073/full/ncomms9073.htmlhttp://www.ncbi.nlm.nih.gov/pubmed/26305015http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4560803http://www.nature.com/doifinder/10.1038/ncomms9073 26305015 PMC4560803 · DOI
  30. Friede R.L. Bischhausen R. The precise geometry of large internodes J. Neurol. Sci. 48 1980 367 381 7441287
  31. Gasser H.S. Grundfest H. Axon diameters in relation to the spike dimensions and the conduction velocity in mammalian a fibers Am. J. Physiol. 127 1939 393 414 http://www.physiology.org/doi/10.1152/ajplegacy.1939.127.2.393 · DOI
  32. Glasser M.F. Sotiropoulos S.N. Wilson J.A. Coalson T.S. Fischl B. Andersson J.L. Xu J. Jbabdi S. Webster M. Polimeni J.R. Van Essen D.C. Jenkinson M. The minimal preprocessing pipelines for the Human Connectome Project Neuroimage 80 2013 105 124 https://linkinghub.elsevier.com/retrieve/pii/S1053811913005053 23668970 PMC3720813
  33. Harms R.L. Fritz F.J. Tobisch A. Goebel R. Roebroeck A. Robust and fast nonlinear optimization of diffusion MRI microstructure models Neuroimage 155 2017 82 96 10.1016/j.neuroimage.2017.04.064 28457975 PMC5518773 · DOI
  34. Horowitz A. Barazany D. Tavor I. Bernstein M. Yovel G. Assaf Y. In vivo correlation between axon diameter and conduction velocity in the human brain Brain Struct. Funct. 220 2015 1777 1788 10.1007/s00429-014-0871-0 25139624 · DOI
  35. Hursh J.B. Conduction velocity and diameter of nerve fibers Am. J. Physiol. 127 1939 131 139 http://www.physiology.org/doi/10.1152/ajplegacy.1939.127.1.131 · DOI · Paper page
  36. Huxley A.F. Stämpeli R. Evidence for saltatory conduction in peripheral myelinated nerve fibres J. Physiol. 108 1949 315 339 http://www.ncbi.nlm.nih.gov/pubmed/16991863 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1392492 10.1113/jphysiol.1949.sp004335 18144923 · DOI
  37. Ibrahim M. Butt A.M. Berry M. Relationship between myelin sheath diameter and internodal length in axons of the anterior medullary velum of the adult rat J. Neurol. Sci. 133 1995 119 127 8583214
  38. Innocenti G.M. Network causality, axonal computations, and Poffenberger Exp. Brain Res. 235 2017 2349 2357 28488011 PMC5502070
  39. Innocenti G.M. Caminiti R. Aboitiz F. Comments on the paper by Horowitz et al. (2014) Brain Struct. Funct. 220 2015 1789 1790 10.1007/s00429-014-0974-7 25579065 · DOI
  40. Innocenti G.M. Vercelli A. Caminiti R. The diameter of cortical axons depends both on the area of origin and target Cerebr. Cortex 24 2014 2178 2188 23529006
  41. Jones D.K. Precision and accuracy in diffusion tensor magnetic resonance imaging Top. Magn. Reson. Imaging 21 2010 87 99 http://www.ncbi.nlm.nih.gov/pubmed/21613874https://insights.ovid.com/crossref?an=00002142-201004000-00004 21613874
  42. Jones D.K. Alexander D.C. Bowtell R. Cercignani M. Dell'Acqua F. McHugh D.J. Miller K.L. Palombo M. Parker G.J. Rudrapatna U.S. Tax C.M. Microstructural imaging of the human brain with a super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI Neuroimage 182 2018 8 38 10.1016/j.neuroimage.2018.05.047 29793061 · DOI
  43. Jung W. Lee J.J. Shin H.G. Nam Y. Zhang H. Oh S.H. Lee J.J. Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI) Neuroimage 182 2018 379 388 http://www.ncbi.nlm.nih.gov/pubmed/28962901 28962901
  44. Kellner E. Dhital B. Kiselev V.G. Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts Magn. Reson. Med. 76 2016 1574 1581 10.1002/mrm.26054 26745823 · DOI · Paper page
  45. Lankford C.L. Does M.D. On the inherent precision of mcDESPOT Magn. Reson. Med. 69 2013 127 136 http://www.ncbi.nlm.nih.gov/pubmed/22411784http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3449046 22411784 PMC3449046
  46. Lee H.H. Fieremans E. Novikov D.S. What dominates the time dependence of diffusion transverse to axons: intra- or extra-axonal water? NeuroImage 2017 1 11 http://arxiv.org/abs/1707.09426 PMC6004237 29253652 · arXiv
  47. Mierzwa A. Shroff S. Rosenbluth J. Permeability of the paranodal junction of myelinated nerve fibers J. Neurosci.: The Off. J. Soc. Neurosci. 30 2010 15962 15968 PMC3001347 21106834
  48. Moore J.W. Joyner R.W. Brill M.H. Waxman S.D. Najar-Joa M. Simulations of conduction in uniform myelinated fibers. Relative sensitivity to changes in nodal and internodal parameters Biophys. J. 21 1978 147 160 https://www.sciencedirect.com/science/article/pii/S0006349578855155?via%3Dihubhttp://www.ncbi.nlm.nih.gov/pubmed/623863http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1473353 623863 PMC1473353
  49. Murray J.A. Blakemore W.F. The relationship between internodal length and fibre diameter in the spinal cord of the cat J. Neurol. Sci. 45 1980 29 41 https://linkinghub.elsevier.com/retrieve/pii/S0022510X80800049 7359166
  50. Nilsson M. Lasič S. Drobnjak I. Topgaard D. Westin C.F. Resolution limit of cylinder diameter estimation by diffusion MRI: the impact of gradient waveform and orientation dispersion NMR Biomed. 2017 e3711URL 10.1002/nbm.3711 PMC5485041 28318071 · DOI
  51. Novikov D.S. Fieremans E. Jespersen S.N. Kiselev V.G. Fieremans E. Quantifying brain microstructure with diffusion MRI: theory and parameter estimation NMR Biomed. 1 2018 e3998 http://arxiv.org/abs/1612.02059 http://ieeexplore.ieee.org/document/496485/ 10.1002/nbm.3998 https://arxiv.org/abs/1612.02059 PMC6481929 30321478 · DOI · Paper page
  52. Novikov D.S. Jensen J.H. Helpern J.A. Fieremans E. Revealing mesoscopic structural universality with diffusion Proc. Natl. Acad. Sci. 111 2014 5088 5093 http://www.pnas.org/cgi/doi/10.1073/pnas.1316944111 24706873 PMC3986157 · DOI
  53. Omi T. Shinomoto S. Can distributed delays perfectly stabilize dynamical networks? Phys. Rev. E - Stat. Nonlinear Soft Matter Phys. 77 2008 1 6 18517717
  54. Perge J.a. Niven J.E. Mugnaini E. Balasubramanian V. Sterling P. Why do axons differ in caliber? J. Neurosci. 32 2012 626 638 10.1523/?JNEUROSCI.4254-11.2012 22238098 PMC3571697 · DOI
  55. Richardson A.G. Mclntyre C.C. Grill W.M. Modelling the effects of electric fields on nerve fibres: influence of the myelin sheath Med. Biol. Eng. Comput. 38 2000 438 446 10984943
  56. Rudrapatna S.U. Parker G.D. Roberts J. Jones D.K. Can we correct for interactions between subject motion and gradient-nonlinearity in diffusion {MRI}? Proceedings of the International Society for Magnetic Resonance in Medicine 2018 1206
  57. Rushton W.A.H. A theory of the effects of fibre size in medullated nerve J. Physiol. 115 1951 101 122 10.1113/jphysiol.1951.sp004655 14889433 PMC1392008 · DOI
  58. Schüz A. Preil H. Basic connectivity of the cerebral cortex and some considerations on the corpus callosum Neurosci. Biobehav. Rev. 1996 8994195
  59. Schwarz G. Schwarz Gideon Estimating the dimension of a model Ann. Stat. 6 1978 461 464 https://www.jstor.org/stable/2958889http://projecteuclid.org/euclid.aos/1176344136
  60. Sepehrband F. Alexander D.C. Clark K.A. Kurniawan N.D. Yang Z. Reutens D.C. Parametric probability distribution functions for axon diameters of corpus callosum Front. Neuroanat. 10 2016 1 9 http://journal.frontiersin.org/Article/10.3389/fnana.2016.00059/abstract 27303273 PMC4880597 · DOI
  61. Sepehrband F. Alexander D.C. Kurniawan N.D. Reutens D.C. Yang Z. Towards higher sensitivity and stability of axon diameter estimation with diffusion-weighted MRI NMR Biomed. 29 2016 293 308 26748471 PMC4949708
  62. Sled J.G. Pike G.B. Quantitative imaging of magnetization transfer exchange and relaxation properties in vivo using MRI Magn. Reson. Med. 46 2001 923 931 10.1002/mrm.1278 11675644 · DOI
  63. Smith K.J. Blakemore W.F. Murray J.A. Patterson R.C. Internodal myelin volume and axon surface area. A relationship determining myelin thickness? J. Neurol. Sci. 55 1982 231 246 7131033
  64. Smith R. Koles Z. Myelinated nerve fibers: computed effect of myelin thickness on conduction velocity Am. J. Physiol. Legacy Content 219 1970 1256 1258 http://www.ncbi.nlm.nih.gov/pubmed/5473105http://www.physiology.org/doi/10.1152/ajplegacy.1970.219.5.1256 5473105 · DOI
  65. Smith S.M. Fast robust automated brain extraction Hum. Brain Mapp. 17 2002 143 155 http://www.ncbi.nlm.nih.gov/pubmed/12391568 12391568 PMC6871816
  66. Stikov N. Campbell J.S. Stroh T. Lavelée M. Frey S. Novek J. Nuara S. Ho M.K.K. Bedell B.J. Dougherty R.F. Leppert I.R. Boudreau M. Narayanan S. Duval T. Cohen-Adad J. Picard P.A.A. Gasecka A. Côté D. Pike G.B. In vivo histology of the myelin g-ratio with magnetic resonance imaging Neuroimage 118 2015 397 405 http://www.ncbi.nlm.nih.gov/pubmed/26004502 26004502
  67. Sugihara I. Lang E.J. Llinás R. Uniform olivocerebellar conduction time underlies Purkinje cell complex spike synchronicity in the rat cerebellum J. Physiol. 1993 PMC1143916 8308729
  68. Swadlow H. Rosene D. Waxman S. Characteristics of interhemispheric impulse conduction between prelunate gyri of the rhesus monkey Exp. Brain Res. 33 1978 http://link.springer.com/10.1007/BF00235567 103739 · DOI
  69. Tolhurst D.J. Lewis P.R. Effect of myelination on the conduction velocity of optic nerve fibres Ophthalmic Physiol. Opt. 12 1992 241 243 http://www.ncbi.nlm.nih.gov/pubmed/1408181 1408181
  70. Tomasi S. Caminiti R. Innocenti G.M. Areal differences in diameter and length of corticofugal projections Cerebr. Cortex 22 2012 1463 1472 https://academic.oup.com/cercor/article-lookup/doi/10.1093/cercor/bhs011 22302056 · DOI
  71. Tomassy G.S. Berger D.R. Chen H.H. Kasthuri N. Hayworth K.J. Vercelli A. Seung H.S. Lichtman J.W. Arlotta P. Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex Science 344 2014 319 324 http://www.ncbi.nlm.nih.gov/pubmed/24744380http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4122120http://www.sciencemag.org/cgi/doi/10.1126/science.1249766 24744380 PMC4122120 · DOI · Paper page
  72. Van Gelderen P. Des Pres D. Van Zijl P.C. Moonen C.T. Evaluation of restricted diffusion in cylinders. Phosphocreatine in rabbit leg muscle J. Magn. Reson., Ser. B 103 1994 255 260 8019777
  73. Veraart J. Fieremans E. Novikov D.S. On the scaling behavior of water diffusion in human brain white matter Neuroimage 185 2019 379 387 10.1016/j.neuroimage.2018.09.075 30292815 PMC6388620 · DOI · Paper page
  74. Vizoso A.D. Young J.Z. Internode length and fibre diameter in developing and regenerating nerves J. Anat. 82 1948 110 134 1 http://www.ncbi.nlm.nih.gov/pubmed/17105043http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1272963 PMC1272963 17105043
  75. Vos S.B. Tax C.M.W. Luijten P.R. Ourselin S. Leemans A. Froeling M. The importance of correcting for signal drift in diffusion MRI Magn. Reson. Med. 77 2017 285 299 10.1002/mrm.26124 26822700 · DOI
  76. Waxman S.G. Bennett M.V.L. Relative conduction velocities of small myelinated and non-myelinated fibres in the central nervous system Nat. New Biol. 235 1972 217 219 http://link.springer.com/10.1038/newbio238217a0 4506206 · DOI · Paper page
  77. West D.J. Teixeira R.P. Wood T.C. Hajnal J.V. Tournier J.D. Malik S.J. Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation Neuroimage 195 2019 78 88 10.1016/j.neuroimage.2019.03.049 30930311 PMC7100802 · DOI
  78. West K.L. Kelm N.D. Carson R.P. Gochberg D.F. Ess K.C. Does M.D. Myelin volume fraction imaging with MRI 10.1016/j.neuroimage.2016.12.067 2016 PMC5481512 28025129 · DOI
  79. Wood T.C. QUIT: QUantitative imaging tools J. Open Source Softw. 3 2018 656 http://joss.theoj.org/papers/10.21105/joss.00656 · DOI

Cited by

Sorted by citations

Authors
Drakesmith, M., Harms, R., Rudrapatna, S. U., Parker, G. D., Evans, C. J., & Jones, D. K.
Affiliations
  • Cardiff University (GB)
  • Maastricht University (NL)
Corresponding author
Drakesmith, Mark
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2019-12
Volume
203
Pages
116186
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2019.116186
PMC
PMC6854468
OpenAlex
W2974195983
Cited by
83
References
6
Citations by year
2026: 9 · 2025: 14 · 2024: 6 · 2023: 8 · 2022: 17 · 2021: 14 · 2020: 11 · 2019: 2 · 2018: 1
FWCI
7.27
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion, cc-by)
  • ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
  • Research Bank (Australian Catholic University) (submittedVersion, cc-by)
  • Research Publications (Maastricht University) (submittedVersion, other-oa)
Funders
Wellcome Trust
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
Keywords
axonal conduction velocity, action potential, axon diameter, g-ratio, relaxometry, sensitivity analysis, corpus callosum, in vivo estimation
MeSH terms
Action Potentials, Adult, Axons, Brain, Corpus Callosum, Female, Humans, Male, Models, Neurological, Neural Conduction, Diffusion Magnetic Resonance Imaging, Biophysical Phenomena, Young Adult

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