Analysis of White Matter Damage in Patients with Multiple Sclerosis via a Novel In Vivo MR Method for Measuring Myelin, Axons, and G-Ratio

References

1. 1.↵
   2. Polman CH,
   3. Reingold SC,
   4. Banwell B, et al

   . Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69:292–302 doi:10.1002/ana.22366 pmid:21387374

   CrossRefPubMedWeb of Science
2. 2.↵
   2. Filippi M,
   3. Rocca MA,
   4. De Stefano N, et al

   . Magnetic resonance techniques in multiple sclerosis: the present and the future. Arch Neurol 2011;68:1514–20 doi:10.1001/archneurol.2011.914 pmid:22159052

   CrossRefPubMed
3. 3.↵
   2. Yoshida M,
   3. Hori M,
   4. Yokoyama K, et al

   . Diffusional kurtosis imaging of normal-appearing white matter in multiple sclerosis: preliminary clinical experience. Jpn J Radiol 2013;31:50–55 doi:10.1007/s11604-012-0147-7 pmid:23086313

   CrossRefPubMed
4. 4.↵
   2. Guo AC,
   3. MacFall JR,
   4. Provenzale JM

   . Multiple sclerosis: diffusion tensor MR imaging for evaluation of normal-appearing white matter. Radiology 2002;222:729–36 doi:10.1148/radiol.2223010311 pmid:11867792

   CrossRefPubMedWeb of Science
5. 5.↵
   2. Hori M,
   3. Yoshida M,
   4. Yokoyama K, et al

   . Multiple sclerosis: benefits of q-space imaging in evaluation of normal-appearing and periplaque white matter. Magn Reson Imaging 2014;32:625–29 doi:10.1016/j.mri.2014.02.024 pmid:24726649

   CrossRefPubMed
6. 6.↵
   2. Warntjes JB,
   3. Leinhard OD,
   4. West J, et al

   . Rapid magnetic resonance quantification on the brain: optimization for clinical usage. Magn Reson Med 2008;60:320–29 doi:10.1002/mrm.21635 pmid:18666127

   CrossRefPubMed
7. 7.↵
   2. Hagiwara A,
   3. Warntjes M,
   4. Hori M, et al

   . SyMRI of the brain: rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement. Invest Radiol 2017 Mar 3. [Epub ahead of print] doi:10.1097/RLI.0000000000000365 pmid:28257339

   CrossRefPubMed
8. 8.↵
   2. Warntjes M,
   3. Engström M,
   4. Tisell A, et al

   . Modeling the presence of myelin and edema in the brain based on multi-parametric quantitative MRI. Front Neurol 2016;7:16 doi:10.3389/fneur.2016.00016 pmid:26925030

   CrossRefPubMed
9. 9.↵
   2. Warntjes JBM,
   3. Persson A,
   4. Berge J, et al

   . Myelin detection using rapid quantitative MR imaging correlated to macroscopically registered luxol fast blue–stained brain specimens. AJNR Am J Neuroradiol 2017;38:1096–1102 doi:10.3174/ajnr.A5168 pmid:28428209

   Abstract/FREE Full Text
10. 10.↵
    2. Hagiwara A,
    3. Hori M,
    4. Yokoyama K, et al

    . Utility of a multiparametric quantitative MRI model that assesses myelin and edema for evaluating plaques, periplaque white matter, and normal-appearing white matter in patients with multiple sclerosis: a feasibility study. AJNR Am J Neuroradiol 2017;38:237–42 doi:10.3174/ajnr.A4977 pmid:27789453

    Abstract/FREE Full Text
11. 11.↵
    2. Zhang H,
    3. Schneider T,
    4. Wheeler-Kingshott CA, et al

    . NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012;61:1000–16 doi:10.1016/j.neuroimage.2012.03.072 pmid:22484410

    CrossRefPubMed
12. 12.↵
    2. Stikov N,
    3. Campbell JS,
    4. Stroh T, et al

    . In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage 2015;118:397–405 doi:10.1016/j.neuroimage.2015.05.023 pmid:26004502

    CrossRefPubMed
13. 13.↵
    2. Campbell JS,
    3. Leppert IR,
    4. Narayanan S, et al

    . Promise and pitfalls of g-ratio estimation with MRI. arXiv:1701.02760v1 [physics.med-ph]. January 10, 2017. https://arxiv.org/abs/1701.02760v1. Accessed July 2, 2017.
14. 14.↵
    2. Rushton WA

    . A theory of the effects of fibre size in medullated nerve. J Physiol 1951;115:101–22 doi:10.1113/jphysiol.1951.sp004655 pmid:14889433

    CrossRefPubMedWeb of Science
15. 15.↵
    2. Cercignani M,
    3. Giulietti G,
    4. Dowell NG, et al

    . Characterizing axonal myelination within the healthy population: a tract-by-tract mapping of effects of age and gender on the fiber g-ratio. Neurobiol Aging 2017;49:109–18 doi:10.1016/j.neurobiolaging.2016.09.016 pmid:27792897

    CrossRefPubMed
16. 16.↵
    2. Dean DC 3rd.,
    3. O'Muircheartaigh J,
    4. Dirks H, et al

    . Mapping an index of the myelin g-ratio in infants using magnetic resonance imaging. Neuroimage 2016;132:225–37 doi:10.1016/j.neuroimage.2016.02.040 pmid:26908314

    CrossRefPubMed
17. 17.↵
    2. Melbourne A,
    3. Eaton-Rosen Z,
    4. Orasanu E, et al

    . Longitudinal development in the preterm thalamus and posterior white matter: MRI correlations between diffusion weighted imaging and T2 relaxometry. Hum Brain Mapp 2016;37:2479–92 doi:10.1002/hbm.23188 pmid:26996400

    CrossRefPubMed
18. 18.↵
    2. Mohammadi S,
    3. Carey D,
    4. Dick F, et al

    . Whole-brain in-vivo measurements of the axonal g-ratio in a group of 37 healthy volunteers. Front Neurosci 2015;9:441 pmid:26640427

    PubMed
19. 19.↵
    2. Albert M,
    3. Antel J,
    4. Brück W, et al

    . Extensive cortical remyelination in patients with chronic multiple sclerosis. Brain Pathol 2007;17:129–38 doi:10.1111/j.1750-3639.2006.00043.x pmid:17388943

    CrossRefPubMedWeb of Science
20. 20.↵
    2. McDonald WI,
    3. Compston A,
    4. Edan G, et al

    . Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–27 doi:10.1002/ana.1032 pmid:11456302

    CrossRefPubMedWeb of Science
21. 21.↵
    2. Polman CH,
    3. Reingold SC,
    4. Edan G, et al

    . Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria.” Ann Neurol 2005;58:840–46 doi:10.1002/ana.20703 pmid:16283615

    CrossRefPubMedWeb of Science
22. 22.↵
    2. Kurtzke JF

    . A new scale for evaluating disability in multiple sclerosis. Neurology 1955;5:580–83 doi:10.1212/WNL.5.8.580 pmid:13244774

    FREE Full Text
23. 23.↵
    2. Mohammadi S,
    3. Möller HE,
    4. Kugel H, et al

    . Correcting eddy current and motion effects by affine whole-brain registrations: evaluation of three-dimensional distortions and comparison with slicewise correction. Magn Reson Med 2010;64:1047–56 doi:10.1002/mrm.22501 pmid:20574966

    CrossRefPubMed
24. 24.↵
    2. Becker SM,
    3. Tabelow K,
    4. Voss HU, et al

    . Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS). Med Image Anal 2012;16:1142–55 doi:10.1016/j.media.2012.05.007 pmid:22677817

    CrossRefPubMed
25. 25.↵
    2. Becker SM,
    3. Tabelow K,
    4. Mohammadi S, et al

    . Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS. Neuroimage 2014;95:90–105 doi:10.1016/j.neuroimage.2014.03.053 pmid:24680711

    CrossRefPubMed
26. 26.↵
    2. Daducci A,
    3. Canales-Rodríguez EJ,
    4. Zhang H, et al

    . Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. Neuroimage 2015;105:32–44 doi:10.1016/j.neuroimage.2014.10.026 pmid:25462697

    CrossRefPubMed
27. 27.↵
    2. Levesque IR,
    3. Pike GB

    . Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T(2) relaxometry: a unified view via a four-pool model. Magn Reson Med 2009;62:1487–96 doi:10.1002/mrm.22131 pmid:19859946

    CrossRefPubMed
28. 28.↵
    2. Lucchinetti CF,
    3. Brück W,
    4. Rodriguez M, et al

    . Distinct patterns of multiple sclerosis pathology indicates heterogeneity on pathogenesis. Brain Pathol 1996;6:259–74 doi:10.1111/j.1750-3639.1996.tb00854.x pmid:8864283

    CrossRefPubMedWeb of Science
29. 29.↵
    2. Dziedzic T,
    3. Metz I,
    4. Dallenga T, et al

    . Wallerian degeneration: a major component of early axonal pathology in multiple sclerosis. Brain Pathol 2010;20:976–85 pmid:20477831

    CrossRefPubMedWeb of Science
30. 30.↵
    2. Simons M,
    3. Misgeld T,
    4. Kerschensteiner M

    . A unified cell biological perspective on axon-myelin injury. J Cell Biol 2014;206:335–45 doi:10.1083/jcb.201404154 pmid:25092654

    Abstract/FREE Full Text
31. 31.↵
    2. Haines JD,
    3. Inglese M,
    4. Casaccia P

    . Axonal damage in multiple sclerosis. Mt Sinai J Med 2011;78:231–43 doi:10.1002/msj.20246 pmid:21425267

    CrossRefPubMed
32. 32.↵
    2. Bjartmar C,
    3. Kidd G,
    4. Mörk S, et al

    . Neurological disability correlates with spinal cord axonal loss and reduced N-acetyl aspartate in chronic multiple sclerosis patients. Ann Neurol 2000;48:893–901 pmid:11117546

    CrossRefPubMedWeb of Science
33. 33.↵
    2. Graf von Keyserlingk D,
    3. Schramm U

    . Diameter of axons and thickness of myelin sheaths of the pyramidal tract fibres in the adult human medullary pyramid. Anat Anz 1984;157:97–111 pmid:6507887

    PubMed
34. 34.↵
    2. West KL,
    3. Kelm ND,
    4. Carson RP, et al

    . Myelin volume fraction imaging with MRI. Neuroimage 2016 Dec 23. [Epub ahead of print] doi:10.1016/j.neuroimage.2016.12.067 pmid:28025129

    CrossRefPubMed
35. 35.↵
    2. Lampron A,
    3. Larochelle A,
    4. Laflamme N, et al

    . Inefficient clearance of myelin debris by microglia impairs remyelinating processes. J Exp Med 2015;212:481–95 doi:10.1084/jem.20141656 pmid:25779633

    Abstract/FREE Full Text
36. 36.↵
    2. Liu F,
    3. Block WF,
    4. Kijowski R, et al

    . Rapid multicomponent relaxometry in steady state with correction of magnetization transfer effects. Magn Reson Med 2016;75:1423–33 doi:10.1002/mrm.25672 pmid:25959974

    CrossRefPubMed
37. 37.↵
    2. Mossahebi P,
    3. Alexander AL,
    4. Field AS, et al

    . Removal of cerebrospinal fluid partial volume effects in quantitative magnetization transfer imaging using a three-pool model with nonexchanging water component. Magn Reson Med 2015;74:1317–26 doi:10.1002/mrm.25516 pmid:25394181

    CrossRefPubMed
38. 38.↵
    2. Hagiwara A,
    3. Nakazawa M,
    4. Andica C, et al

    . Dural enhancement in a patient with Sturge-Weber syndrome revealed by double inversion recovery contrast using synthetic MRI. Magn Reson Med Sci 2016;15:151–52 doi:10.2463/mrms.ci.2015-0066 pmid:26549162

    CrossRefPubMed
39. 39.↵
    2. Hagiwara A,
    3. Hori M,
    4. Suzuki M, et al

    . Contrast-enhanced synthetic MRI for the detection of brain metastases. Acta Radiol Open 2016;5:2058460115626757 doi:10.1177/2058460115626757 pmid:26962461

    CrossRefPubMed
40. 40.↵
    2. Andica C,
    3. Hagiwara A,
    4. Nakazawa M, et al

    . The advantage of synthetic MRI for the visualization of early white matter change in an infant with Sturge-Weber syndrome. Magn Reson Med Sci 2016;15:347–48 doi:10.2463/mrms.ci.2015-0164 pmid:27001386

    CrossRefPubMed
41. 41.↵
    2. Granberg T,
    3. Uppman M,
    4. Hashim F, et al

    . Clinical feasibility of synthetic MRI in multiple sclerosis: a diagnostic and volumetric validation study. AJNR Am J Neuroradiol 2016;37:1023–29 doi:10.3174/ajnr.A4665 pmid:26797137

    Abstract/FREE Full Text
42. 42.↵
    2. Hagiwara A,
    3. Hori M,
    4. Yokoyama K, et al

    . Synthetic MRI in the detection of multiple sclerosis plaques. AJNR Am J Neuroradiol 2017;38:257–63 doi:10.3174/ajnr.A5012 pmid:27932506

    Abstract/FREE Full Text