Detection of axonal degeneration in a mouse model of Huntington’s disease: comparison between diffusion tensor imaging and anomalous diffusion metrics

  • Rodolfo G. Gatto
  • Allen Q. Ye
  • Luis Colon-Perez
  • Thomas H. Mareci
  • Anna Lysakowski
  • Steven D. Price
  • Scott T. Brady
  • Muge Karaman
  • Gerardo Morfini
  • Richard L. MaginEmail author
Research Article



The goal of this work is to study the changes in white matter integrity in R6/2, a well-established animal model of Huntington’s disease (HD) that are captured by ex vivo diffusion imaging (DTI) using a high field MRI (17.6 T).

Materials and methods

DTI and continuous time random walk (CTRW) models were used to fit changes in the diffusion-weighted signal intensity in the corpus callosum of controls and in R6/2 mice.


A significant 13% decrease in fractional anisotropy, a 7% increase in axial diffusion, and a 33% increase in radial diffusion were observed between R6/2 and control mice. No change was observed in the CTRW beta parameter, but a significant decrease in the alpha parameter (− 21%) was measured. Histological analysis of the corpus callosum showed a decrease in axonal organization, myelin alterations, and astrogliosis. Electron microscopy studies demonstrated ultrastructural changes in degenerating axons, such as an increase in tortuosity in the R6/2 mice.


DTI and CTRW diffusion models display quantitative changes associated with the microstructural alterations observed in the corpus callosum of the R6/2 mice. The observed increase in the diffusivity and decrease in the alpha CTRW parameter providing support for the use of these diffusion models for non-invasive detection of white matter alterations in HD.


Magnetic resonance imaging Diffusion tensor imaging Anomalous diffusion Huntington disease Mice 



This work was support provided by grants from the National Center for Advancing Translational Science grant (NCATS TLTR000049 to AY), NIH DC02058 (to AL), CHDI (#A-11872; to GM and SB), NIH R21NS096642 (to GM). A portion of this work was performed in the McKnight Brain Institute at the National High Magnetic Field Laboratory’s AMRIS Facility, which is supported by National Science Foundation Cooperative Agreement no. DMR-1157490 and the State of Florida. In addition, we would like to thank Mr. Dan Plant for his input and assistance in the design and experimental set up for this project, as well as Dr. Carson Ingo for his assistance in fitting the data to the Mittag–Leffler function.

Compliance with ethical standards

Conflict of interest

Authors of this manuscript have no conflicts of interest to report.

Ethical approval

Experiments were performed under protocols approved by the Animal Care Committee at the University of illinois in Chicago.

Statement on the welfare of animals

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Supplementary material

10334_2019_742_MOESM1_ESM.pdf (1.4 mb)
Supplementary material 1 (PDF 1421 kb)


  1. 1.
    Harper PS (1992) The epidemiology of Huntington’s disease. Hum Genet 89(4):365–376Google Scholar
  2. 2.
    Pringsheim T, Wiltshire K, Day L, Dykeman J, Steeves T, Jette N (2012) The incidence and prevalence of Huntington’s disease: a systematic review and meta-analysis. Mov Disord 27(9):1083–1091Google Scholar
  3. 3.
    Walker FO (2007) Huntington’s disease. Lancet 369(9557):218–228Google Scholar
  4. 4.
    Han I, You Y, Kordower JH, Brady ST, Morfini GA (2010) Differential vulnerability of neurons in Huntington’s disease: the role of cell type-specific features. J Neurochem 113(5):1073–1091Google Scholar
  5. 5.
    Perez-Navarro E, Canals JM, Gines S, Alberch J (2006) Cellular and molecular mechanisms involved in the selective vulnerability of striatal projection neurons in Huntington’s disease. Histol Histopathol 21(11):1217–1232Google Scholar
  6. 6.
    Pouladi MA, Morton AJ, Hayden MR (2013) Choosing an animal model for the study of Huntington’s disease. Nat Rev Neurosci 14(10):708–721Google Scholar
  7. 7.
    Mangiarini L, Sathasivam K, Seller M, Cozens B, Harper A, Hetherington C, Lawton M, Trottier Y, Lehrach H, Davies SW, Bates GP (1996) Exon 1 of the HD gene with an expanded CAG repeat is sufficient to cause a progressive neurological phenotype in transgenic mice. Cell 87(3):493–506Google Scholar
  8. 8.
    Menalled L, El-Khodor BF, Patry M, Suarez-Farinas M, Orenstein SJ, Zahasky B, Leahy C, Wheeler V, Yang XW, MacDonald M, Morton AJ, Bates G, Leeds J, Park L, Howland D, Signer E, Tobin A, Brunner D (2009) Systematic behavioral evaluation of Huntington’s disease transgenic and knock-in mouse models. Neurobiol Dis 35(3):319–336Google Scholar
  9. 9.
    Ramaswamy S, McBride JL, Kordower JH (2007) Animal models of Huntington’s disease. ILAR J 48(4):356–373Google Scholar
  10. 10.
    Gatto RG, Chu Y, Ye AQ, Price SD, Tavassoli E, Buenaventura A, Brady ST, Magin RL, Kordower JH, Morfini GA (2015) Analysis of YFP(J16)-R6/2 reporter mice and postmortem brains reveals early pathology and increased vulnerability of callosal axons in Huntington’s disease. Hum Mol Genet 24(18):5285–5298Google Scholar
  11. 11.
    Rosas HD, Lee SY, Bender AC, Zaleta AK, Vangel M, Yu P, Fischl B, Pappu V, Onorato C, Cha JH, Salat DH, Hersch SM (2010) Altered white matter microstructure in the corpus callosum in Huntington’s disease: implications for cortical “disconnection”. NeuroImage 49(4):2995–3004Google Scholar
  12. 12.
    Poudel GR, Stout JC, Dominguez DJ, Churchyard A, Chua P, Egan GF, Georgiou-Karistianis N (2015) Longitudinal change in white matter microstructure in Huntington’s disease: the IMAGE-HD study. Neurobiol Dis 74:406–412Google Scholar
  13. 13.
    Poudel GR, Stout JC, Dominguez DJ, Salmon L, Churchyard A, Chua P, Georgiou-Karistianis N, Egan GF (2014) White matter connectivity reflects clinical and cognitive status in Huntington’s disease. Neurobiol Dis 65:180–187Google Scholar
  14. 14.
    Rosas HD, Tuch DS, Hevelone ND, Zaleta AK, Vangel M, Hersch SM, Salat DH (2006) Diffusion tensor imaging in presymptomatic and early Huntington’s disease: selective white matter pathology and its relationship to clinical measures. Mov Disord 21(9):1317–1325Google Scholar
  15. 15.
    Le Bihan D (2013) Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 268(2):318–322Google Scholar
  16. 16.
    Tabesh A, Jensen JH, Ardekani BA, Helpern JA (2011) Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 65(3):823–836Google Scholar
  17. 17.
    Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4):1000–1016Google Scholar
  18. 18.
    Ingo C, Magin RL, Colon-Perez L, Triplett W, Mareci TH (2014) On random walks and entropy in diffusion-weighted magnetic resonance imaging studies of neural tissue. Magn Reson Med 71(2):617–627Google Scholar
  19. 19.
    Yu Q, Reutens D, O’Brien K, Vegh V (2016) Tissue microstructure features derived from anomalous diffusion measurements in magnetic resonance imaging. Hum Brain Mapp 38(2):1068–1081Google Scholar
  20. 20.
    Karaman MM, Sui Y, Wang H, Magin RL, Li Y, Zhou XJ (2016) Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values. Magn Reson Med 76(4):1149–1157Google Scholar
  21. 21.
    Xu B, Su L, Wang Z, Fan Y, Gong G, Zhu W, Gao P, Gao JH (2017) Anomalous diffusion in cerebral glioma assessed using a fractional motion model. Magn Reson Med 78(5):1944–1949Google Scholar
  22. 22.
    Karaman MM, Wang H, Sui Y, Engelhard HH, Li Y, Zhou XJ (2016) A fractional motion diffusion model for grading pediatric brain tumors. NeuroImage Clinical 12:707–714Google Scholar
  23. 23.
    Gatto RG, Li W, Magin RL (2018) Diffusion tensor imaging identifies presymptomatic axonal degeneration in the spinal cord of ALS mice. Brain Res 1679:7Google Scholar
  24. 24.
    Alexander AL, Lee JE, Lazar M, Field AS (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4(3):316–329Google Scholar
  25. 25.
    Gatto RG (2018) Diffusion tensor imaging as a tool to detect presymptomatic axonal degeneration in a preclinical spinal cord model of amyotrophic lateral sclerosis. Neural Regen Res 13(3):425–426Google Scholar
  26. 26.
    Metzler R, Klafter J (2000) The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys Rep 339:1–77Google Scholar
  27. 27.
    Magin RL, Ingo C, Colon-Perez L, Triplett W, Mareci TH (2013) Characterization of anomalous diffusion in porous biological tissues using fractional order derivatives and entropy. Microporous Mesoporous Mater 178:39–43Google Scholar
  28. 28.
    Klages R, Radons G, Sokolov IM (eds) (2012) Anomalous transport: foundations and applications. Wiley, WeinheimGoogle Scholar
  29. 29.
    Magin RL, Abdullah O, Baleanu D, Zhou XJ (2008) Anomalous diffusion expressed through fractional order differential operators in the Bloch–Torrey equation. J Magnetic Resonance 190(2):255–270Google Scholar
  30. 30.
    Gorenflo RMF, Moretti D, Pagnini G, Paradisi P (2002) Fractional diffusion: probability distributions and random walk models. Phys A 305(1–2):106–112Google Scholar
  31. 31.
    Wen CSH, Zhanga X, Korosak D (2010) Anomalous diffusion modeling by fractal and fractional derivatives. Comput Math Appl 59(5):1754–1758Google Scholar
  32. 32.
    Lenzi EK, Ribeiro HV, Tateishi AA, Zola RS, Evangelista LR (2016) Anomalous diffusion and transport in heterogeneous systems separated by a membrane. Proc Math Phys Eng Sci 472(2195):20160502Google Scholar
  33. 33.
    Klafter J, Lim SC, Metzler R (eds) (2011) Fractional dynamics: recent advances. World Scientific, SinaporeGoogle Scholar
  34. 34.
    Mainardi F (2000) Fractional calculus and waves in linear viscoelasticity: an introduction to mathematical models. Imperial College Press, LondonGoogle Scholar
  35. 35.
    Ingo C, Magin RL, Parrish TB (2014) New insights into the fractional order diffusion equation using entropy and kurtosis. Entropy 16(11):5838–5852Google Scholar
  36. 36.
    Ingo C, Barrick TR, Webb AG, Ronen I (2017) Accurate Padé global approximations for the Mittag–Leffler function, its inverse, and its partial derivatives to efficiently compute convergent power series. Int J Appl Comput Mat 3:347–362Google Scholar
  37. 37.
    Mount SL, Schwarz JE, Taatjes DJ (1997) Prolonged storage of fixative for electron microscopy: effects on tissue preservation for diagnostic specimens. Ultrastruct Pathol 21(2):195–200Google Scholar
  38. 38.
    Karnovsky MJ (1965) A formaldehyde-glutaraldehyde fixative of high osmolality for use in electron microscopy. J Cell Biol 27:137A–138AGoogle Scholar
  39. 39.
    Vranceanu F, Perkins GA, Terada M, Chidavaenzi RL, Ellisman MH, Lysakowski A (2012) Striated organelle, a cytoskeletal structure positioned to modulate hair-cell transduction. Proc Natl Acad Sci USA 109(12):4473–4478Google Scholar
  40. 40.
    Shaffer JJ, Ghayoor A, Long JD, Kim RE, Lourens S, O’Donnell LJ, Westin CF, Rathi Y, Magnotta V, Paulsen JS, Johnson HJ (2017) Longitudinal diffusion changes in prodromal and early HD: evidence of white-matter tract deterioration. Hum Brain Mapp 38(3):1460–1477Google Scholar
  41. 41.
    Phillips O, Squitieri F, Sanchez-Castaneda C, Elifani F, Caltagirone C, Sabatini U, Di Paola M (2014) Deep white matter in Huntington’s disease. PLoS One 9(10):e109676Google Scholar
  42. 42.
    Phillips OR, Joshi SH, Squitieri F, Sanchez-Castaneda C, Narr K, Shattuck DW, Caltagirone C, Sabatini U, DiPaola M (2016) Major superficial white matter abnormalities in Huntington’s disease. Front Neurosci 10:197Google Scholar
  43. 43.
    Symms M, Jager HR, Schmierer K, Yousry TA (2004) A review of structural magnetic resonance neuroimaging. J Neurol Neurosurg Psychiat 75(9):1235–1244Google Scholar
  44. 44.
    Höfling F, Franosch T (2013) Anomalous transport in the crowded world of biological cells. Rep Prog Phys 76:046602Google Scholar
  45. 45.
    Schumer R, Meerschaert MM, Baeumer B (2009) Fractional advection–dispersion equations for modeling transport at the Earth surface. J Geophys Res 114:F00A07Google Scholar
  46. 46.
    Morton AJ, Glynn D, Leavens W, Zheng Z, Faull RL, Skepper JN, Wight JM (2009) Paradoxical delay in the onset of disease caused by super-long CAG repeat expansions in R6/2 mice. Neurobiol Dis 33(3):331–341Google Scholar
  47. 47.
    Li H, Li SH, Yu ZX, Shelbourne P, Li XJ (2001) Huntingtin aggregate-associated axonal degeneration is an early pathological event in Huntington’s disease mice. J Neurosci 21(21):8473–8481Google Scholar
  48. 48.
    Liu W, Yang J, Burgunder J, Cheng B, Shang H (2016) Diffusion imaging studies of Huntington’s disease: a meta-analysis. Park Relat Disord 32:94–101Google Scholar
  49. 49.
    Matsui JT, Vaidya JG, Johnson HJ, Magnotta VA, Long JD, Mills JA, Lowe MJ, Sakaie KE, Rao SM, Smith MM, Paulsen JS (2014) Diffusion weighted imaging of prefrontal cortex in prodromal Huntington’s disease. Hum Brain Mapp 35(4):1562–1573Google Scholar
  50. 51.
    Van Camp N, Blockx I, Camon L, de Vera N, Verhoye M, Veraart J, Van Hecke W, Martinez E, Soria G, Sijbers J, Planas AM, Van der Linden A (2012) A complementary diffusion tensor imaging (DTI)-histological study in a model of Huntington’s disease. Neurobiol Aging 33(5):945–959Google Scholar
  51. 51.
    Phillips O, Sanchez-Castaneda C, Elifani F, Maglione V, Di Pardo A, Caltagirone C, Squitieri F, Sabatini U, Di Paola M (2013) Tractography of the corpus callosum in Huntington’s disease. PLoS One 8(9):e73280Google Scholar
  52. 52.
    Mori S, Crain BJ, Chacko VP, van Zijl PC (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45(2):265–269Google Scholar
  53. 53.
    Weaver KE, Richards TL, Liang O, Laurino MY, Samii A, Aylward EH (2009) Longitudinal diffusion tensor imaging in Huntington’s Disease. Exp Neurol 216(2):525–529Google Scholar
  54. 54.
    Porrero C, Rubio-Garrido P, Avendano C, Clasca F (2010) Mapping of fluorescent protein-expressing neurons and axon pathways in adult and developing Thy1-eYFP-H transgenic mice. Brain Res 1345:59–72Google Scholar
  55. 55.
    Chan CS, Surmeier DJ (2014) Astrocytes go awry in Huntington’s disease. Nat Neurosci 17(5):641–642Google Scholar
  56. 56.
    Huang B, Wei W, Wang G, Gaertig MA, Feng Y, Wang W, Li XJ, Li S (2015) Mutant huntingtin downregulates myelin regulatory factor-mediated myelin gene expression and affects mature oligodendrocytes. Neuron 85(6):1212–1226Google Scholar
  57. 57.
    Bartzokis G, Lu PH, Tishler TA, Fong SM, Oluwadara B, Finn JP, Huang D, Bordelon Y, Mintz J, Perlman S (2007) Myelin breakdown and iron changes in Huntington’s disease: pathogenesis and treatment implications. Neurochem Res 32(10):1655–1664Google Scholar
  58. 58.
    Crawford HE, Hobbs NZ, Keogh R, Langbehn DR, Frost C, Johnson H, Landwehrmeyer B, Reilmann R, Craufurd D, Stout JC, Durr A, Leavitt BR, Roos RA, Tabrizi SJ, Scahill RI (2013) Corpus callosal atrophy in premanifest and early Huntington’s disease. J Huntingt Dis 2(4):517–526Google Scholar
  59. 59.
    Gomez-Tortosa E, MacDonald ME, Friend JC, Taylor SA, Weiler LJ, Cupples LA, Srinidhi J, Gusella JF, Bird ED, Vonsattel JP, Myers RH (2001) Quantitative neuropathological changes in presymptomatic Huntington’s disease. Ann Neurol 49(1):29–34Google Scholar
  60. 60.
    Chilla GS, Tan CH, Xu C, Poh CL (2015) Diffusion weighted magnetic resonance imaging and its recent trend-a survey. Quant Imag Med Surg 5(3):407–422Google Scholar
  61. 61.
    Zhang J, Jones MV, McMahon MT, Mori S, Calabresi PA (2012) In vivo and ex vivo diffusion tensor imaging of cuprizone-induced demyelination in the mouse corpus callosum. Magn Reson Med 67:750–759Google Scholar
  62. 62.
    Ryutaro Y, Junichi H, Yoshifumi A, Fumiko S, Keitaro Y, Yuji K, Hideyuki O, Kenji FT (2018) Quantitative temporal changes in DTI values coupled with histological properties in cuprizone-induced demyelination and remyelination. Neurochem Intern 119:151–158Google Scholar
  63. 63.
    Liang Y, Ye AQ, Chen W, Gatto RG, Colon-Perez L, Mareci TH, Magin RL (2016) A fractal derivative model for the characterization of anomalous diffusion in magnetic resonance imaging. Commun Nonlinear Sci Numer Simul 39:529–537Google Scholar
  64. 64.
    Ingo C, Sui Y, Chen Y, Parrish TB, Webb AG, Ronen I (2015) Parsimonious continuous time random walk models and kurtosis for diffusion in magnetic resonance of biological tissue. Front Phys 3:11Google Scholar
  65. 65.
    Hrabe J, Hrabetova S, Segeth K (2004) A model of effective diffusion and tortuosity in the extracellular space of the brain. Biophys J 87(3):1606–1617Google Scholar
  66. 66.
    Sui Y, Wang H, Liu G, Damen FW, Wanamaker C, Li Y, Zhou XJ (2015) Differentiation of low- and high-grade pediatric brain tumors with high b-value diffusion-weighted MR imaging and a fractional order calculus model. Radiology 277(2):489–496Google Scholar

Copyright information

© European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2019

Authors and Affiliations

  • Rodolfo G. Gatto
    • 1
    • 2
  • Allen Q. Ye
    • 2
  • Luis Colon-Perez
    • 3
    • 4
  • Thomas H. Mareci
    • 4
  • Anna Lysakowski
    • 1
  • Steven D. Price
    • 1
  • Scott T. Brady
    • 1
  • Muge Karaman
    • 2
    • 5
  • Gerardo Morfini
    • 1
  • Richard L. Magin
    • 2
    Email author
  1. 1.Department of Anatomy and Cell BiologyUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Department of BioengineeringUniversity of Illinois at ChicagoChicagoUSA
  3. 3.Department of Neurology and BehaviorUniversity of California at IrvineIrvineUSA
  4. 4.Department of Biochemistry and Molecular BiologyUniversity of FloridaGainesvilleUSA
  5. 5.Center for MR ResearchUniversity of Illinois at ChicagoChicagoUSA

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