Magnetic Resonance Imaging in Huntington’s Disease

  • Sarah GregoryEmail author
  • Rachael I. Scahill
  • Geraint Rees
  • Sarah Tabrizi
Part of the Methods in Molecular Biology book series (MIMB, volume 1780)


Magnetic resonance imaging (MRI) is a noninvasive technique used routinely to image the body in both clinical and research settings. Through the manipulation of radio waves and static field gradients, MRI uses the principle of nuclear magnetic resonance to produce images with high spatial resolution, appropriate for the investigation of brain structure and function.


Magnetic resonance imaging Clinical studies Structural MRI Diffusion Weighted Imaging Functional MRI Data analysis 



The authors would like to thank Eileanoir Johnson for her suggestions and comments.


  1. 1.
    Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11:805–821CrossRefPubMedGoogle Scholar
  2. 2.
    Ashburner J, Friston KJ (2001) Why voxel-based morphometry should be used. Neuroimage 14:1238–1243CrossRefPubMedGoogle Scholar
  3. 3.
    Morey RA, Petty CM, Xu Y et al (2009) A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes. Neuroimage 45:855–866CrossRefPubMedGoogle Scholar
  4. 4.
    Aylward EH, Codori AM, Rosenblatt A et al (2000) Rate of caudate atrophy in presymptomatic and symptomatic stages of Huntington’s disease. Mov Disord 15:552–560CrossRefPubMedGoogle Scholar
  5. 5.
    Aylward EH, Nopoulos PC, Ross CA et al (2011) Longitudinal change in regional brain volumes in prodromal Huntington disease. J Neurol Neurosurg Psychiatry 82:405–410CrossRefPubMedGoogle Scholar
  6. 6.
    Georgiou-Karistianis N, Scahill R et al (2013) Structural MRI in Huntington’s disease and recommendations for its potential use in clinical trials. Neurosci Biobehav Rev 37:480–490CrossRefPubMedGoogle Scholar
  7. 7.
    Paulsen JS, Nopoulos PC, Aylward E et al (2010) Striatal and white matter predictors of estimated diagnosis for Huntington disease. Brain Res Bull 82:201–207CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Tabrizi SJ, Langbehn DR, Leavitt BR et al (2009) Biological and clinical manifestations of Huntington’s disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data. Lancet Neurol 8:791–801CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Tabrizi SJ, Reilmann R, Roos RA et al (2012) Potential endpoints for clinical trials in premanifest and early Huntington’s disease in the TRACK-HD study: analysis of 24 month observational data. Lancet Neurol 11:42–53CrossRefPubMedGoogle Scholar
  10. 10.
    Tabrizi SJ, Scahill RI, Durr A et al (2011) Biological and clinical changes in premanifest and early stage Huntington’s disease in the TRACK-HD study: the 12-month longitudinal analysis. Lancet Neurol 10:31–42CrossRefPubMedGoogle Scholar
  11. 11.
    Tabrizi SJ, Scahill RI, Owen G et al (2013) Predictors of phenotypic progression and disease onset in premanifest and early-stage Huntington’s disease in the TRACK-HD study: analysis of 36-month observational data. Lancet Neurol 11:42–53CrossRefGoogle Scholar
  12. 12.
    Rosas HD, Reuter M, Doros G et al (2011) A tale of two factors: what determines the rate of progression in Huntington’s disease? A longitudinal MRI study. Mov Disord 26:1691–1697CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Johnson EB, Rees EM, Labuschagne I et al (2015) The impact of occipital lobe cortical thickness on cognitive task performance: an investigation in Huntington’s disease. Neuropsychologia 79:138–146CrossRefPubMedGoogle Scholar
  14. 14.
    Jones DK (2008) Studying connections in the living human brain with diffusion MRI. Cortex 44:936–952. CrossRefPubMedGoogle Scholar
  15. 15.
    Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254CrossRefPubMedGoogle Scholar
  16. 16.
    Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system – a technical review. NMR Biomed 15:435–455CrossRefPubMedGoogle Scholar
  17. 17.
    Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66:259–267CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Smith SM, Jenkinson M, Johansen-Berg H et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31:1487–1505CrossRefPubMedGoogle Scholar
  19. 19.
    Conturo TE, Lori NF, Cull TS et al (1999) Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci U S A 96:10422–10427CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Bullmore ET, Bassett DS (2011) Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol 7:113–140CrossRefPubMedGoogle Scholar
  21. 21.
    Sporns O (2011) The human connectome: a complex network. Ann N Y Acad Sci 1224:109–125CrossRefPubMedGoogle Scholar
  22. 22.
    Sporns O, Tononi G, Kotter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Bohanna I, Georgiou-Karistianis N, Hannan AJ, Egan GF (2008) Magnetic resonance imaging as an approach towards identifying neuropathological biomarkers for Huntington’s disease. Brain Res Rev 58(1):209–225CrossRefPubMedGoogle Scholar
  24. 24.
    Della Nave R, Ginestroni A, Tessa C et al (2010) Regional distribution and clinical correlates of white matter structural damage in Huntington disease: a tract-based spatial statistics study. Am J Neuroradiol 31:1675–1681CrossRefPubMedGoogle Scholar
  25. 25.
    Douaud G, Behrens TE, Poupon C et al (2009) In vivo evidence for the selective subcortical degeneration in Huntington’s disease. Neuroimage 46:958–966CrossRefPubMedGoogle Scholar
  26. 26.
    Dumas EM, van den Bogaard SJ et al (2012) Early changes in white matter pathways of the sensorimotor cortex in premanifest Huntington’s disease. Hum Brain Mapp 33:203–212CrossRefPubMedGoogle Scholar
  27. 27.
    Novak MJU, Seunarine KK, Gibbard CR et al (2014) White matter integrity in premanifest and early Huntington’s disease is related to caudate loss and disease progression. Cortex 52(1):98–112CrossRefPubMedGoogle Scholar
  28. 28.
    Poudel GR, Stout JC, Dominguez DJ et al (2015) Longitudinal change in white matter microstructure in Huntington’s disease: the IMAGE-HD study. Neurobiol Dis 74:406–412CrossRefPubMedGoogle Scholar
  29. 29.
    Poudel GR, Stout JC, Dominguez DJ et al (2014) White matter connectivity reflects clinical and cognitive status in Huntington’s disease. Neurobiol Dis 65:180–187CrossRefPubMedGoogle Scholar
  30. 30.
    Gregory S, Cole JH, Farmer RE et al (2015) Longitudinal diffusion tensor imaging shows progressive changes in white matter in Huntington’s disease. J Huntingtons Dis 4:333–346CrossRefPubMedGoogle Scholar
  31. 31.
    Klöppel S, Draganski B, Golding CV et al (2008) White matter connections reflect changes in voluntary-guided saccades in pre-symptomatic Huntington’s disease. Brain 131:196–204CrossRefPubMedGoogle Scholar
  32. 32.
    McColgan P, Seunarine KK, Razi A et al (2015) Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington’s disease. Brain 138:3327–3344CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Orth M, Gregory S, Scahill RI et al (2016) Natural variation in sensory-motor white matter organization influences manifestations of Huntington’s disease. Hum Brain Mapp 37:4615–4628CrossRefPubMedGoogle Scholar
  34. 34.
    OF O, Caeyenberghs K, Hosseini H et al (2015) Dynamics of the connectome in Huntington’s disease: a longitudinal diffusion MRI study. Neuroimage Clin 9:32–43CrossRefGoogle Scholar
  35. 35.
    OF O, Leemans A, Reijntjes RH et al (2015) Microstructural brain abnormalities in Huntington’s disease: a two-year follow-up. Hum Brain Mapp 36:2061–2074CrossRefGoogle Scholar
  36. 36.
    Gregory S, Scahill RI, Seunarine KK et al (2015) Neuropsychiatry and white matter microstructure in Huntington’s disease. J Huntingtons Dis 4:239–249CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Ogawa S, Lee TM, Kay AR, Tank DW (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 87:9868–9872CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Ogawa S, Menon RS, Tank DW et al (1993) Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys J 64:803–812CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Bandettini PA, Wong EC, Hinks RS et al (1992) Time course EPI of human brain function during task activation. Magn Reson Med 25:390–397CrossRefPubMedGoogle Scholar
  40. 40.
    Logothetis NK, Pfeuffer J (2004) On the nature of the BOLD fMRI contrast mechanism. Magn Reson Imaging 22:1517–1531CrossRefPubMedGoogle Scholar
  41. 41.
    Kim J, Zhu W, Chang L et al (2007) Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Hum Brain Mapp 28:85–93CrossRefPubMedGoogle Scholar
  42. 42.
    Seth AK, Barrett AB, Barnett L (2015) Granger causality analysis in neuroscience and neuroimaging. J Neurosci 35:3293–3297CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. Neuroimage 19:1273–1302CrossRefPubMedGoogle Scholar
  44. 44.
    Georgiou-Karistianis N, Poudel GR, Dominguez DJ et al (2013) Functional and connectivity changes during working memory in Huntington’s disease: 18 month longitudinal data from the IMAGE-HD study. Brain Cogn 83:80–89CrossRefPubMedGoogle Scholar
  45. 45.
    Poudel GR, Stout JC, Dominguez DJ et al (2015) Functional changes during working memory in Huntington’s disease: 30-month longitudinal data from the IMAGE-HD study. Brain Struct Funct 220:501–512CrossRefPubMedGoogle Scholar
  46. 46.
    Wolf RC, Kloppel S (2013) Clinical significance of frontal cortex abnormalities in Huntington’s disease. Exp Neurol 247:39–44CrossRefPubMedGoogle Scholar
  47. 47.
    Wolf RC, Sambataro F, Vasic N et al (2014) Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease. Psychol Med 44:3341–3356CrossRefPubMedGoogle Scholar
  48. 48.
    Wolf RC, Sambataro F, Vasic N et al (2008) Aberrant connectivity of lateral prefrontal networks in presymptomatic Huntington’s disease. Exp Neurol 213:137–144CrossRefPubMedGoogle Scholar
  49. 49.
    Wolf RC, Sambataro F, Vasic N et al (2014) Longitudinal task-negative network analyses in preclinical Huntington’s disease. Eur Arch Psychiatry Clin Neurosci 264:493–505CrossRefPubMedGoogle Scholar
  50. 50.
    Kloppel S, Draganski B, Siebner HR et al (2009) Functional compensation of motor function in pre-symptomatic Huntington’s disease. Brain 132:1624–1632CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Gray MA, Egan GF, Ando A et al (2013) Prefrontal activity in Huntington’s disease reflects cognitive and neuropsychiatric disturbances: the IMAGE-HD study. Exp Neurol 239:218–228CrossRefPubMedGoogle Scholar
  52. 52.
    Malejko K, Weydt P, Sussmuth SD et al (2014) Prodromal Huntington disease as a model for functional compensation of early neurodegeneration. PLoS One 9:e114569CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Kloppel S, Gregory S (2015) Compensation in preclinical Huntington’s disease: evidence from the Track-On HD study. EBioMedicine.
  54. 54.
    Harrington DL, Rubinov M, Durgerian S et al (2015) Network topology and functional connectivity disturbances precede the onset of Huntington’s disease. Brain 138:2332–2346CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Werner CJ, Dogan I, Sass C, Mirzazade S, Schiefer J, Shah NJ, Schulz JB, Reetz K (2014) Altered resting-state connectivity in Huntington’s disease. Hum Brain Mapp 35:2582–2593CrossRefPubMedGoogle Scholar
  56. 56.
    OF O, van den Berg-Huysmans AA et al (2015) Longitudinal resting state fMRI analysis in healthy controls and premanifest Huntington’s disease gene carriers: a three-year follow-up study. Hum Brain Mapp 36:110–119CrossRefGoogle Scholar
  57. 57.
    Mumford JA (2012) A power calculation guide for fMRI studies. Soc Cogn Affect Neurosci 7:738–742CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Button KS, Ioannidis JP, Mokrysz C et al (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14:365–376CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Pernet C, Poline JB (2015) Improving functional magnetic resonance imaging reproducibility. Gigascience 4:15. CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Mansfield P (1984) Real-time echo-planar imaging by NMR. Br Med Bull 40:187–190CrossRefPubMedGoogle Scholar
  61. 61.
    Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97CrossRefPubMedGoogle Scholar
  62. 62.
    Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851CrossRefPubMedGoogle Scholar
  63. 63.
    Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:11050–11055CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Fischl B, Salat DH, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355CrossRefPubMedGoogle Scholar
  65. 65.
    Fischl B, van der Kouwe A, Destrieux C et al (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11–22CrossRefPubMedGoogle Scholar
  66. 66.
    Andersson JL, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125:1063–1078CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Lori NF, Akbudak E, Shimony JS et al (2002) Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results. NMR Biomed 15:494–515CrossRefPubMedGoogle Scholar
  68. 68.
    Friston KJ, Worsley KJ, Frackowiak RS et al (1994) Assessing the significance of focal activations using their spatial extent. Hum Brain Mapp 1:210–220CrossRefPubMedGoogle Scholar
  69. 69.
    Worsley KJ, Marrett S, Neelin P et al (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 4:58–73CrossRefPubMedGoogle Scholar
  70. 70.
    Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44:83–98CrossRefPubMedGoogle Scholar
  71. 71.
    Khalsa S, Mayhew SD, Chechlacz M et al (2014) The structural and functional connectivity of the posterior cingulate cortex: comparison between deterministic and probabilistic tractography for the investigation of structure-function relationships. Neuroimage 102:118–127CrossRefPubMedGoogle Scholar
  72. 72.
    Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541CrossRefPubMedGoogle Scholar
  73. 73.
    Fox MD, Snyder AZ, Vincent JL et al (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102:9673–9678. CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Gotts SJ, Saad ZS, Jo HJ et al (2013) The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders. Front Hum Neurosci 7:356. CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156CrossRefPubMedGoogle Scholar
  76. 76.
    Qing Z, Dong Z, Li S, Zang Y, Liu D (2015) Global signal regression has complex effects on regional homogeneity of resting state fMRI signal. Magn Reson Imaging 33:1306–1313CrossRefPubMedGoogle Scholar
  77. 77.
    Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151CrossRefPubMedGoogle Scholar
  78. 78.
    Beckmann CF, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23:137–152CrossRefPubMedGoogle Scholar
  79. 79.
    Filippini N, MacIntosh BJ, Hough MG et al (2009) Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A 106:7209–7214CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sarah Gregory
    • 1
    Email author
  • Rachael I. Scahill
    • 1
  • Geraint Rees
    • 1
  • Sarah Tabrizi
    • 1
  1. 1.Huntington’s Disease Research CentreUCL Institute of NeurologyLondonUK

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