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Longitudinal Progression Markers of Parkinson’s Disease: Current View on Structural Imaging

  • Neuroimaging (N Pavese, Section Editor)
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Abstract

Purpose of Review

Advances in neuroimaging techniques pave a rich avenue for in vivo progression biomarkers, which can objectively and noninvasively assess the long-term dynamic alterations in the brain of Parkinson’s disease (PD) patients. This article reviews recent progress in structural magnetic resonance imaging (MRI) tools to track disease progression in PD, and discusses specific criteria a neuroimaging tool needs to meet to be a progression biomarker of PD and the potential applications of these techniques in PD based on current evidence.

Recent Findings

Recent longitudinal studies showed that quantitative structural MRI markers derived from T1-weighted, diffusion-weighted, neuromelanin-sensitive, and iron-sensitive imaging have the potential to track disease progression in PD. However, validation of these progression biomarkers is only beginning, and more work is required for multisite validation, the sample size for use in a clinical trial, and drug-responsiveness of most of these biomarkers. At present, the most clinical trial-ready biomarker is free-water diffusion imaging of the substantia nigra and seems well established to be used in disease-modifying studies in PD.

Summary

A variety of structural imaging biomarkers are promising candidates to be progression biomarkers in PD. Further studies are needed to elucidate the sensitivity, reliability, sample size, and effect of confounding factors of these progression biomarkers.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Parkinson’s Disease Foundation. Statistics on Parkinson’s. http://www.pdforg/en/parkinson_statistics (Accessed July 12, 2018).

  2. Pringsheim T, Jette N, Frolkis A, Steeves TD. The prevalence of Parkinson’s disease: a systematic review and meta-analysis. Mov Disord. 2014;29(13):1583–90. https://doi.org/10.1002/mds.25945.

    Article  PubMed  Google Scholar 

  3. Kalia LV, Lang AE. Parkinson’s disease. Lancet (London, England). 2015;386(9996):896–912. https://doi.org/10.1016/s0140-6736(14)61393-3.

    Article  CAS  Google Scholar 

  4. Bernheimer H, Birkmayer W, Hornykiewicz O, Jellinger K, Seitelberger F. Brain dopamine and the syndromes of Parkinson and Huntington. Clinical, morphological and neurochemical correlations. J Neurol Sci. 1973;20(4):415–55.

    Article  CAS  Google Scholar 

  5. Cheng HC, Ulane CM, Burke RE. Clinical progression in Parkinson disease and the neurobiology of axons. Ann Neurol. 2010;67(6):715–25. https://doi.org/10.1002/ana.21995.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Dauer W, Przedborski S. Parkinson’s disease: mechanisms and models. Neuron. 2003;39(6):889–909.

    Article  CAS  Google Scholar 

  7. Braak H, Del Tredici K, Rub U, de Vos RA, Jansen Steur EN, Braak E. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging. 2003;24(2):197–211.

    Article  Google Scholar 

  8. Ashburner J, Friston KJ. Voxel-based morphometry--the methods. NeuroImage. 2000;11(6 Pt 1):805–21. https://doi.org/10.1006/nimg.2000.0582.

    Article  CAS  PubMed  Google Scholar 

  9. Diez-Cirarda M, Ojeda N, Pena J, Cabrera-Zubizarreta A, Lucas-Jimenez O, Gomez-Esteban JC, et al. Long-term effects of cognitive rehabilitation on brain, functional outcome and cognition in Parkinson’s disease. Eur J Neurol. 2018;25(1):5–12. https://doi.org/10.1111/ene.13472.

    Article  CAS  PubMed  Google Scholar 

  10. Gee M, Dukart J, Draganski B, Wayne Martin WR, Emery D, Camicioli R. Regional volumetric change in Parkinson’s disease with cognitive decline. J Neurol Sci. 2017;373:88–94. https://doi.org/10.1016/j.jns.2016.12.030.

    Article  PubMed  Google Scholar 

  11. Ibarretxe-Bilbao N, Ramirez-Ruiz B, Junque C, Marti MJ, Valldeoriola F, Bargallo N, et al. Differential progression of brain atrophy in Parkinson’s disease with and without visual hallucinations. J Neurol Neurosurg Psychiatry. 2010;81(6):650–7. https://doi.org/10.1136/jnnp.2009.179655.

    Article  PubMed  Google Scholar 

  12. Mak E, Su L, Williams GB, Firbank MJ, Lawson RA, Yarnall AJ, et al. Longitudinal whole-brain atrophy and ventricular enlargement in nondemented Parkinson’s disease. Neurobiol Aging. 2017;55:78–90. https://doi.org/10.1016/j.neurobiolaging.2017.03.012.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mollenhauer B, Zimmermann J, Sixel-Doring F, Focke NK, Wicke T, Ebentheuer J, et al. Monitoring of 30 marker candidates in early Parkinson disease as progression markers. Neurology. 2016;87(2):168–77. https://doi.org/10.1212/wnl.0000000000002651.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ibarretxe-Bilbao N, Junque C, Segura B, Baggio HC, Marti MJ, Valldeoriola F, et al. Progression of cortical thinning in early Parkinson’s disease. Mov Disord. 2012;27(14):1746–53. https://doi.org/10.1002/mds.25240.

    Article  PubMed  Google Scholar 

  15. Zeng Q, Guan X, JCF LYL, Shen Z, Guo T, Xuan M, et al. Longitudinal alterations of local spontaneous brain activity in Parkinson’s disease. Neurosci Bull. 2017;33(5):501–9. https://doi.org/10.1007/s12264-017-0171-9.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jia X, Liang P, Li Y, Shi L, Wang D, Li K. Longitudinal study of gray matter changes in Parkinson disease. AJNR Am J Neuroradiol. 2015;36(12):2219–26. https://doi.org/10.3174/ajnr.A4447.

    Article  CAS  PubMed  Google Scholar 

  17. Hua X, Gutman B, Boyle CP, Rajagopalan P, Leow AD, Yanovsky I, et al. Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry. NeuroImage. 2011;57(1):5–14. https://doi.org/10.1016/j.neuroimage.2011.01.079.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Tessa C, Lucetti C, Giannelli M, Diciotti S, Poletti M, Danti S, et al. Progression of brain atrophy in the early stages of Parkinson’s disease: a longitudinal tensor-based morphometry study in de novo patients without cognitive impairment. Hum Brain Mapp. 2014;35(8):3932–44. https://doi.org/10.1002/hbm.22449.

    Article  PubMed  Google Scholar 

  19. Yau Y, Zeighami Y, Baker TE, Larcher K, Vainik U, Dadar M, et al. Network connectivity determines cortical thinning in early Parkinson’s disease progression. Nat Commun. 2018;9(1):12. https://doi.org/10.1038/s41467-017-02416-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mak E, Su L, Williams GB, Firbank MJ, Lawson RA, Yarnall AJ, et al. Baseline and longitudinal grey matter changes in newly diagnosed Parkinson’s disease: ICICLE-PD study. Brain. 2015;138(Pt 10):2974–86. https://doi.org/10.1093/brain/awv211.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Campabadal A, Uribe C, Segura B, Baggio HC, Abos A, Garcia-Diaz AI, et al. Brain correlates of progressive olfactory loss in Parkinson’s disease. Parkinsonism Relat Disord. 2017;41:44–50. https://doi.org/10.1016/j.parkreldis.2017.05.005.

    Article  PubMed  Google Scholar 

  22. Garcia-Diaz AI, Segura B, Baggio HC, Uribe C, Campabadal A, Abos A, et al. Cortical thinning correlates of changes in visuospatial and visuoperceptual performance in Parkinson’s disease: a 4-year follow-up. Parkinsonism Relat Disord. 2018;46:62–8. https://doi.org/10.1016/j.parkreldis.2017.11.003.

    Article  CAS  PubMed  Google Scholar 

  23. Nurnberger L, Gracien RM, Hok P, Hof SM, Rub U, Steinmetz H, et al. Longitudinal changes of cortical microstructure in Parkinson’s disease assessed with T1 relaxometry. Neuroimage Clin. 2017;13:405–14. https://doi.org/10.1016/j.nicl.2016.12.025.

    Article  PubMed  Google Scholar 

  24. Levy G. The relationship of Parkinson disease with aging. Arch Neurol. 2007;64(9):1242–6. https://doi.org/10.1001/archneur.64.9.1242.

    Article  PubMed  Google Scholar 

  25. • Sterling NW, Wang M, Zhang L, Lee EY, Du G, Lewis MM, et al. Stage-dependent loss of cortical gyrification as Parkinson disease “unfolds”. Neurology. 2016;86(12):1143–51. https://doi.org/10.1212/wnl.0000000000002492 This study reports different cortical gyrification rates in PD patients with different disease durations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lewis MM, Du G, Lee EY, Nasralah Z, Sterling NW, Zhang L, et al. The pattern of gray matter atrophy in Parkinson’s disease differs in cortical and subcortical regions. J Neurol. 2016;263(1):68–75. https://doi.org/10.1007/s00415-015-7929-7.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Melzer TR, Myall DJ, MacAskill MR, Pitcher TL, Livingston L, Watts R, et al. Tracking Parkinson’s disease over one year with multimodal magnetic resonance imaging in a group of older Patients with moderate disease. PLoS One. 2015;10(12):e0143923. https://doi.org/10.1371/journal.pone.0143923.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hanganu A, Bedetti C, Degroot C, Mejia-Constain B, Lafontaine AL, Soland V, et al. Mild cognitive impairment is linked with faster rate of cortical thinning in patients with Parkinson’s disease longitudinally. Brain. 2014;137(Pt 4):1120–9. https://doi.org/10.1093/brain/awu036.

    Article  PubMed  Google Scholar 

  29. Matsuura K, Maeda M, Tabei KI, Umino M, Kajikawa H, Satoh M, et al. A longitudinal study of neuromelanin-sensitive magnetic resonance imaging in Parkinson’s disease. Neurosci Lett. 2016;633:112–7. https://doi.org/10.1016/j.neulet.2016.09.011.

    Article  CAS  PubMed  Google Scholar 

  30. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001;13(4):534–46.

    Article  Google Scholar 

  31. Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006;51(5):527–39. https://doi.org/10.1016/j.neuron.2006.08.012.

    Article  CAS  PubMed  Google Scholar 

  32. Bennett IJ, Madden DJ, Vaidya CJ, Howard DV, Howard JH Jr. Age-related differences in multiple measures of white matter integrity: a diffusion tensor imaging study of healthy aging. Hum Brain Mapp. 2010;31(3):378–90. https://doi.org/10.1002/hbm.20872.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rossi ME, Ruottinen H, Saunamaki T, Elovaara I, Dastidar P. Imaging brain iron and diffusion patterns: a follow-up study of Parkinson’s disease in the initial stages. Acad Radiol. 2014;21(1):64–71. https://doi.org/10.1016/j.acra.2013.09.018.

    Article  PubMed  Google Scholar 

  34. Lenfeldt N, Larsson A, Nyberg L, Birgander R, Forsgren L. Fractional anisotropy in the substantia nigra in Parkinson’s disease: a complex picture. Eur J Neurol. 2015;22(10):1408–14. https://doi.org/10.1111/ene.12760.

    Article  CAS  PubMed  Google Scholar 

  35. Loane C, Politis M, Kefalopoulou Z, Valle-Guzman N, Paul G, Widner H, et al. Aberrant nigral diffusion in Parkinson’s disease: a longitudinal diffusion tensor imaging study. Mov Disord. 2016;31(7):1020–6. https://doi.org/10.1002/mds.26606.

    Article  PubMed  Google Scholar 

  36. Zhang Y, Wu IW, Tosun D, Foster E, Schuff N. Progression of regional microstructural degeneration in Parkinson’s disease: a multicenter diffusion tensor imaging study. PLoS One. 2016;11(10):e0165540. https://doi.org/10.1371/journal.pone.0165540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cochrane CJ, Ebmeier KP. Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology. 2013;80(9):857–64. https://doi.org/10.1212/WNL.0b013e318284070c.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Schwarz ST, Abaei M, Gontu V, Morgan PS, Bajaj N, Auer DP. Diffusion tensor imaging of nigral degeneration in Parkinson’s disease: a region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis. Neuroimage Clin. 2013;3:481–8. https://doi.org/10.1016/j.nicl.2013.10.006.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Metzler-Baddeley C, O’Sullivan MJ, Bells S, Pasternak O, Jones DK. How and how not to correct for CSF-contamination in diffusion MRI. NeuroImage. 2012;59(2):1394–403. https://doi.org/10.1016/j.neuroimage.2011.08.043.

    Article  PubMed  Google Scholar 

  40. Pasternak O, Sochen N, Gur Y, Intrator N, Assaf Y. Free water elimination and mapping from diffusion MRI. Magn Reson Med. 2009;62(3):717–30. https://doi.org/10.1002/mrm.22055.

    Article  PubMed  Google Scholar 

  41. Alexander AL, Hasan KM, Lazar M, Tsuruda JS, Parker DL. Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med. 2001;45(5):770–80.

    Article  CAS  Google Scholar 

  42. •• Burciu RG, Ofori E, Archer DB, Wu SS, Pasternak O, McFarland NR, et al. Progression marker of Parkinson’s disease: a 4-year multi-site imaging study. Brain. 2017;140(8):2183–92. https://doi.org/10.1093/brain/awx146 This study replicated the 1-year increase of free-water in the posterior substantia nigra in PD but not in controls across multisites, and reported the 4-year longitudinal increase of free-water in the posterior substantia nigra in PD patients.

    Article  PubMed  PubMed Central  Google Scholar 

  43. • Ofori E, Pasternak O, Planetta PJ, Burciu R, Snyder A, Febo M, et al. Increased free water in the substantia nigra of Parkinson’s disease: a single-site and multi-site study. Neurobiology of aging. 2015;36(2):1097–104. https://doi.org/10.1016/j.neurobiolaging.2014.10.029 This study reports the consistent increased free-water in PD patients but not in controls across multisites.

    Article  CAS  PubMed  Google Scholar 

  44. • Ofori E, Pasternak O, Planetta PJ, Li H, Burciu RG, Snyder AF, et al. Longitudinal changes in free-water within the substantia nigra of Parkinson’s disease. Brain. 2015;138(Pt 8):2322–31. https://doi.org/10.1093/brain/awv136 This study reported that free-water in the posterior substantia nigra increased longitudinally over 1 year in PD patients but not in controls.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Guttuso T Jr, Bergsland N, Hagemeier J, Lichter DG, Pasternak O, Zivadinov R. Substantia nigra free water increases longitudinally in Parkinson disease. AJNR Am J Neuroradiol. 2018;39:479–84. https://doi.org/10.3174/ajnr.A5545.

    Article  Google Scholar 

  46. Surova Y, Nilsson M, Lampinen B, Latt J, Hall S, Widner H, et al. Alteration of putaminal fractional anisotropy in Parkinson’s disease: a longitudinal diffusion kurtosis imaging study. Neuroradiology. 2018;60(3):247–54. https://doi.org/10.1007/s00234-017-1971-3.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Sofic E, Paulus W, Jellinger K, Riederer P, Youdim MB. Selective increase of iron in substantia nigra zona compacta of parkinsonian brains. J Neurochem. 1991;56(3):978–82.

    Article  CAS  Google Scholar 

  48. Dexter DT, Wells FR, Agid F, Agid Y, Lees AJ, Jenner P, et al. Increased nigral iron content in postmortem parkinsonian brain. Lancet (London, England). 1987;2(8569):1219–20.

    Article  CAS  Google Scholar 

  49. Guan X, Xu X, Zhang M. Region-specific iron measured by MRI as a biomarker for Parkinson’s disease. Neurosci Bull. 2017;33(5):561–7. https://doi.org/10.1007/s12264-017-0138-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hopes L, Grolez G, Moreau C, Lopes R, Ryckewaert G, Carriere N, et al. Magnetic resonance imaging features of the nigrostriatal system: biomarkers of Parkinson’s disease stages? PLoS One. 2016;11(4):e0147947. https://doi.org/10.1371/journal.pone.0147947.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ulla M, Bonny JM, Ouchchane L, Rieu I, Claise B, Durif F. Is R2* a new MRI biomarker for the progression of Parkinson’s disease? A longitudinal follow-up. PLoS One. 2013;8(3):e57904. https://doi.org/10.1371/journal.pone.0057904.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wieler M, Gee M, Martin WR. Longitudinal midbrain changes in early Parkinson’s disease: iron content estimated from R2*/MRI. Parkinsonism Relat Disord. 2015;21(3):179–83. https://doi.org/10.1016/j.parkreldis.2014.11.017.

    Article  PubMed  Google Scholar 

  53. Wieler M, Gee M, Camicioli R, Martin WR. Freezing of gait in early Parkinson’s disease: nigral iron content estimated from magnetic resonance imaging. J Neurol Sci. 2016;361:87–91. https://doi.org/10.1016/j.jns.2015.12.008.

    Article  CAS  PubMed  Google Scholar 

  54. Du G, Lewis MM, Sica C, He L, Connor JR, Kong L, et al. Distinct progression pattern of susceptibility MRI in the substantia nigra of Parkinson’s patients. Mov Disord. 2018. https://doi.org/10.1002/mds.27318.

    Article  Google Scholar 

  55. Barbosa JH, Santos AC, Tumas V, Liu M, Zheng W, Haacke EM, et al. Quantifying brain iron deposition in patients with Parkinson’s disease using quantitative susceptibility mapping, R2 and R2. Magn Reson Imaging. 2015;33(5):559–65. https://doi.org/10.1016/j.mri.2015.02.021.

    Article  CAS  PubMed  Google Scholar 

  56. Du G, Liu T, Lewis MM, Kong L, Wang Y, Connor J, et al. Quantitative susceptibility mapping of the midbrain in Parkinson’s disease. Mov Disord. 2016;31(3):317–24. https://doi.org/10.1002/mds.26417.

    Article  PubMed  Google Scholar 

  57. Brooks DJ, Frey KA, Marek KL, Oakes D, Paty D, Prentice R, et al. Assessment of neuroimaging techniques as biomarkers of the progression of Parkinson’s disease. Exp Neurol. 2003;184(Suppl 1):S68–79.

    Article  CAS  Google Scholar 

  58. McGhee DJ, Royle PL, Thompson PA, Wright DE, Zajicek JP, Counsell CE. A systematic review of biomarkers for disease progression in Parkinson’s disease. BMC Neurol. 2013;13:35. https://doi.org/10.1186/1471-2377-13-35.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Wang J, Hoekstra JG, Zuo C, Cook TJ, Zhang J. Biomarkers of Parkinson’s disease: current status and future perspectives. Drug Discov Today. 2013;18(3–4):155–62. https://doi.org/10.1016/j.drudis.2012.09.001.

    Article  CAS  PubMed  Google Scholar 

  60. Shao N, Yang J, Shang H. Voxelwise meta-analysis of gray matter anomalies in Parkinson variant of multiple system atrophy and Parkinson’s disease using anatomic likelihood estimation. Neurosci Lett. 2015;587:79–86. https://doi.org/10.1016/j.neulet.2014.12.007.

    Article  CAS  PubMed  Google Scholar 

  61. Hikishima K, Ando K, Komaki Y, Kawai K, Yano R, Inoue T, et al. Voxel-based morphometry of the marmoset brain: in vivo detection of volume loss in the substantia nigra of the MPTP-treated Parkinson’s disease model. Neuroscience. 2015;300:585–92. https://doi.org/10.1016/j.neuroscience.2015.05.041.

    Article  CAS  PubMed  Google Scholar 

  62. Vernon AC, Crum WR, Johansson SM, Modo M. Evolution of extra-nigral damage predicts behavioural deficits in a rat proteasome inhibitor model of Parkinson’s disease. PLoS One. 2011;6(2):e17269. https://doi.org/10.1371/journal.pone.0017269.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Van Camp N, Blockx I, Verhoye M, Casteels C, Coun F, Leemans A, et al. Diffusion tensor imaging in a rat model of Parkinson’s disease after lesioning of the nigrostriatal tract. NMR Biomed. 2009;22(7):697–706. https://doi.org/10.1002/nbm.1381.

    Article  PubMed  Google Scholar 

  64. Boska MD, Hasan KM, Kibuule D, Banerjee R, McIntyre E, Nelson JA, et al. Quantitative diffusion tensor imaging detects dopaminergic neuronal degeneration in a murine model of Parkinson’s disease. Neurobiol Dis. 2007;26(3):590–6. https://doi.org/10.1016/j.nbd.2007.02.010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hikishima K, Ando K, Yano R, Kawai K, Komaki Y, Inoue T, et al. Parkinson disease: diffusion MR imaging to detect nigrostriatal pathway loss in a marmoset model treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Radiology. 2015;275(2):430–7. https://doi.org/10.1148/radiol.14140601.

    Article  PubMed  Google Scholar 

  66. Virel A, Faergemann E, Oradd G, Stromberg I. Magnetic resonance imaging (MRI) to study striatal iron accumulation in a rat model of Parkinson’s disease. PLoS One. 2014;9(11):e112941. https://doi.org/10.1371/journal.pone.0112941.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Jovicich J, Marizzoni M, Bosch B, Bartres-Faz D, Arnold J, Benninghoff J, et al. Multisite longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging of healthy elderly subjects. NeuroImage. 2014;101:390–403. https://doi.org/10.1016/j.neuroimage.2014.06.075.

    Article  PubMed  Google Scholar 

  68. Fox RJ, Sakaie K, Lee JC, Debbins JP, Liu Y, Arnold DL, et al. A validation study of multicenter diffusion tensor imaging: reliability of fractional anisotropy and diffusivity values. AJNR Am J Neuroradiol. 2012;33(4):695–700. https://doi.org/10.3174/ajnr.A2844.

    Article  CAS  PubMed  Google Scholar 

  69. • Albi A, Pasternak O, Minati L, Marizzoni M, Bartres-Faz D, Bargallo N, et al. Free water elimination improves test-retest reproducibility of diffusion tensor imaging indices in the brain: a longitudinal multisite study of healthy elderly subjects. Hum Brain Mapp. 2017;38(1):12–26. https://doi.org/10.1002/hbm.23350 This study compared test-retest reproducibility of measurements derived from single-tensor dMRI and bi-tensor dMRI across MRI sites.

    Article  PubMed  Google Scholar 

  70. Seiger R, Hahn A, Hummer A, Kranz GS, Ganger S, Kublbock M, et al. Voxel-based morphometry at ultra-high fields. A comparison of 7T and 3T MRI data. NeuroImage. 2015;113:207–16. https://doi.org/10.1016/j.neuroimage.2015.03.019.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Takao H, Hayashi N, Ohtomo K. Effect of scanner in longitudinal studies of brain volume changes. J Magn Reson Imaging. 2011;34(2):438–44. https://doi.org/10.1002/jmri.22636.

    Article  PubMed  Google Scholar 

  72. Langley J, Huddleston DE, Liu CJ, Hu X. Reproducibility of locus coeruleus and substantia nigra imaging with neuromelanin sensitive MRI. Magma (New York, NY). 2017;30(2):121–5. https://doi.org/10.1007/s10334-016-0590-z.

    Article  CAS  Google Scholar 

  73. Deh K, Nguyen TD, Eskreis-Winkler S, Prince MR, Spincemaille P, Gauthier S, et al. Reproducibility of quantitative susceptibility mapping in the brain at two field strengths from two vendors. J Magn Reson Imaging. 2015;42(6):1592–600. https://doi.org/10.1002/jmri.24943.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Santin MD, Didier M, Valabregue R, Yahia Cherif L, Garcia-Lorenzo D, Loureiro de Sousa P et al. Reproducibility of R2 * and quantitative susceptibility mapping (QSM) reconstruction methods in the basal ganglia of healthy subjects. NMR in biomedicine. 2017;30(4). doi:https://doi.org/10.1002/nbm.3491.

    Article  Google Scholar 

  75. Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. NeuroImage. 2006;32(1):180–94. https://doi.org/10.1016/j.neuroimage.2006.02.051.

    Article  PubMed  Google Scholar 

  76. Tustison NJ, Cook PA, Klein A, Song G, Das SR, Duda JT, et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. NeuroImage. 2014;99:166–79. https://doi.org/10.1016/j.neuroimage.2014.05.044.

    Article  PubMed  Google Scholar 

  77. Cousineau M, Jodoin PM, Morency FC, Rozanski V, Grand’Maison M, Bedell BJ, et al. A test-retest study on Parkinson’s PPMI dataset yields statistically significant white matter fascicles. Neuroimage Clin. 2017;16:222–33. https://doi.org/10.1016/j.nicl.2017.07.020.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Chung JW, Burciu RG, Ofori E, Shukla P, Okun MS, Hess CW, et al. Parkinson’s disease diffusion MRI is not affected by acute antiparkinsonian medication. Neuroimage Clin. 2017;14:417–21. https://doi.org/10.1016/j.nicl.2017.02.012.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Ray NJ, Bradburn S, Murgatroyd C, Toseeb U, Mir P, Kountouriotis GK, et al. In vivo cholinergic basal forebrain atrophy predicts cognitive decline in de novo Parkinson’s disease. Brain. 2018;141(1):165–76. https://doi.org/10.1093/brain/awx310.

    Article  PubMed  Google Scholar 

  80. Dickerson BC, Sperling RA. Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer’s disease. NeuroRx. 2005;2(2):348–60. https://doi.org/10.1602/neurorx.2.2.348.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by R01 NS052318; U01 NS102038.

Author information

Authors and Affiliations

  1. Department of Neurology, West China Hospital of Sichuan University, Chengdu, China

    Jing Yang

  2. Department of Applied Physiology and Kinesiology, University of Florida, P.O. Box 118205, Gainesville, FL, 32611, USA

    Roxana G. Burciu

  3. Department of Kinesiology and Applied Physiology, College of Health Sciences, University of Delaware, Newark, DE, USA

    Roxana G. Burciu

  4. Department of Applied Physiology and Kinesiology, Department of Neurology, Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    David E. Vaillancourt

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Correspondence to David E. Vaillancourt.

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Conflict of Interest

Dr. Jing Yang and Dr. Roxana G. Burciu declare that they have no conflicts of interest. Dr. David E. Vaillancourt reports grants from NIH, NSF, and Tyler’s Hope Foundation during the conduct of the study, and personal honoraria from NIH, National Parkinson’s Foundation, Sanofi, and Northwestern University unrelated to the submitted work. Dr. David E. Vaillancourt reports a patent 62/486,580 is pending.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Neuroimaging

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Yang, J., Burciu, R.G. & Vaillancourt, D.E. Longitudinal Progression Markers of Parkinson’s Disease: Current View on Structural Imaging. Curr Neurol Neurosci Rep 18, 83 (2018). https://doi.org/10.1007/s11910-018-0894-7

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  • DOI: https://doi.org/10.1007/s11910-018-0894-7

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