Advances in Clinical and Experimental Medicine

Title abbreviation: Adv Clin Exp Med
JCR Impact Factor (IF) – 2.1 (5-Year IF – 2.0)
Journal Citation Indicator (JCI) (2023) – 0.4
Scopus CiteScore – 3.7 (CiteScore Tracker – 4.2)
Index Copernicus  – 171.00; MNiSW – 70 pts

ISSN 1899–5276 (print)
ISSN 2451-2680 (online)
Periodicity – monthly

Open Access
Clockss
Download original text (EN)

Advances in Clinical and Experimental Medicine

2019, vol. 28, nr 7, July, p. 989–999

doi: 10.17219/acem/94137

Publication type: review

Language: English

Download citation:

  • BIBTEX (JabRef, Mendeley)
  • RIS (Papers, Reference Manager, RefWorks, Zotero)

The role of MR volumetry in brain atrophy assessment in multiple sclerosis: A review of the literature

Ewelina Marciniewicz1,A,B,C,D, Przemysław Podgórski1,B,C,D, Marek Sąsiadek1,E,F, Joanna Bladowska1,A,B,C,D,E,F

1 Department of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw Medical University, Poland

Abstract

We review the current role of magnetic resonance (MR) volumetry as a meaningful indicator of neurodegeneration and clinical disease progression in multiple sclerosis (MS) patients. Based on a review of the current literature we summarize the mechanisms that contribute to brain atrophy. We present the newest magnetic resonance imaging (MRI)-based methods used in atrophy quantification. We also analyze important biological factors which can influence the accuracy of brain atrophy evaluation. Evidence shows that measures of brain volume (BV) have the potential to be an important determinant of disease progression to a greater extent than conventional lesion assessment. Finally, scientific reports concerning limitations of MRI-based volumetry that affect its implementation into routine clinical practice are also reviewed. The technical challenges that need to be overcome include creating a standardized protocol for image acquisition − a fully automated, accurate and reproducible method that allows comparison in either single-center or multicenter settings. In the near future, quantitative MR research will probably be the basic method used in neurology to monitor the rate of atrophic processes and clinical deterioration in MS patients, and to evaluate the results of treatment.

Key words

magnetic resonance imaging, multiple sclerosis (MS), brain atrophy, MR volumetry

References (68)

  1. Lassmann H. Neuropathology in multiple sclerosis: New concepts. Mult Scler. 1998;4(3):93–98.
  2. Bermel RA, Bakshi R. The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol. 2006;5(2):158–170.
  3. Lassmann H, Brück W, Lucchinetti C. Heterogeneity of multiple sclerosis pathogenesis: Implications for diagnosis and therapy. Trends Mol Med. 2001;7(3):115–121.
  4. Ceccarelli A, Rocca MA, Pagani E, et al. A voxel-based morphometry study of grey matter loss in MS patients with different clinical phenotypes. Neuroimage. 2008;42(1):315–322.
  5. Chetelat G, Baron J-C. Early diagnosis of Alzheimer’s disease: Contribution of structural neuroimaging. Neuroimage. 2003;18(2):525–541.
  6. Fox NC, Freeborough PA. Brain atrophy progression measured from registered serial MRI: Validation and application to Alzheimer’s disease. J Magn Reson Imaging. 1997;7(6):1069–1075.
  7. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002;17(1):479–489.
  8. Rocca MA, Battaglini M, Benedict RHB, et al. Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology. 2017;88(4):403–413.
  9. Popescu V, Klaver R, Versteeg A, et al. Postmortem validation of MRI cortical volume measurements in MS. Hum Brain Mapp. 2016;37(6):2223–2233.
  10. Klaver R, Popescu V, Voorn P, et al. Neuronal and axonal loss in normal-appearing gray matter and subpial lesions in multiple sclerosis. J Neuropathol Exp Neurol. 2015;74(5):453–458.
  11. Hardmeier M, Wagenpfeil S, Freitag P, et al; European IFN-1a in Relapsing MS Dose Comparison Trial Study Group. Rate of brain atrophy in relapsing MS decreases during treatment with IFNbeta 1a. Neurology. 2005;64(2):236–240.
  12. Bakshi R, Thompson AJ, Rocca MA, et al. MRI in multiple sclerosis: Current status and future prospects. Lancet Neurol. 2008;7(7):615–625.
  13. Fox RJ, Fisher E, Tkach J, Lee J-C, Cohen JA, Rudick RA. Brain atrophy and magnetization transfer ratio following methylprednisolone in multiple sclerosis: Short-term changes and long-term implications. Mult Scler. 2005;11(2):140–145.
  14. Ceccarelli A, Rocca MA, Pagani E, et al. A voxel-based morphometry study of grey matter loss in MS patients with different clinical phenotypes. Neuroimage. 2008;42(1):315–322.
  15. Kutzelnigg A, Lucchinetti CF, Stadelmann C, et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain. 2005;128(Pt 11):2705–2712.
  16. Pirko I, Johnson AJ, Chen Y, et al. Brain atrophy correlates with functional outcome in a murine model of multiple sclerosis. Neuroimage. 2011;54(2):802–806.
  17. De Stefano N, Narayanan S, Francis GS, et al. Evidence of axonal damage in the early stages of multiple sclerosis and its relevance to disability. Arch Neurol. 2001;58(1):65–70.
  18. Morgen K, Sammer G, Courtney SM, et al. Evidence for a direct association between cortical atrophy and cognitive impairment in relapsing–remitting MS. Neuroimage. 2006;30(3):891–898.
  19. Ellwardt E, Zipp F. Molecular mechanisms linking neuroinflammation and neurodegeneration in MS. Exp Neurol. 2014;262(Pt A):8–17.
  20. Zindler E, Zipp F. Neuronal injury in chronic CNS inflammation. Best Pract Res Clin Anaesthesiol. 2010;24(4):551–562.
  21. De Stefano N, Iannucci G, Sormani MP, et al. MR correlates of cerebral atrophy in patients with multiple sclerosis. J Neurol. 2002;249(8):1072–1077.
  22. Gonçalves LI, Dos Passos GR, Conzatti LP, et al. Correlation between the corpus callosum index and brain atrophy, lesion load, and cognitive dysfunction in multiple sclerosis. Mult Scler Relat Disord. 2018;20:154–158.
  23. Wiggermann V, Ibs I, Schoerner S, et al. Exploring mechanisms of multiple sclerosis lesion evolution using advanced MRI. Neurology. 2016;86(Suppl 16):I10.012.
  24. Zivadinov R, Jakimovski D, Gandhi S, et al. Clinical relevance of brain atrophy assessment in multiple sclerosis: Implications for its use in a clinical routine. Expert Rev Neurother. 2016;16(7):777–793.
  25. Ohara N, Suzuki H, Suzuki A, et al. Reversible brain atrophy and cognitive impairment in an adolescent Japanese patient with primary adrenal Cushing’s syndrome. Neuropsychiatr Dis Treat. 2014;10:1763–1767.
  26. Zivadinov R, Bergsland N, Dolezal O, et al. Evolution of cortical and thalamus atrophy and disability progression in early relapsing-remitting MS during 5 years. AJNR Am J Neuroradiol. 2013;34:1931–1939.
  27. Duning T, Kloska S, Steinsträter O, Kugel H, Heindel W, Knecht S. Dehydration confounds the assessment of brain atrophy. Neurology. 2005;64(3):548–550.
  28. Meyers SM, Tam R, Lee JS, et al. Does hydration status affect MRI measures of brain volume or water content? J Magn Reson Imaging. 2016;44(2):296–304.
  29. Mellanby AR, Reveley MA. Effects of acute dehydration on computerized tomographic assessment of cerebral density and ventricular volume. Lancet. 1982;2(8303):874.
  30. Heinz ER, Martinez J, Haenggeli A. Reversibility of cerebral atrophy in anorexia nervosa and Cushing’s syndrome. J Comput Assist Tomogr. 1977;1(4):415–418.
  31. Addolorato G, Taranto C, De Rossi G, Gasbarrini G. Neuroimaging of cerebral and cerebellar atrophy in anorexia nervosa. Psychiatry Res. 1997;76(2–3):139–141.
  32. Tomassini V, d’Ambrosio A, Petsas N, et al. The effect of inflammation and its reduction on brain plasticity in multiple sclerosis: MRI evidence. Hum Brain Mapp. 2016;37(7):2431–2445.
  33. Rocca MA, Pagani E, Ghezzi A, et al. Functional cortical changes in patients with multiple sclerosis and nonspecific findings on conventional magnetic resonance imaging scans of the brain. Neuroimage. 2003;19(3):826–836.
  34. Rocca MA, Mezzapesa DM, Falini A, et al. Evidence for axonal pathology and adaptive cortical reorganization in patients at presentation with clinically isolated syndromes suggestive of multiple sclerosis. Neuroimage. 2003;18(4):847–855.
  35. Giorgio A, Battaglini M, Smith SM, De Stefano N. Brain atrophy assessment in multiple sclerosis: Importance and limitations. Neuroimaging Clin N Am. 2008;18(4):675–686,xi.
  36. Chen JT, Collins DL, Atkins HL, Freedman MS, Galal A, Arnold DL; Canadian MS BMT Study Group. Brain atrophy after immunoablation and stem cell transplantation in multiple sclerosis. Neurology. 2006;66(12):1935–1937.
  37. De Stefano N, Arnold DL. Towards a better understanding of pseudoatrophy in the brain of multiple sclerosis patients. Mult Scler. 2015;21(6):675–676.
  38. Zivadinov R. Steroids and brain atrophy in multiple sclerosis. J Neurol Sci. 2005;233(1–2):73–81.
  39. Cohen JA, Barkhof F, Comi G, et al; TRANSFORMS Study Group. Oral fingolimod or intramuscular interferon for relapsing multiple sclerosis. N Engl J Med. 2010;362(5):402–415.
  40. Vidal-Jordana A, Sastre-Garriga J, Pérez-Miralles F, et al. Brain volume loss during the first year of interferon-beta treatment in multiple sclerosis: Baseline inflammation and regional brain volume dynamics. J Neuroimaging. 2016;26(5):532–538.
  41. Gordon N. Apparent cerebral atrophy in patients on treatment with steroids. Dev Med Child Neurol. 1980;22(4):502–506.
  42. Lyen KR, Holland IM, Lyen YC. Reversible cerebral atrophy in infantile spasms caused by corticotrophin. Lancet. 1979;2(8132):37–38.
  43. Filippi M, Rovaris M, Inglese M, et al. Interferon beta-1a for brain tissue loss in patients at presentation with syndromes suggestive of multiple sclerosis: A randomised, double-blind, placebo-controlled trial. Lancet. 2004;364(9444):1489–1496.
  44. Prins M, Schul E, Geurts J, van der Valk P, Drukarch B, van Dam A-M. Pathological differences between white and grey matter multiple sclerosis lesions. Ann N Y Acad Sci. 2015;1351:99–113.
  45. Calabrese M, Agosta F, Rinaldi F, et al. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol. 2009;66(9):1144–1150.
  46. Diker S, Has AC, Kurne A, Göçmen R, Oğuz KK, Karabudak R. The association of cognitive impairment with gray matter atrophy and cortical lesion load in clinically isolated syndrome. Mult Scler Relat Disord. 2016;10:14–21.
  47. Paul F. Pathology and MRI: Exploring cognitive impairment in MS. Acta Neurol Scand. 2016;134(Suppl 200):24–33. doi:10.1111/ane.12649
  48. Cappellani R, Bergsland N, Weinstock-Guttman B, et al. Subcortical deep gray matter pathology in patients with multiple sclerosis is associated with white matter lesion burden and atrophy but not with cortical atrophy: A diffusion tensor MRI study. AJNR Am J Neuroradiol. 2014;35(5):912–919.
  49. Datta S, Staewen TD, Cofield SS, et al; MRI Analysis Center at Houston; CombiRx Investigators Group. Regional gray matter atrophy in relapsing remitting multiple sclerosis: Baseline analysis of multi-center data. Mult Scler Relat Disord. 2015;4(2):124–136.
  50. Sanfilipo MP, Benedict RHB, Weinstock-Guttman B, Bakshi R. Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology. 2006;66(5):685–692.
  51. Henry RG, Shieh M, Okuda DT, Evangelista A, Gorno-Tempini ML, Pelletier D. Regional grey matter atrophy in clinically isolated syndromes at presentation. J Neurol Neurosurg Psychiatry. 2008;79(11):1236–1244.
  52. Azevedo CJ, Overton E, Khadka S, et al. Early CNS neurodegeneration in radiologically isolated syndrome. Neurol Neuroimmunol Neuroinflamm. 2015;2(3):e102.
  53. Dalton CM, Chard DT, Davies GR, et al. Early development of multiple sclerosis is associated with progressive grey matter atrophy in patients presenting with clinically isolated syndromes. Brain. 2004;127(Pt 5):1101–1107.
  54. Kincses ZT, Tóth E, Bankó N, et al. Grey matter atrophy in patients suffering from multiple sclerosis. Ideggyogy Sz. 2014;67:293–300.
  55. Steenwijk MD, Geurts JJG, Daams M, et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain. 2016;139(Pt 1):115–126.
  56. Filippi M, Rocca MA. MRI evidence for multiple sclerosis as a diffuse disease of the central nervous system. J Neurol. 2005;252(Suppl):v16–24.
  57. Calabrese M, Poretto V, Favaretto A, et al. Cortical lesion load associates with progression of disability in multiple sclerosis. Brain. 2012;135(Pt 10):2952–2961.
  58. Chataway J. When confronted by a patient with the radiologically isolated syndrome. Pract Neurol. 2010;10(5):271–277.
  59. Hasan KM, Walimuni IS, Abid H, et al. Multimodal quantitative magnetic resonance imaging of thalamic development and aging across the human lifespan: Implications to neurodegeneration in multiple sclerosis. J Neurosci. 2011;31(46):16826–16832.
  60. Giorgio A, De Stefano N. Clinical use of brain volumetry. J Magn Reson Imaging. 2013;37(1):1–14.
  61. Næss-Schmidt E, Tietze A, Blicher JU, et al. Automatic thalamus and hippocampus segmentation from MP2RAGE: Comparison of publicly available methods and implications for DTI quantification. Int J Comput Assist Radiol Surg. 2016;11(11):1979–1991.
  62. Manjón J V, Coupé P. volBrain: An online MRI brain volumetry system. Front Neuroinform. 2016;10:30.
  63. Hao Y, Wang T, Zhang X, et al. Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation. Hum Brain Mapp. 2014;35(6):2674–2697.
  64. Yeo BTT, Sabuncu MR, Desikan R, Fischl B, Golland P. Effects of registration regularization and atlas sharpness on segmentation accuracy. Med Image Anal. 2008;12(5):603–615.
  65. Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774–781.
  66. Shattuck DW, Prasad G, Mirza M, Narr KL, Toga AW. Online resource for validation of brain segmentation methods. Neuroimage. 2009;45(2):431–439.
  67. Collins DL, Pruessner JC. Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. Neuroimage. 2010;52(4):1355–1366.
  68. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62(2):782–790.