Brain MRI atrophy quantification in MS
From methods to clinical application
Abstract
Patients with the main clinical phenotypes of multiple sclerosis (MS) manifest varying degrees of brain atrophy beyond that of normal aging. Assessment of atrophy helps to distinguish clinically and cognitively deteriorating patients and predicts those who will have a less-favorable clinical outcome over the long term. Atrophy can be measured from brain MRI scans, and many technological improvements have been made over the last few years. Several software tools, with differing requirements on technical ability and levels of operator intervention, are currently available and have already been applied in research or clinical trial settings. Despite this, the measurement of atrophy in routine clinical practice remains an unmet need. After a short summary of the pathologic substrates of brain atrophy in MS, this review attempts to guide the clinician towards a better understanding of the methods currently used for quantifying brain atrophy in this condition. Important physiologic factors that affect brain volume measures are also considered. Finally, the most recent research on brain atrophy in MS is summarized, including whole brain and various compartments thereof (i.e., white matter, gray matter, selected CNS structures). Current methods provide sufficient precision for cohort studies, but are not adequate for confidently assessing changes in individual patients over the scale of months or a few years.
Get full access to this article
View all available purchase options and get full access to this article.
Supplementary Material
File (data_supplement.docx)
- Download
- 15.37 KB
REFERENCES
1.
Dalton CM, Brex PA, Jenkins R, et al. Progressive ventricular enlargement in patients with clinically isolated syndromes is associated with the early development of multiple sclerosis. J Neurol Neurosurg Psychiatry 2002;73:141–147.
2.
Ingle GT, Stevenson VL, Miller DH, Thompson AJ. Primary progressive multiple sclerosis: a 5-year clinical and MR study. Brain 2003;126:2528–2536.
3.
Roosendaal SD, Bendfeldt K, Vrenken H, et al. Grey matter volume in a large cohort of MS patients: relation to MRI parameters and disability. Mult Scler 2011;17:1098–1106.
4.
Fisher E, Lee JC, Nakamura K, Rudick RA. Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol 2008;64:255–265.
5.
Benedict RHB, Weinstock-Guttman B, Fishman I, Sharma J, Tjoa CW, Bakshi R. Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 2004;61:226–230.
6.
Filippi M, Preziosa P, Copetti M, et al. Gray matter damage predicts the accumulation of disability 13 years later. Neurology 2013;81:1759–1767.
7.
Bermel RA, Bakshi R. The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol 2006;5:158–170.
8.
De Stefano N, Airas L, Grigoriadis N, et al. Clinical relevance of brain volume measures in multiple sclerosis. CNS Drugs 2014;28:147–156.
9.
Zivadinov R, Reder AT, Filippi M, et al. Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis. Neurology 2008;71:136–144.
10.
Popescu V, Klaver R, Voorn P, et al. What drives MRI-measured cortical atrophy in multiple sclerosis? Mult Scler 2015;21:1280–1290.
11.
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;74:453–458.
12.
Popescu V, Klaver R, Versteeg A, et al. Postmortem validation of MRI cortical volume measurements in MS. Hum Brain Mapp 2016;37:2223–2233.
13.
Sullivan DC, Obuchowski NA, Kessler LG, et al. Metrology standards for quantitative imaging biomarkers. Radiology 2015;277:813–825.
14.
Deoni SC. Quantitative relaxometry of the brain. Top Magn Reson Imaging 2010;21:101–113.
15.
Nakamura K, Brown RA, Araujo D, Narayanan S, Arnold DL. Correlation between brain volume change and T2 relaxation time induced by dehydration and rehydration: implications for monitoring atrophy in clinical studies. Neuroimage Clin 2014;6:166–170.
16.
Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17:479–489.
17.
Tustison NJ, Cook PA, Klein A, et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage 2014;99:166–179.
18.
Enzinger C, Fazekas F, Matthews PM, et al. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. Neurology 2005;64:1704–1711.
19.
Nakamura K, Brown RA, Narayanan S, Collins DL, Arnold DL; Alzheimer's Disease Neuroimaging Initiative. Diurnal fluctuations in brain volume: statistical analyses of MRI from large populations. Neuroimage 2015;118:126–132.
20.
De Stefano N, Giorgio A, Battaglini M, et al. Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology 2010;74:1868–1876.
21.
De Stefano N, Stromillo ML, Giorgio A, et al. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry 2016;87:93–99.
22.
Rao SM, Leo GJ, Haughton VM, St Aubin-Faubert P, Bernardin L. Correlation of magnetic resonance imaging with neuropsychological testing in multiple sclerosis. Neurology 1989;39:161–166.
23.
Bakshi R, Czarnecki D, Shaikh ZA, et al. Brain MRI lesions and atrophy are related to depression in multiple sclerosis. Neuroreport 2000;11:1153–1158.
24.
Fisher E, Rudick RA, Simon JH, et al. Eight-year follow-up study of brain atrophy in patients with MS. Neurology 2002;59:1412–1420.
25.
Gauthier SA, Mandel M, Guttmann CR, et al. Predicting short-term disability in multiple sclerosis. Neurology 2007;68:2059–2065.
26.
Radue EW, Barkhof F, Kappos L, et al. Correlation between brain volume loss and clinical and MRI outcomes in multiple sclerosis. Neurology 2015;84:784–793.
27.
Chard DT, Brex PA, Ciccarelli O, et al. The longitudinal relation between brain lesion load and atrophy in multiple sclerosis: a 14 year follow up study. J Neurol Neurosurg Psychiatry 2003;74:1551–1554.
28.
Cheriyan J, Kim S, Wolansky LJ, Cook SD, Cadavid D. Impact of inflammation on brain volume in multiple sclerosis. Arch Neurol 2012;69:82–88.
29.
Enzinger C, Ropele S, Smith S, et al. Accelerated evolution of brain atrophy and “black holes” in MS patients with APOE-epsilon 4. Ann Neurol 2004;55:563–569.
30.
van der Walt A, Stankovich J, Bahlo M, et al. Apolipoprotein genotype does not influence MS severity, cognition, or brain atrophy. Neurology 2009;73:1018–1025.
31.
Kappus N, Weinstock-Guttman B, Hagemeier J, et al. Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis. J Neurol Neurosurg Psychiatry 2016;87:181–187.
32.
Saidha S, Al-Louzi O, Ratchford JN, et al. Optical coherence tomography reflects brain atrophy in multiple sclerosis: a four-year study. Ann Neurol 2015;78:801–813.
33.
Lukas C, Sombekke MH, Bellenberg B, et al. Relevance of spinal cord abnormalities to clinical disability in multiple sclerosis: MR imaging findings in a large cohort of patients. Radiology 2013;269:542–552.
34.
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;127:1101–1107.
35.
Amato MP, Portaccio E, Stromillo ML, et al. Cognitive assessment and quantitative magnetic resonance metrics can help to identify benign multiple sclerosis. Neurology 2008;71:632–638.
36.
Mesaros S, Rovaris M, Pagani E, et al. A magnetic resonance imaging voxel-based morphometry study of regional gray matter atrophy in patients with benign multiple sclerosis. Arch Neurol 2008;65:1223–1230.
37.
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;42:315–322.
38.
Eshaghi A, Bodini B, Ridgway GR, et al. Temporal and spatial evolution of grey matter atrophy in primary progressive multiple sclerosis. Neuroimage 2014;86:257–264.
39.
Riccitelli G, Rocca MA, Pagani E, et al. Cognitive impairment in multiple sclerosis is associated to different patterns of gray matter atrophy according to clinical phenotype. Hum Brain Mapp 2011;32:1535–1543.
40.
Sailer M, Fischl B, Salat D, et al. Focal thinning of the cerebral cortex in multiple sclerosis. Brain 2003;126:1734–1744.
41.
Longoni G, Rocca MA, Pagani E, et al. Deficits in memory and visuospatial learning correlate with regional hippocampal atrophy in MS. Brain Struct Funct 2015;220:435–444.
42.
Rocca MA, Longoni G, Pagani E, et al. In vivo evidence of hippocampal dentate gyrus expansion in multiple sclerosis. Hum Brain Mapp 2015;36:4702–4713.
43.
Rocca MA, Preziosa P, Mesaros S, et al. Clinically isolated syndrome suggestive of multiple sclerosis: dynamic patterns of gray and white matter changes: a 2-year MR imaging study. Radiology 2016;278:841–853.
44.
Charil A, Dagher A, Lerch JP, Zijdenbos AP, Worsley KJ, Evans AC. Focal cortical atrophy in multiple sclerosis: relation to lesion load and disability. Neuroimage 2007;34:509–517.
45.
Battaglini M, Giorgio A, Stromillo ML, et al. Voxel-wise assessment of progression of regional brain atrophy in relapsing-remitting multiple sclerosis. J Neurol Sci 2009;282:55–60.
46.
Damasceno A, Damasceno BP, Cendes F. No evidence of disease activity in multiple sclerosis: implications on cognition and brain atrophy. Mult Scler 2016;22:64–72.
47.
Jehna M, Pirpamer L, Khalil M, et al. Periventricular lesions correlate with cortical thinning in multiple sclerosis. Ann Neurol 2015;78:530–539.
48.
Riccitelli G, Rocca MA, Forn C, Colombo B, Comi G, Filippi M. Voxelwise assessment of the regional distribution of damage in the brains of patients with multiple sclerosis and fatigue. AJNR Am J Neuroradiol 2011;32:874–879.
49.
Bodini B, Chard D, Altmann DR, et al. White and gray matter damage in primary progressive MS: the chicken or the egg? Neurology 2016;86:170–176.
50.
van de Pavert SH, Muhlert N, Sethi V, et al. DIR-visible grey matter lesions and atrophy in multiple sclerosis: partners in crime? J Neurol Neurosurg Psychiatry 2016;87:461–467.
51.
Tiberio M, Chard DT, Altmann DR, et al. Gray and white matter volume changes in early RRMS: a 2-year longitudinal study. Neurology 2005;64:1001–1007.
52.
Cifelli A, Arridge M, Jezzard P, Esiri MM, Palace J, Matthews PM. Thalamic neurodegeneration in multiple sclerosis. Ann Neurol 2002;52:650–653.
53.
Batista S, Zivadinov R, Hoogs M, et al. Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. J Neurol 2012;259:139–146.
54.
Schoonheim MM, Popescu V, Rueda Lopes FC, et al. Subcortical atrophy and cognition: sex effects in multiple sclerosis. Neurology 2012;79:1754–1761.
55.
Bisecco A, Rocca MA, Pagani E, et al. Connectivity-based parcellation of the thalamus in multiple sclerosis and its implications for cognitive impairment: a multicenter study. Hum Brain Mapp 2015;36:2809–2825.
56.
Modica CM, Bergsland N, Dwyer MG, et al. Cognitive reserve moderates the impact of subcortical gray matter atrophy on neuropsychological status in multiple sclerosis. Mult Scler 2016;22:36–42.
57.
Bergsland N, Zivadinov R, Dwyer MG, Weinstock-Guttman B, Benedict RH. Localized atrophy of the thalamus and slowed cognitive processing speed in MS patients. Mult Scler 2016;22:1327–1336.
58.
Zivadinov R, Havrdova E, Bergsland N, et al. Thalamic atrophy is associated with development of clinically definite multiple sclerosis. Radiology 2013;268:831–841.
59.
Rocca MA, Mesaros S, Pagani E, Sormani MP, Comi G, Filippi M. Thalamic damage and long-term progression of disability in multiple sclerosis. Radiology 2010;257:463–469.
60.
Muhlau M, Buck D, Forschler A, et al. White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI. Mult Scler 2013;19:1485–1492.
Information & Authors
Information
Published In
Copyright
© 2016 American Academy of Neurology.
Publication History
Received: June 29, 2016
Accepted: October 18, 2016
Published online: December 16, 2016
Published in issue: January 24, 2017
Disclosure
M.A. Rocca received speakers' honoraria from Biogen Idec, Novartis, Genzyme, Sanofi-Aventis, Teva Pharmaceutical Industries, and Excemed and receives research support from the Italian Ministry of Health and Fondazione Italiana Sclerosi Multipla. M. Battaglini reports no disclosures relevant to the manuscript. R. Benedict receives research support from Accorda, Novartis, Genzyme, Biogen, and Mallinckrodt Pharmaceuticals, is on the speakers' bureau for EMD Serono (designing CME courses), consults for Biogen, Genentech, Genzyme, and Novartis, and receives royalties from Psychological Assessment Resources. N. De Stefano has received honoraria from Schering, Biogen-Idec, Teva, Novartis, Genzyme, and Merck Serono S.A. for consulting services, and speaking and travel support. He serves on advisory boards for Biogen-Idec Merck Serono S.A. and Novartis. J. Geurts serves on the editorial board of Multiple Sclerosis Journal and Neurology®; received speaker honoraria from Novartis, Biogen, Genzyme, Merck Serono, and Teva; and served on the board of the Dutch MS Research Foundation and the progressive MS Alliance. R. Henry has received grants from Stem Cells Inc, Hoffmann La Roche, and Genentech. Consultancies have been compensated for Novartis, Abbvie, and Mallinckrodt. M. Horsfield is an employee and stockholder of Xinapse Systems Ltd. M. Jenkinson receives royalties from ISIS (University of Oxford's commercialization company) for licensing of FSL to commercial enterprises and received speaker honoraria from Novartis. E. Pagani reports no disclosures relevant to the manuscript. M. Filippi is Editor-in-Chief of the Journal of Neurology; serves on the scientific advisory board for Teva Pharmaceutical Industries; has received compensation for consulting services and/or speaking activities from Biogen Idec, Excemed, Novartis, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Teva Pharmaceutical Industries, Novartis, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, Cure PSP, Alzheimer's Drug Discovery Foundation (ADDF), the Jacques and Gloria Gossweiler Foundation (Switzerland), and ARiSLA (Fondazione Italiana di Ricerca per la SLA). Go to Neurology.org for full disclosures.
Study Funding
This article reports the conclusions of the Eighteenth Advanced Course on Magnetic Resonance Techniques in Multiple Sclerosis, which was held in Milan on March 19–20, 2015, and subsequent discussion among meeting participants. The course was supported by an unrestricted educational grant from Biogen Idec. The funding source had no role in the preparation of the manuscript.
Authors
Author Contributions
M.F. drafted the introduction and conclusions as well as the global measurement section. M.A.R. drafted the gray matter and white matter atrophy section. J.J.G.G. drafted the pathologic basis section. M.A.H., R.G.H., M.J., and E.P drafted the methodologic issues section. N.D.S. and M.B. drafted the pitfalls and limitations section. R.H.B.B. drafted the deep gray matter nuclei section. The complete article was commented on, revised, and approved by all authors.
Metrics & Citations
Metrics
Citation information is sourced from Crossref Cited-by service.
Citations
Download Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Select your manager software from the list below and click Download.
Cited By
- Multiple Sclerosis–Specific Reference Curves for Brain Volumes to Explain Disease Severity, Neurology, 104, 10, (2025)./doi/10.1212/WNL.0000000000213618
- Brain Volume Measures in Adults with MOG-Antibody-Associated Disease: A Longitudinal Multicenter Study, Journal of Clinical Medicine, 14, 7, (2445), (2025).https://doi.org/10.3390/jcm14072445
- Extended Technical and Clinical Validation of Deep Learning‐Based Brainstem Segmentation for Application in Neurodegenerative Diseases, Human Brain Mapping, 46, 3, (2025).https://doi.org/10.1002/hbm.70141
- Use of magnetic resonance imaging and quantitative imaging reports in clinical care of multiple sclerosis, Handbook of Imaging in Multiple Sclerosis, (93-121), (2025).https://doi.org/10.1016/B978-0-323-95739-7.00014-9
- Spinal cord imaging in multiple sclerosis, Handbook of Imaging in Multiple Sclerosis, (175-188), (2025).https://doi.org/10.1016/B978-0-323-95739-7.00006-X
- Neuroimaging markers and disability scales in multiple sclerosis: A systematic review and meta-analysis, PLOS ONE, 19, 12, (e0312421), (2024).https://doi.org/10.1371/journal.pone.0312421
- Utility of icobrain for brain volumetry in multiple sclerosis clinical practice, Multiple Sclerosis and Related Disorders, 92, (106148), (2024).https://doi.org/10.1016/j.msard.2024.106148
- Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning, Computers in Biology and Medicine, 183, (109289), (2024).https://doi.org/10.1016/j.compbiomed.2024.109289
- Microbiota and Neurodegenerative and Brain Diseases, Dysbiosis, (23-39), (2024).https://doi.org/10.1007/978-3-031-65650-7_3
- Radiological Changes in the Spinal Cord and Brain of Patients with HTLV-1-Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP), Pathogens, 13, 11, (920), (2024).https://doi.org/10.3390/pathogens13110920
- See more
Loading...
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Personal login Institutional LoginPurchase Options
The neurology.org payment platform is currently offline. Our technical team is working as quickly as possible to restore service.
If you need immediate support or to place an order, please call or email customer service:
- 1-800-638-3030 for U.S. customers - 8:30 - 7 pm ET (M-F)
- 1-301-223-2300 for customers outside the U.S. - 8:30 - 7 pm ET (M-F)
- [email protected]
We appreciate your patience during this time and apologize for any inconvenience.