Address reprint requests to: K.H.C., Department of Medicine, University of Hong Kong, 4/F, Professorial Block, Queen Mary Hospital, 102 Pok Fu Lam Road, Hong Kong. E-mail: [email protected]Search for more papers by this author

Chi-Yan Lee MBBS,

Chi-Yan Lee MBBS

Department of Medicine, University of Hong Kong, Hong Kong

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Henry Ka-Fung Mak MD,

Henry Ka-Fung Mak MD

Department of Diagnostic Radiology, University of Hong Kong, Hong Kong

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Pui-Wai Chiu PhD,

Pui-Wai Chiu PhD

Department of Diagnostic Radiology, University of Hong Kong, Hong Kong

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Hing-Chiu Chang PhD,

Hing-Chiu Chang PhD

Department of Diagnostic Radiology, University of Hong Kong, Hong Kong

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Frederik Barkhof MD, PhD,

Frederik Barkhof MD, PhD

Department of Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, the Netherlands

Institutes of Neurology and Healthcare Engineering, University College London, London, UK

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Koon-Ho Chan MD, PhD,

Corresponding Author

Koon-Ho Chan MD, PhD

[email protected]

Department of Medicine, University of Hong Kong, Hong Kong

First published: 09 October 2017

https://doi.org/10.1002/jmri.25866

Citations: 22

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Abstract

Background

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are central nervous system (CNS) inflammatory demyelinating disorders. It is clinically important to distinguish MS from NMOSD, as treatment and prognosis differ. Brainstem involvement is common in both disorders.

Purpose

To investigate whether the patterns of brainstem atrophy on volumetric analysis in MS and NMOSD were different and correlated with clinical disability.

Study Type

Case–control cross-sectional study.

Subjects

In all, 17 MS, 13 NMOSD, and 18 healthy control (HC) subjects were studied.

Field Strength/Sequence

T₁-weighted and T₂w spin-echo images were acquired with a 3T scanner.

Assessment

Semiautomated segmentation and volumetric measurement of brainstem regions were performed. Anatomical information was obtained from whole brain T₁w images using a 3D magnetization-prepared rapid gradient-echo (MPRAGE) imaging sequence (TR/TE/T: 7.0/3.2/800 msec, voxel size: 1 × 1 × 1 mm³, scan time: 10 min 41 sec).

Statistical Tests

Independent samples t-test, Mann–Whitney U-test, partial correlation, and multiple regression analysis.

Results

Baseline characteristics were similar across the three groups, without significant difference in disease duration (P = 0.354) and EDSS score (P = 0.159) between MS and NMOSD subjects. Compared to HC, MS subjects had significantly smaller normalized whole brainstem (−5.2%, P = 0.027), midbrain (−8.3%, P = 0.0001), and pons volumes (−5.9%, P = 0.048), while only the normalized medulla volume was significantly smaller in NMOSD subjects compared to HC (−8.5% vs. HC, P = 0.024). Normalized midbrain volume was significantly smaller in MS compared to NMOSD subjects (−5.0%, P = 0.014), whereas normalized medulla volume was significantly smaller in NMOSD compared to MS subjects (−8.1%, P = 0.032). Partial correlations and multiple regression analysis revealed that smaller normalized whole brainstem, pons, and medulla oblongata volumes were associated with greater disability on the Expanded Disability Status Scale (EDSS), Functional System Score (FSS)-brainstem and FSS-cerebellar in NMOSD subjects.

Data Conclusion

Differential patterns of brainstem atrophy were observed, with the midbrain being most severely affected followed by pons in MS, whereas only the medulla oblongata was affected in NMOSD.

Level of Evidence: 2

Technical Efficacy: Stage 3

J. Magn. Reson. Imaging 2018;47:1601–1609.

Conflict of Interest

On behalf of all authors, the corresponding author states that there are no conflicts of interest.

References

1 Compston A, Coles A. Multiple sclerosis. Lancet 2008; 372: 1502–1517.
10.1016/S0140-6736(08)61620-7
CAS PubMed Web of Science® Google Scholar
2 Trapp BD, Peterson J, Ransohoff RM, Rudick R, Mork S, Bo L. Axonal transection in the lesions of multiple sclerosis. N Engl J Med 1998; 338: 278–285.
10.1056/NEJM199801293380502
CAS PubMed Web of Science® Google Scholar
3 Lucchinetti CF, Popescu BF, Bunyan RF, et al. Inflammatory cortical demyelination in early multiple sclerosis. N Engl J Med 2011; 365: 2188–2197.
10.1056/NEJMoa1100648
CAS PubMed Web of Science® Google Scholar
4 Wingerchuk DM, Lennon VA, Lucchinetti CF, Pittock SJ, Weinshenker BG. The spectrum of neuromyelitis optica. Lancet Neurol. 2007; 6: 805–815.
10.1016/S1474-4422(07)70216-8
CAS PubMed Web of Science® Google Scholar
5 Kim HJ, Paul F, Lana-Peixoto MA, et al. MRI characteristics of neuromyelitis optica spectrum disorder: an international update. Neurology 2015; 84: 1165–1173.
10.1212/WNL.0000000000001367
PubMed Web of Science® Google Scholar
6 Wingerchuk DM, Banwell B, Bennett JL, et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 2015; 85: 177–189.
10.1212/WNL.0000000000001729
PubMed Web of Science® Google Scholar
7 Lennon VA, Kryzer TJ, Pittock SJ, Verkman AS, Hinson SR. IgG marker of optic-spinal multiple sclerosis binds to the aquaporin-4 water channel. J Exp Med 2005; 202: 473–477.
10.1084/jem.20050304
CAS PubMed Web of Science® Google Scholar
8 Roemer SF, Parisi JE, Lennon VA, et al. Pattern-specific loss of aquaporin-4 immunoreactivity distinguishes neuromyelitis optica from multiple sclerosis. Brain 2007; 130(Pt 5): 1194–1205.
10.1093/brain/awl371
PubMed Web of Science® Google Scholar
9 Pittock SJ, Lucchinetti CF. Neuromyelitis optica and the evolving spectrum of autoimmune aquaporin-4 channelopathies: a decade later. Ann N Y Acad Sci 2016; 1366: 20–39.
10.1111/nyas.12794
PubMed Web of Science® Google Scholar
10 Chan KH, Lee R, Lee JC, et al. Central nervous system inflammatory demyelinating disorders among Hong Kong Chinese. J Neuroimmunol 2013; 262: 100–105.
10.1016/j.jneuroim.2013.06.004
CAS PubMed Web of Science® Google Scholar
11 Palace J, Leite MI, Nairne A, Vincent A. Interferon beta treatment in neuromyelitis optica: increase in relapses and aquaporin 4 antibody titers. Arch Neurol 2010; 67: 1016–1017.
10.1001/archneurol.2010.188
PubMed Web of Science® Google Scholar
12 Min JH, Kim BJ, Lee KH. Development of extensive brain lesions following fingolimod (FTY720) treatment in a patient with neuromyelitis optica spectrum disorder. Mult Scler 2012; 18: 113–115.
10.1177/1352458511431973
CAS PubMed Web of Science® Google Scholar
13 Kleiter I, Hellwig K, Berthele A, et al. Failure of natalizumab to prevent relapses in neuromyelitis optica. Arch Neurol 2012; 69: 239–245.
10.1001/archneurol.2011.216
PubMed Web of Science® Google Scholar
14 Jarius S, Wildemann B, Paul F. Neuromyelitis optica: clinical features, immunopathogenesis and treatment. Clin Exp Immunol 2014; 176: 149–164.
10.1111/cei.12271
CAS PubMed Web of Science® Google Scholar
15 Chan KH, Tsang KL, Ho PW, et al. Clinical outcome of relapsing remitting multiple sclerosis among Hong Kong Chinese. Clin Neurol Neurosurg 2011; 113: 617–622.
10.1016/j.clineuro.2011.04.013
CAS PubMed Web of Science® Google Scholar
16 Chan KH, Tse CT, Chung CP, et al. Brain involvement in neuromyelitis optica spectrum disorders. Arch Neurol 2011; 68: 1432–1439.
10.1001/archneurol.2011.249
PubMed Web of Science® Google Scholar
17 Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011; 69: 292–302.
10.1002/ana.22366
PubMed Web of Science® Google Scholar
18 Wingerchuk DM, Lennon VA, Pittock SJ, Lucchinetti CF, Weinshenker BG. Revised diagnostic criteria for neuromyelitis optica. Neurology 2006; 66: 1485–1489.
10.1212/01.wnl.0000216139.44259.74
CAS PubMed Web of Science® Google Scholar
19 Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012; 61: 1402–1418.
10.1016/j.neuroimage.2012.02.084
PubMed Web of Science® Google Scholar
20 Fischl B. FreeSurfer. Neuroimage 2012; 62: 774–781.
10.1016/j.neuroimage.2012.01.021
PubMed Web of Science® Google Scholar
21 Iglesias JE, Van Leemput K, Bhatt P, et al. Bayesian segmentation of brainstem structures in MRI. Neuroimage 2015; 113: 184–195.
10.1016/j.neuroimage.2015.02.065
PubMed Web of Science® Google Scholar
22 Mathalon DH, Sullivan EV, Rawles JM, Pfefferbaum A. Correction for head size in brain-imaging measurements. Psychiatry Res 1993; 50: 121–139.
10.1016/0925-4927(93)90016-B
CAS PubMed Web of Science® Google Scholar
23 Sanfilipo MP, Benedict RH, Zivadinov R, Bakshi R. Correction for intracranial volume in analysis of whole brain atrophy in multiple sclerosis: the proportion vs. residual method. Neuroimage 2004; 22: 1732–1743.
10.1016/j.neuroimage.2004.03.037
PubMed Web of Science® Google Scholar
24 Barnes J, Ridgway GR, Bartlett J, et al. Head size, age and gender adjustment in MRI studies: a necessary nuisance? Neuroimage 2010; 53: 1244–1255.
10.1016/j.neuroimage.2010.06.025
PubMed Web of Science® Google Scholar
25 Malone IB, Leung KK, Clegg S, et al. Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. Neuroimage 2015; 104: 366–372.
10.1016/j.neuroimage.2014.09.034
PubMed Web of Science® Google Scholar
26 McAuliffe MJ LF, McGarry D, Gandler W, Csaky K, Trus BL. Medical image processing, analysis & visualization in clinical research. IEEE Comput Based Med Syst 2001: 381–386.
Google Scholar
27 Popescu BF, Lennon VA, Parisi JE, et al. Neuromyelitis optica unique area postrema lesions: nausea, vomiting, and pathogenic implications. Neurology 2011; 76: 1229–1237.
10.1212/WNL.0b013e318214332c
PubMed Web of Science® Google Scholar
28 Wang Y, Zhang L, Zhang B, et al. Comparative clinical characteristics of neuromyelitis optica spectrum disorders with and without medulla oblongata lesions. J Neurol 2014; 261: 954–962.
10.1007/s00415-014-7298-7
PubMed Web of Science® Google Scholar
29 Blanc F, Noblet V, Jung B, et al. White matter atrophy and cognitive dysfunctions in neuromyelitis optica. PLoS One 2012; 7:e33878.
10.1371/journal.pone.0033878
Web of Science® Google Scholar
30 Chanson JB, Lamy J, Rousseau F, et al. White matter volume is decreased in the brain of patients with neuromyelitis optica. Eur J Neurol 2013; 20: 361–367.
10.1111/j.1468-1331.2012.03867.x
PubMed Web of Science® Google Scholar
31 Duan Y, Liu Y, Liang P, et al. Comparison of grey matter atrophy between patients with neuromyelitis optica and multiple sclerosis: a voxel-based morphometry study. Eur J Radiol 2012; 81: e110–114.
10.1016/j.ejrad.2011.01.065
PubMed Web of Science® Google Scholar
32 Liu C, Edwards S, Gong Q, Roberts N, Blumhardt LD. Three dimensional MRI estimates of brain and spinal cord atrophy in multiple sclerosis. J Neurol Neurosurg Psychiatry 1999; 66: 323–330.
10.1136/jnnp.66.3.323
CAS PubMed Web of Science® Google Scholar
33 Daams M, Steenwijk MD, Wattjes MP, et al. Unraveling the neuroimaging predictors for motor dysfunction in long-standing multiple sclerosis. Neurology 2015; 85: 248–255.
10.1212/WNL.0000000000001756
PubMed Web of Science® Google Scholar
34 Chivers TR, Constantinescu CS, Tench CR. MRI-based measurement of brain stem cross-sectional area in relapsing-remitting multiple sclerosis. J Neuroimaging 2015; 25: 1002–1006.
10.1111/jon.12244
PubMed Web of Science® Google Scholar
35 Liptak Z, Berger AM, Sampat MP, et al. Medulla oblongata volume: a biomarker of spinal cord damage and disability in multiple sclerosis. AJNR Am J Neuroradiol 2008; 29: 1465–1470.
10.3174/ajnr.A1162
CAS PubMed Web of Science® Google Scholar
36 Fisniku LK, Brex PA, Altmann DR, et al. Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain 2008; 131(Pt 3): 808–817.
10.1093/brain/awm329
CAS PubMed Web of Science® Google Scholar
37 Filippi M. MRI measures of neurodegeneration in multiple sclerosis: implications for disability, disease monitoring, and treatment. J Neurol 2015; 262: 1–6.
10.1007/s00415-014-7340-9
CAS PubMed Web of Science® Google Scholar
38 Jurynczyk M, Craner M, Palace J. Overlapping CNS inflammatory diseases: differentiating features of NMO and MS. J Neurol Neurosurg Psychiatry 2015; 86: 20–25.
10.1136/jnnp-2014-308984
PubMed Web of Science® Google Scholar
39 Eshaghi A, Riyahi-Alam S, Saeedi R, et al. Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis. Neuroimage Clin 2015; 7: 306–314.
10.1016/j.nicl.2015.01.001
PubMed Web of Science® Google Scholar
40 Lukas C, Knol DL, Sombekke MH, et al. Cervical spinal cord volume loss is related to clinical disability progression in multiple sclerosis. J Neurol Neurosurg Psychiatry 2015; 86: 410–418.
10.1136/jnnp-2014-308021
PubMed Web of Science® Google Scholar

Citing Literature

Volume47, Issue6

June 2018

Pages 1601-1609

Differential brainstem atrophy patterns in multiple sclerosis and neuromyelitis optica spectrum disorders

Abstract

Background

Purpose

Study Type

Subjects

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Conflict of Interest

References

Citing Literature

References

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Differential brainstem atrophy patterns in multiple sclerosis and neuromyelitis optica spectrum disorders

Abstract

Background

Purpose

Study Type

Subjects

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Conflict of Interest

References

Citing Literature

References

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