Skip to main content

Advertisement

SpringerLink
Log in

Patterns of regional brain volume loss in multiple sclerosis: a cluster analysis

  • Original Communication
  • Published:
Journal of Neurology Aims and scope Submit manuscript

Abstract

Objectives

Although whole and individual regional brain volume loss have been separately reported to correlate with disability in multiple sclerosis (MS), hierarchical cluster analyses of the whole and regional brain to find their pattern in MS are few.

Methods

We cross-sectionally conducted high-resolution, T1-weighted volumetric MRI examinations in 75 MS patients and 21 healthy controls (HCs) to measure the volumes of whole brain and a total of 56 brain regions of interest. Using a hierarchical cluster analysis with multivariate imaging data, we classified the patients into clusters according to their brain-volume patterns. Principal component analysis was also applied. Clinical features and brain volumes were then compared among the MS clusters.

Results

The MS patients were categorized into three major clusters (Clusters 1, 2, and 3) with increasing disability in that order. Principal component analysis also identified Clusters 1, 2 and 3. Whole brain volume and supratentorial regional brain volumes, including thalamus and corpus callosum, decreased severely in Cluster 3 and moderately in Cluster 2, while equally preserved in Cluster 1 and the HCs. Only the volumes of the ventral diencephalon and T1 white matter hypointensities significantly differed in Clusters 1, 2 and 3 and HCs. In contrast, the volumes of the cerebellar cortex and brainstem were significantly different between Clusters 3 and 1, whereas there were no significant differences between Clusters 1 and 2 and Clusters 2 and 3.

Conclusion

We identified brain regions that exhibit different degree of atrophy in a background of global brain atrophy in MS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Reich DS, Lucchinetti CF, Calabresi PA (2018) Multiple sclerosis. N Engl J Med 378(2):169–180. https://doi.org/10.1056/NEJMra1401483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Vigeveno RM, Wiebenga OT, Wattjes MP, Geurts JJ, Barkhof F (2012) Shifting imaging targets in multiple sclerosis: from inflammation to neurodegeneration. J Magn Reson Imaging 36(1):1–19. https://doi.org/10.1002/jmri.23578

    Article  PubMed  Google Scholar 

  3. Rocca MA, Comi G, Filippi M (2017) The role of T1-weighted derived measures of neurodegeneration for assessing disability progression in multiple sclerosis. Front Neurol 8:433. https://doi.org/10.3389/fneur.2017.00433

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rocca MA, Battaglini M, Benedict RH, De Stefano N, Geurts JJ, Henry RG, Horsfield MA, Jenkinson M, Pagani E, Filippi M (2017) Brain MRI atrophy quantification in MS: from methods to clinical application. Neurology 88(4):403–413. https://doi.org/10.1212/wnl.0000000000003542

    Article  PubMed  PubMed Central  Google Scholar 

  5. Eshaghi A, Prados F, Brownlee W, Altmann DR, Tur C, Cardoso MJ, De Angelis F, van de Pavert SH, Cawley N, De Stefano N, Stromillo ML, Battaglini M, Ruggieri S, Gasperini C, Filippi M, Rocca MA, Rovira A, Sastre-Garriga J, Vrenken H, Leurs CE, Killestein J, Pirpamer L, Enzinger C, Ourselin S, Wheeler-Kingshott C, Chard D, Thompson AJ, Alexander DC, Barkhof F, Ciccarelli O (2018) Deep grey matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol. https://doi.org/10.1002/ana.25145

    Article  PubMed  PubMed Central  Google Scholar 

  6. Vollmer T, Huynh L, Kelley C, Galebach P, Signorovitch J, DiBernardo A, Sasane R (2016) Relationship between brain volume loss and cognitive outcomes among patients with multiple sclerosis: a systematic literature review. Neurol Sci 37(2):165–179. https://doi.org/10.1007/s10072-015-2400-1

    Article  PubMed  Google Scholar 

  7. Granberg T, Martola J, Bergendal G, Shams S, Damangir S, Aspelin P, Fredrikson S, Kristoffersen-Wiberg M (2015) Corpus callosum atrophy is strongly associated with cognitive impairment in multiple sclerosis: results of a 17-year longitudinal study. Mult Scler (Houndmills, Basingstoke, England) 21(9):1151–1158. https://doi.org/10.1177/1352458514560928

    Article  Google Scholar 

  8. Schoonheim MM, Hulst HE, Brandt RB, Strik M, Wink AM, Uitdehaag BM, Barkhof F, Geurts JJ (2015) Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology 84(8):776–783. https://doi.org/10.1212/wnl.0000000000001285

    Article  PubMed  Google Scholar 

  9. Weier K, Till C, Fonov V, Yeh EA, Arnold DL, Banwell B, Collins DL (2016) Contribution of the cerebellum to cognitive performance in children and adolescents with multiple sclerosis. Mult Scler (Houndmills, Basingstoke, England) 22(5):599–607. https://doi.org/10.1177/1352458515595132

    Article  Google Scholar 

  10. Whitwell JL, Przybelski SA, Weigand SD, Ivnik RJ, Vemuri P, Gunter JL, Senjem ML, Shiung MM, Boeve BF, Knopman DS, Parisi JE, Dickson DW, Petersen RC, Jack CR Jr, Josephs KA (2009) Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: a cluster analysis study. Brain J Neurol 132(Pt 11):2932–2946. https://doi.org/10.1093/brain/awp232

    Article  Google Scholar 

  11. Noh Y, Jeon S, Lee JM, Seo SW, Kim GH, Cho H, Ye BS, Yoon CW, Kim HJ, Chin J, Park KH, Heilman KM, Na DL (2014) Anatomical heterogeneity of Alzheimer disease: based on cortical thickness on MRIs. Neurology 83(21):1936–1944. https://doi.org/10.1212/wnl.0000000000001003

    Article  PubMed  PubMed Central  Google Scholar 

  12. Uribe C, Segura B, Baggio HC, Abos A, Marti MJ, Valldeoriola F, Compta Y, Bargallo N, Junque C (2016) Patterns of cortical thinning in nondemented Parkinson's disease patients. Mov Disord 31(5):699–708. https://doi.org/10.1002/mds.26590

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O'Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302. https://doi.org/10.1002/ana.22366

    Article  PubMed  PubMed Central  Google Scholar 

  14. Fischl B, Liu A, Dale AM (2001) Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans Med Imaging 20(1):70–80. https://doi.org/10.1109/42.906426

    Article  CAS  PubMed  Google Scholar 

  15. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis I. Segmentation and surface reconstruction. NeuroImage 9(2):179–194. https://doi.org/10.1006/nimg.1998.0395

    Article  CAS  PubMed  Google Scholar 

  16. Segonne F, Pacheco J, Fischl B (2007) Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans Med Imaging 26(4):518–529. https://doi.org/10.1109/tmi.2006.887364

    Article  PubMed  Google Scholar 

  17. Dale AM, Sereno MI (1993) Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: a Linear Approach. J Cogn Neurosci 5(2):162–176. https://doi.org/10.1162/jocn.1993.5.2.162

    Article  CAS  PubMed  Google Scholar 

  18. Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97(20):11050–11055. https://doi.org/10.1073/pnas.200033797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355

    Article  CAS  PubMed  Google Scholar 

  20. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Statist Assoc 58(301):236–244. https://doi.org/10.2307/2282967

    Article  Google Scholar 

  21. Azevedo CJ, Overton E, Khadka S, Buckley J, Liu S, Sampat M, Kantarci O, Lebrun FC, Siva A, Okuda DT, Pelletier D (2015) Early CNS neurodegeneration in radiologically isolated syndrome. Neurol (R) Neuroimmunol Neuroinflamm 2(3):e102. https://doi.org/10.1212/nxi.0000000000000102

    Article  Google Scholar 

  22. Jain S, Sima DM, Ribbens A, Cambron M, Maertens A, Van Hecke W, De Mey J, Barkhof F, Steenwijk MD, Daams M, Maes F, Van Huffel S, Vrenken H, Smeets D (2015) Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. NeuroImage Clin 8:367–375. https://doi.org/10.1016/j.nicl.2015.05.003

    Article  PubMed  PubMed Central  Google Scholar 

  23. Akaishi T, Nakashima I, Mugikura S, Aoki M, Fujihara K (2017) Whole brain and grey matter volume of Japanese patients with multiple sclerosis. J Neuroimmunol 306:68–75. https://doi.org/10.1016/j.jneuroim.2017.03.009

    Article  CAS  PubMed  Google Scholar 

  24. Fujimori J, Baba T, Meguro Y, Nakashima I, Mori E, Fujihara K, Aoki M (2015) Comparison of the rao brief repeatable neuropsychological battery with wechsler adult intelligence scale-III and Wechsler Memory Scale-revised in Japanese patients with multiple sclerosis. Clin Exp Neuroimmunol 6(3):306–308

    Article  Google Scholar 

  25. De Stefano N, Airas L, Grigoriadis N, Mattle HP, O'Riordan J, Oreja-Guevara C, Sellebjerg F, Stankoff B, Walczak A, Wiendl H, Kieseier BC (2014) Clinical relevance of brain volume measures in multiple sclerosis. CNS Drugs 28(2):147–156. https://doi.org/10.1007/s40263-014-0140-z

    Article  PubMed  Google Scholar 

  26. Tao G, Datta S, He R, Nelson F, Wolinsky JS, Narayana PA (2009) Deep gray matter atrophy in multiple sclerosis: a tensor based morphometry. J Neurol Sci 282(1–2):39–46. https://doi.org/10.1016/j.jns.2008.12.035

    Article  PubMed  PubMed Central  Google Scholar 

  27. Riccitelli G, Rocca MA, Pagani E, Martinelli V, Radaelli M, Falini A, Comi G, Filippi M (2012) Mapping regional grey and white matter atrophy in relapsing–remitting multiple sclerosis. Mult Scler (Houndmills, Basingstoke, England) 18(7):1027–1037. https://doi.org/10.1177/1352458512439239

    Article  Google Scholar 

  28. Jacobsen C, Hagemeier J, Myhr KM, Nyland H, Lode K, Bergsland N, Ramasamy DP, Dalaker TO, Larsen JP, Farbu E, Zivadinov R (2014) Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study. J Neurol Neurosurg Psychiatry 85(10):1109–1115. https://doi.org/10.1136/jnnp-2013-306906

    Article  PubMed  Google Scholar 

  29. Rocca MA, Mesaros S, Pagani E, Sormani MP, Comi G, Filippi M (2010) Thalamic damage and long-term progression of disability in multiple sclerosis. Radiology 257(2):463–469. https://doi.org/10.1148/radiol.10100326

    Article  PubMed  Google Scholar 

  30. Granberg T, Bergendal G, Shams S, Aspelin P, Kristoffersen-Wiberg M, Fredrikson S, Martola J (2015) MRI-defined corpus callosal atrophy in multiple sclerosis: a comparison of volumetric measurements, corpus callosum area and index. J Neuroimaging 25(6):996–1001. https://doi.org/10.1111/jon.12237

    Article  PubMed  Google Scholar 

  31. Riva M, Ikonomidou VN, Ostuni JJ, van Gelderen P, Auh S, Ohayon JM, Tovar-Moll F, Richert ND, Duyn JH, Bagnato F (2009) Tissue-specific imaging is a robust methodology to differentiate in vivo T1 black holes with advanced multiple sclerosis-induced damage. AJNR Am J Neuroradiol 30(7):1394–1401. https://doi.org/10.3174/ajnr.A1573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Makris N, Oscar-Berman M, Jaffin SK, Hodge SM, Kennedy DN, Caviness VS, Marinkovic K, Breiter HC, Gasic GP, Harris GJ (2008) Decreased volume of the brain reward system in alcoholism. Biol Psychiat 64(3):192–202. https://doi.org/10.1016/j.biopsych.2008.01.018

    Article  CAS  PubMed  Google Scholar 

  33. Henry RG, Shieh M, Okuda DT, Evangelista A, Gorno-Tempini ML, Pelletier D (2008) Regional grey matter atrophy in clinically isolated syndromes at presentation. J Neurol Neurosurg Psychiatry 79(11):1236–1244. https://doi.org/10.1136/jnnp.2007.134825

    Article  CAS  PubMed  Google Scholar 

  34. Piccolo L, Kumar G, Nakashima I, Misu T, Kong Y, Wakerley B, Ryan S, Cavey A, Fujihara K, Palace J (2015) Multiple sclerosis in Japan appears to be a milder disease compared to the UK. J Neurol 262(4):831–836. https://doi.org/10.1007/s00415-015-7637-3

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

Kazuo Fujihara has received funding for travel and speaker honoraria from Bayer Schering Pharma, Biogen Idec, Eisai Inc., Mitsubishi Tanabe Pharma Corporation, Novartis Pharma, Astellas Pharma Inc., Takeda Pharmaceutical Company Limited, Asahi Kasei Medical Co., Daiichi Sankyo, and Nihon Pharmaceutical; Dr. Fujihara’s research is funded by Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan (22229008, 2010–2015; 26293205, 2014–2016) and by Grants-in-Aid for Scientific Research from the Ministry of Health, Welfare and Labor of Japan (2010 to present). Ichiro Nakashima is receiving research support from LSI Medience and is funded by JSPS KAKENHI Grant Number 17K09772. The funders had no role in the design or conduct of the study; the collection, management, analysis, or interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Department of Neurology, Tohoku Medical and Pharmaceutical University, 1-15-1 Fukumuro, Miyagino-ku, Sendai, 983-8536, Japan

    Juichi Fujimori & Ichiro Nakashima

  2. Department of Multiple Sclerosis Therapeutics, Tohoku University Graduate School of Medicine, Sendai, Japan

    Kazuo Fujihara

  3. Department of Multiple Sclerosis Therapeutics, Fukushima Medical University School of Medicine and Multiple Sclerosis and Neuromyelitis Optica Center, Southern Tohoku Research Institute for Neuroscience, Koriyama, Japan

    Kazuo Fujihara

  4. Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan

    Ryo Ogawa

  5. Department of Neurology, Sendai Nishitaga Hospital, Sendai, Japan

    Toru Baba

  6. Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany

    Mike Wattjes

Authors
  1. Juichi Fujimori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazuo Fujihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryo Ogawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Toru Baba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mike Wattjes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ichiro Nakashima
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JF, RO and TB. The first draft of the manuscript was written by JF and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Juichi Fujimori.

Ethics declarations

Conflicts of interest

Kazuo Fujihara serves on scientific advisory boards for Bayer Schering Pharma, Biogen Idec, Mitsubishi Tanabe Pharma Corporation, Novartis Pharma, Chugai Pharmaceutical, Ono Pharmaceutical, Nihon Pharmaceutical, Merck Serono, Alexion Pharmaceuticals, MedImmune, and Medical Review; serves as an editorial board member for Clinical and Experimental Neuroimmunology (2009 to present) and an advisory board member for the Sri Lanka Journal of Neurology; and has received research support from Bayer Schering Pharma, Biogen Idec Japan, Asahi Kasei Medical, The Chemo-Sero-Therapeutic Research Institute, Teva Pharmaceutical, Mitsubishi Tanabe Pharma, Teijin Pharma, Chugai Pharmaceutical, Ono Pharmaceutical, Nihon Pharmaceutical, and Genzyme Japan. Mike Wattjes reports speaker or consultancy fees from Bayer Healthcare, Biogen, Biologix, Celgene, Eisai, Genilac, Imcyse, Merck Serono, Novartis, Roche, and Sanofi Genzyme. Ichiro Nakashima is serving on scientific advisory boards for Biogen Japan and Novartis Pharma and is receiving honoraria for speaking engagements with Biogen Japan, Mitsubishi Tanabe Pharma, Novartis Pharma, Takeda Pharmaceutical, and Eisai. No other disclosures were reported.

Ethical standards

This study was approved by the institutional ethics committee and has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Informed consent

All patients provided written informed consent.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 532 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fujimori, J., Fujihara, K., Ogawa, R. et al. Patterns of regional brain volume loss in multiple sclerosis: a cluster analysis. J Neurol 267, 395–405 (2020). https://doi.org/10.1007/s00415-019-09595-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00415-019-09595-4

Keywords

Search

Navigation