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Subtyping relapsing–remitting multiple sclerosis using structural MRI

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Abstract

Background and purpose

Subtyping relapsing–remitting multiple sclerosis (RRMS) patients may help predict disease progression and triage patients for treatment. We aimed to subtype RRMS patients by structural MRI and investigate their clinical significances.

Methods

155 relapse-remitting MS (RRMS) and 210 healthy controls (HC) were retrospectively enrolled with structural 3DT1, diffusion tensor imaging (DTI) and resting-state functional MRI. Z scores of cortical and deep gray matter volumes (CGMV and DGMV) and white matter fractional anisotropy (WM-FA) in RRMS patients were calculated based on means and standard deviations of HC. We defined RRMS as “normal” (− 2 < z scores of both GMV and WM-FA), DGM (z scores of DGMV < − 2), and DGM-plus types (z scores of DGMV and [CGMV or WM-FA] < − 2) according to combinations of z scores compared to HC. Expanded disability status scale (EDSS), cognitive and functional MRI measurements, and conversion rate to secondary progressive MS (SPMS) at 5-year follow-up were compared between subtypes.

Results

77 (49.7%) patients were “normal” type, 37 (23.9%) patients were DGM type and 34 (21.9%) patients were DGM-plus type. 7 (4.5%) patients who were not categorized into the above types were excluded. DGM-plus type had the highest EDSS. Both DGM and DGM-plus types had more severe cognitive impairment than “normal” type. Only DGM-plus type showed decreased functional MRI measures compared to HC. A higher conversion ratio to SPMS in DGM-plus type (55%) was identified compared to “normal” type (14%, p < 0.001) and DGM type (20%, p = 0.005).

Conclusion

Three MRI-subtypes of RRMS were identified with distinct clinical and imaging features and different prognosis.

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Data availability

The data can be made available upon reasonable request by a qualified researcher.

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Acknowledgements

We acknowledge the contribution of colleagues and patients who participated in this multicenter study.

Funding

Beijing Natural Science fund, Grant/Award Number: 7133244; National Science Foundation of China, Grant/Award Numbers: 81571631, 81870958.

Author information

Author notes

  1. Zhizheng Zhuo, Yongmei Li and Yunyun Duan contributed equally to the manuscript.

Authors and Affiliations

  1. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119, The West Southern 4th Ring Road, Fengtai District, Beijing, 100070, China

    Zhizheng Zhuo, Yunyun Duan, Guanmei Cao, Fenglian Zheng, Jinli Ding & Yaou Liu

  2. Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Yongmei Li & Chun Zeng

  3. Center for Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China

    Decai Tian, Xinghu Zhang & Fudong Shi

  4. China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China

    Decai Tian, Fudong Shi & Yaou Liu

  5. Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China

    Xinli Wang & Fudong Shi

  6. Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China

    Jinhui Wang

  7. Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

    Kuncheng Li

  8. Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China

    Fuqing Zhou & Muhua Huang

  9. Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006, Jiangxi Province, China

    Fuqing Zhou & Muhua Huang

  10. Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Yuxin Li & Haiqing Li

  11. Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China

    Ningnannan Zhang, Jie Sun & Chunshui Yu

  12. Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, 130031, Jilin Province, China

    Xuemei Han

  13. Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland

    Sven Haller

  14. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, The Netherlands

    Frederik Barkhof

  15. Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, UK

    Frederik Barkhof

Authors
  1. Zhizheng Zhuo
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  2. Yongmei Li
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  3. Yunyun Duan
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  4. Guanmei Cao
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  5. Fenglian Zheng
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  6. Jinli Ding
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  7. Decai Tian
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  8. Xinli Wang
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  9. Jinhui Wang
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  10. Xinghu Zhang
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  11. Kuncheng Li
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  12. Fuqing Zhou
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  13. Muhua Huang
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  14. Yuxin Li
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  15. Haiqing Li
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  16. Chun Zeng
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  17. Ningnannan Zhang
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  18. Jie Sun
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  19. Chunshui Yu
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  20. Xuemei Han
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  21. Sven Haller
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  22. Frederik Barkhof
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  23. Fudong Shi
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  24. Yaou Liu
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Contributions

ZZ, YL and YD made equal contributions to this work. ZZ was responsible for data processing and statistical analyses, manuscript drafting and revision; YL was responsible for the data processing, manuscript drafting and revision; YD was responsible for clinical and MRI data management, lesion segmentation and revision; GC was responsible for the lesion segmentation; FZ, JD, SH and FB helped revising the manuscript; JW helped the MRI data preprocessing; DT, XW, XZ, KL, FZ, MH, YL, HL, CZ, NZ, JS, CY, XH, and FS were responsible for patient recruitment in their centers; YL was responsible for the study design, clinical and MRI data, manuscript revision and guarantor of the work.

Corresponding author

Correspondence to Yaou Liu.

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Conflicts of interest

Frederik Barkhof acts as a consultant for Apitope, Bayer-Schering, Biogen-Idec, GeNeuro, Sanofi-Genzyme, Ixico, Janssen Research, Merck-Serono, Novartis, Roche and TEVA. He has received grants, or grants are pending, from the Amyloid Imaging to Prevent Alzheimer’s Disease (AMYPAD) initiative, the Biomedical Research Centre at University College London Hospitals, the Dutch MS Society, ECTRIMS–MAGNIMS, EU-H2020, the Dutch Research Council (NWO), the UK MS Society, and the National Institute for Health Research, University College London. He has received payments for the development of educational presentations from Ixico and to his institution from Biogen-Idec. He is on the editorial board of Radiology, Brain, European Radiology, Multiple Sclerosis Journal and Neurology. None of the other authors declare a relevant conflict of interest.

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Zhuo, Z., Li, Y., Duan, Y. et al. Subtyping relapsing–remitting multiple sclerosis using structural MRI. J Neurol 268, 1808–1817 (2021). https://doi.org/10.1007/s00415-020-10376-7

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  • DOI: https://doi.org/10.1007/s00415-020-10376-7

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