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Original research
Brain age gap in neuromyelitis optica spectrum disorders and multiple sclerosis
  1. Ren Wei1,
  2. Xiaolu Xu1,
  3. Yunyun Duan1,
  4. Ningnannan Zhang2,
  5. Jie Sun2,
  6. Haiqing Li3,
  7. Yuxin Li3,
  8. Yongmei Li4,
  9. Chun Zeng4,
  10. Xuemei Han5,
  11. Fuqing Zhou6,
  12. Muhua Huang6,
  13. Runzhi Li7,
  14. Zhizheng Zhuo1,
  15. Frederik Barkhof8,9,
  16. James H Cole9,10,
  17. Yaou Liu1
  1. 1 Department of Radiology, Beijing Tiantan Hospital, Beijing, China
  2. 2 Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
  3. 3 Department of Radiology, Huashan Hospital Fudan University, Shanghai, China
  4. 4 Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
  5. 5 Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
  6. 6 Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
  7. 7 Department of Neurology, Beijing Tiantan Hospital, Beijing, China
  8. 8 Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Centre Amsterdam, Amsterdam, The Netherlands
  9. 9 Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
  10. 10 Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
  1. Correspondence to Professor Yaou Liu, Department of Radiology, Beijing Tiantan Hospital, Beijing, China; yaouliu80{at}


Objective To evaluate the clinical significance of deep learning-derived brain age prediction in neuromyelitis optica spectrum disorder (NMOSD) relative to relapsing-remitting multiple sclerosis (RRMS).

Methods This cohort study used data retrospectively collected from 6 tertiary neurological centres in China between 2009 and 2018. In total, 199 patients with NMOSD and 200 patients with RRMS were studied alongside 269 healthy controls. Clinical follow-up was available in 85 patients with NMOSD and 124 patients with RRMS (mean duration NMOSD=5.8±1.9 (1.9–9.9) years, RRMS=5.2±1.7 (1.5–9.2) years). Deep learning was used to learn ‘brain age’ from MRI scans in the healthy controls and estimate the brain age gap (BAG) in patients.

Results A significantly higher BAG was found in the NMOSD (5.4±8.2 years) and RRMS (13.0±14.7 years) groups compared with healthy controls. A higher baseline disability score and advanced brain volume loss were associated with increased BAG in both patient groups. A longer disease duration was associated with increased BAG in RRMS. BAG significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD and RRMS.

Conclusions There is a clear BAG in NMOSD, although smaller than in RRMS. The BAG is a clinically relevant MRI marker in NMOSD and RRMS.

  • neuroimmunology
  • MRI
  • multiple sclerosis
  • neuroradiology

Data availability statement

Data are available on reasonable request.

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Data availability statement

Data are available on reasonable request.

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  • Twitter @JamesCole_Neuro

  • RW and XX contributed equally.

  • Collaborators Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

  • Contributors RW and XX: conception and design of the study, acquisition and analysis of data and drafting the manuscript. Y Liu acts as the guarantor of the study and takes full responsibility for the work. YD, NZ, JS, HL, Y Li, FB, JHC: conception and design of the study, acquisition and analysis of data. Y Li, CZ, XH, FZ, MH, RL, ZZ: acquisition and analysis of data. All authors revised the manuscript and approved the final draft.

  • Funding This work was supported by the National Natural Science Foundation of China (No. 81870958), the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (No. JQ20035), Beijing Young Scholarship and the Capital’s Funds for Health Improvement and Research (CFH2022-1-2042). FB is supported by the NIHR Biomedical Research Centre at UCLH.

  • Competing interests FB acts as a consultant for Combinostics, Biogen-Idec, Janssen, IXICO, Merck-Serono, Novartis and Roche. 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 his institution from Biogen-Idec and Merck. He is co-founder of Queen Square Analytics. He is on the editorial board of Radiology, Neuroradiology, Multiple Sclerosis Journal and Neurology. JHC is a scientific consultant to and shareholder in BrainKey and Claritas Healthcare, as has worked as a consultant to Queen Square Analytics.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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