• Ren Wei, Xiaolu Xu, Yunyun Duan, Ningnannan Zhang, Jie Sun, Haiqing Li, Yuxin Li, Yongmei Li, Chun Zeng, Xuemei Han, Fuqing Zhou, Muhua Huang, Runzhi Li, Zhizheng Zhuo, Frederik Barkhof, H ColeJamesJ0000-0003-1908-5588Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK., and Yaou Liu.
• Department of Radiology, Beijing Tiantan Hospital, Beijing, China.
• J. Neurol. Neurosurg. Psychiatr. 2023 Jan 1; 94 (1): 313731-37.

ObjectiveTo evaluate the clinical significance of deep learning-derived brain age prediction in neuromyelitis optica spectrum disorder (NMOSD) relative to relapsing-remitting multiple sclerosis (RRMS).MethodsThis 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.ResultsA 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.ConclusionsThere is a clear BAG in NMOSD, although smaller than in RRMS. The BAG is a clinically relevant MRI marker in NMOSD and RRMS.© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.

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