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Updated: Aug 21, 2026
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Brain network biomarkers for diagnosis and clinical stratification in multiple sclerosis: a longitudinal study
Ruiping Gao1, Xuegang Song2, Hanyue Zheng3
1Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Hebei Medical University, Shijiazhuang, China.
Background:
Multiple sclerosis (MS) is increasingly recognised as a disorder of large-scale brain network reorganisation rather than a disease explained solely by focal demyelinating lesions. However, the relevance of structural network abnormalities to clinical heterogeneity and progression remains unclear.
Methods:
We analysed 3T magnetic resonance imaging (MRI) from two independent MS cohorts (total n = 635): Dataset 1 from the Chinese neuroimmunological diseases (NIDBase) cohort (163 MS and 248 healthy controls [HC]) and Dataset 2 from the UK Biobank (117 MS and 107 HC). A subset of Dataset 1 underwent 256-channel resting-state high-density electroencephalography (hd-EEG) (135 MS and 80 HC). We constructed structural covariance networks (iSCNs) from 3D T1-weighted MRI using Kullback-Leibler similarity across 170 Automated Anatomical Labelling atlas 3 (AAL3) regions. Patients were stratified by disability, cognition, and disease activity; a subgroup with no evidence of disease activity (NEDA) with 1-year follow-up MRI (n = 33) was analysed longitudinally. Linear support vector machine classifiers evaluated topological and connectivity features for diagnosis and clinical stratification.
Findings:
MS showed reproducible topological and connectivity abnormalities, involving thalamic and subcortical hubs and altered visual-network-related couplings. Disability showed the broadest abnormalities, whereas cognitive impairment and disease activity were associated with more selective changes. Patients meeting NEDA criteria showed subtle longitudinal nodal changes. Connectivity features performed best for MS-vs-HC discrimination, whereas topological features performed better for clinical stratification.
Interpretation:
The iSCN approach identified reliable patterns of topological and connectivity impairment across MS and its clinical stratifications, providing insight into MS neuropathology and guiding future diagnostic and therapeutic biomarker development.
Funding:
This work was funded by Beijing Research Ward Excellence Program (BRWEP2024W022010104, BRWEP2024W022010109), Beijing Scholar program (No. 106), the Project for Innovation and Development of Beijing Municipal Geriatric Medical Research Center (11000023T000002041657), Dengfeng Talent Program (DFL20220701), National Natural Science Foundation of China (82571539, 82501612), Xuanwu Hospital Talent Convergence Program-Leading Talents (HZ2021ZCLJ008), the Beijing Hospitals Authority's Ascent Plan (DFL20240801), and Beijing "Huizhi" Talent Program, Cultivation Program-Leading Talents (HZ2025PYLJ003).
