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Updated: Jun 3, 2026
Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
Published on: July 19, 2019
Deep learning-based neuroanatomical profiling reveals population-specific brain changes in multiple sclerosis: a
Mahdi Bashiri Bawil1, Mousa Shamsi2, Abolhassan Shakeri Bavil3
1Biomedical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.
Background:
Multiple sclerosis (MS) affects 2.8 million individuals worldwide, with Middle Eastern populations remaining underrepresented in neuroimaging research despite elevated regional prevalence rates. Large-scale comparative studies between MS patients and healthy controls (HC) are essential for characterizing disease-specific brain changes and establishing normative biomarkers. This study aimed to perform comprehensive statistical characterization of brain structural changes in MS patients compared to HC using automated deep learning-based segmentation, establishing population-specific reference ranges for a Middle Eastern cohort.
Methods:
This retrospective observational study analyzed 1,381 subjects (1,000 HC, 381 MS patients) from Northwest Iran. Four deep learning architectures were evaluated on multi-center training data (local cohort plus MSSEG dataset), with U-Net selected for automated FLAIR sequence segmentation. Neuroanatomically-informed lesion classification identified periventricular, deep, and juxtacortical white matter hyperintensities. Statistical analyses employed age group- and gender-stratified comparisons with comprehensive correlation assessments across ventricular and lesion load measures.
Results:
U-Net achieved optimal segmentation performance (DSC = 88.8%, HD95 = 2.8 mm), supporting its selection for population-level analysis. Compared to healthy controls, MS patients exhibited markedly elevated structural burden across all age strata: ventricular load was 1.7-fold higher (normalized ratios: 1.39%→2.37%; t = - 18.92, Cohen's d = 1.139) and white matter lesion load was 2.4-fold higher (normalized ratios: 0.315%→0.749%; U = 11,963.0, rank-biserial r = 0.310), both with p < 10⁻⁶. Among lesion subtypes, periventricular lesions predominated (53.91 ± 20.62% of total burden), while anatomical distribution patterns showed no significant gender differences. Age-related structural changes were more pronounced in MS patients than in controls, with stronger correlations observed for both ventricular load (r = 0.403, p = 2.50 × 10⁻¹⁶) and lesion load (r = 0.266, p = 1.33 × 10⁻⁷).
Conclusions:
This study provides preliminary population-specific reference ranges for MS neuroimaging biomarkers in Middle Eastern populations, revealing lesion accumulation patterns with periventricular predominance. The automated segmentation and statistical framework address gaps in global MS research demographics.
