Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap

Journal article

Background and objectivesDisentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS.MethodsIn this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS).ResultsWe gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p B = 0.060 [0.038-0.082], p R2 = 0.012, p r = 0.50 [0.39-0.60], p R2 = 0.064, p DiscussionThe brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

Authors: Pontillo, G; Prados, F; Colman, J; Kanber, B; Abdel-Mannan, O

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Lippincott, Williams & Wilkins
• Journal: Neurology
• Volume: 103
• Issue: 10
• Pages: e209976
• Publication date: 2024-11-04
• DOI: 10.1212/wnl.0000000000209976
• EISSN: 1526-632X
• ISSN: 0028-3878
• Pmid: 39496109
• Language: English
• Keywords: Male; Neurodegenerative Diseases; Female; Humans; Longitudinal Studies; Magnetic Resonance Imaging; Adult; Multiple Sclerosis; Aging; Cross-Sectional Studies; Retrospective Studies; Brain; Middle Aged; Deep Learning