Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning

Le résumé fourni par la source

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 diseasespecific 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 crosssectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal singlecenter 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, R 2 = 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 < 0.001).DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082],p < 0.001), adding to BAG (DR 2 = 0.012, p < 0.001).Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60],p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (DR 2 = 0.064, p < 0.001). 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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.