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

Abstract

Neurodegeneration; Multiple sclerosis; Deep learning

Neurodegeneració; Esclerosi múltiple; Aprenentatge profund

Neurodegeneración; Esclerosis múltiple; Aprendizaje profundo

Background and Objectives Disentangling 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. Methods In 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). Results We 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 < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038–0.082], p < 0.001), adding to BAG (ΔR2 = 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 (ΔR2 = 0.064, p < 0.001). Discussion The 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.

G. Pontillo was supported by the ESNR Research Fellowship Programme (2021). F. Prados and B. Kanber are supported by the UK National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at UCLH and UCL. A. Cagol is supported by EUROSTAR E!113682 HORIZON2020. S. Collorone is supported by a Rosetrees Trust Grant (PGL21/10079). M.A. Foster is supported by a grant from the MRC (MR/S026088/1). S. Groppa and G. Gonzalez-Escamilla receive support from the German Research Foundation (Deutsche Forschungsgemeinschaft, “DFG”: Priority Programme SPP2177; grants GR 4590/3-1 and GO 3493/1-1). The contribution of data from Oslo (H.F.H., E.A.H., and G.O.N.) was supported by grants from The Research Council of Norway (NFR, grant number 240102) and the South-Eastern Health Authorities of Norway (grant number 257955). The contribution of data from Prague (T.U. and M.V.) was supported by the Ministry of Health of the Czech Republic within the conceptual development of a research organization (00064165) at the General University Hospital in Prague, by the project National Institute for Neurologic Research and by the European Union-Next Generation EU (Programme EXCELES, ID project No LX22NPO5107) and by Roche (NCT03706118).