| Title: |
Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap |
| Authors: |
Pontillo, G; Prados, F; Colman, J; Kanber, B; Abdel-Mannan, O; Al-Araji, S; Bellenberg, B; Bianchi, A; Bisecco, A; Brownlee, WJ; Brunetti, A; Cagol, A; Calabrese, M; Castellaro, M; Christensen, R; Cocozza, S; Colato, E; Collorone, S; Cortese, R; De Stefano, N; Enzinger, C; Filippi, M; Foster, MA; Gallo, A; Gasperini, C; Gonzalez-Escamilla, G; Granziera, C; Groppa, S; Hacohen, Y; Harbo, HFF; He, A; Hogestol, EA; Kuhle, J; Llufriu, S; Lukas, C; Martinez-Heras, E; Messina, S; Moccia, M; Mohamud, S; Nistri, R; Nygaard, GO; Palace, J; Petracca, M; Pinter, D; Rocca, MA; Rovira, A; Ruggieri, S; Sastre-Garriga, J; Strijbis, EM; Toosy, AT; Uher, T; Valsasina, P; Vaneckova, M; Vrenken, H; Wingrove, J; Yam, C; Schoonheim, MM; Ciccarelli, O; Cole, JH; Barkhof, F |
| Publisher Information: |
Lippincott, Williams & Wilkins |
| Publication Year: |
2025 |
| Collection: |
Oxford University Research Archive (ORA) |
| Description: |
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 ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://doi.org/10.1212/wnl.0000000000209976 |
| DOI: |
10.1212/wnl.0000000000209976 |
| Availability: |
https://doi.org/10.1212/wnl.0000000000209976; https://ora.ox.ac.uk/objects/uuid:33dbb801-20ce-40cd-819e-c5cc9e408a2f |
| Rights: |
info:eu-repo/semantics/openAccess ; CC Attribution (CC BY) |
| Accession Number: |
edsbas.9719CFBE |
| Database: |
BASE |