Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning
Giuseppe Pontillo, Ferrán Prados, Jordan Colman, Baris Kanber, Omar Abdel‐Mannan, Sarmad Al‐Araji, Barbara Bellenberg, Alessia Bianchi, Alvino Bisecco, Wallace Brownlee, Arturo Brunetti, Alessandro Cagol, Massimiliano Calabrese, Marco Castellaro, Ronja Christensen, Sirio Cocozza, Elisa Colato, Sara Collorone, Rosa Cortese, Nicola De Stefano, Christian Enzinger, Massimo Filippi, Michael A. Foster, Antonio Gallo, Claudio Gasperini, Gabriel González‐Escamilla, Cristina Granziera, Sergiu Groppa, Yael Hacohen, Hanne F. Harbo, Anna He, Einar August Høgestøl, Jens Kühle, Sara Llufriú, Carsten Lukas, Eloy Martínez‐Heras, Silvia Messina, Marcello Moccia, Suraya Mohamud, Riccardo Nistri, Gro Owren Nygaard, Jacqueline Palace, Maria Petracca, Daniela Pinter, Maria A. Rocca, Àlex Rovira, Serena Ruggieri, Jaume Sastre‐Garriga, Eva Strijbis, Ahmed Toosy, Tomáš Uher, Paola Valsasina, Manuela Vaněčková, Hugo Vrenken, Jed Wingrove, Charmaine Yam, Menno M. Schoonheim, Olga Ciccarelli, James H. Cole, Frederik Barkhof, for the MAGNIMS study group., Ludwig Kappos, Tarek Yousry
Abstract
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: < 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.