Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.
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; MAGNIMS study group..
Pontillo G, et al. Among authors: calabrese m.
Neurology. 2024 Nov 26;103(10):e209976. doi: 10.1212/WNL.0000000000209976. Epub 2024 Nov 4.
Neurology. 2024.
PMID: 39496109
Free PMC article.
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. ...
BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis …