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

Neurology

From the Queen Square Multiple Sclerosis Centre (G.P., F.P., J.C., B.K., O.A.-M., S.A.-A., A. Bianchi, W.J.B., R. Christensen, E.C., S. Collorone, M.A.F., Y.H., A.H., S. Mohamud, R.N., A.T.T., J.W., C.Y., O.C., F.B.), Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, United Kingdom; MS Center Amsterdam (G.P., H.V., F.B.), Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands; Departments of Advanced Biomedical Sciences and Electrical Engineering and Information Technology (G.P., A. Brunetti, S. Cocozza), University of Naples "Federico II," Italy; Centre for Medical Image Computing (F.P., B.K., F.B.), Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; E-Health Center (F.P.), Universitat Oberta de Catalunya, Barcelona, Spain; Institute of Neuroradiology (B.B., C.L.), St. Josef Hospital, Ruhr-University Bochum, Germany; Department of Advanced Medical and Surgical Sciences (A. Bisecco, A.G.), University of Campania "Luigi Vanvitelli," Naples, Italy; Translational Imaging in Neurology (ThINK) Basel (A.C., C. Granziera), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel; Neurologic Clinic and Policlinic (A.C., C. Granziera, J.K.), MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Switzerland; Department of Neurosciences, Biomedicine and Movement Sciences (M. Calabrese, M. Castellaro), University of Verona; Department of Information Engineering (M. Castellaro), University of Padova; Department of Medicine, Surgery and Neuroscience (R. Cortese, N.D.S.), University of Siena, Italy; Department of Neurology (C.E., D.P.), Medical University of Graz, Austria; Neuroimaging Research Unit (M.F., M.A.R., P.V.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Neurology Unit, Neurorehabilitation Unit, Neurophysiology Service, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.F., M.A.R., P.V.), Milan; Department of Neurosciences (C. Gasperini, S.R.), San Camillo-Forlanini Hospital, Rome, Italy; Department of Neurology (G.G.-E., S.G.), Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Department of Neurology (H.F.F.H., E.A.H., G.O.N.), Oslo University Hospital; Institute of Clinical Medicine (H.F.F.H., E.A.H., G.O.N.), and Department of Psychology (E.A.H.), University of Oslo, Norway; Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM) (S.L., E.M.-H.), Hospital Clinic Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Su, Barcelona, Spain; Department of Neurology (C.L.), St. Josef Hospital, Ruhr-University Bochum, Germany; Nuffield Department of Clinical Neurosciences (S. Messina, J.P.), University of Oxford, United Kingdom; Department of Molecular Medicine and Medical Biotechnology (M.M.), and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P.), University of Naples "Federico II"; Department of Human Neurosciences (M.P.), Sapienza University of Rome, Italy; Section of Neuroradiology (A.R.), Department of Radiology, and Centre d'Esclerosi Múltiple de Catalunya (Cemcat) (J.S.-G.), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Spain; MS Center Amsterdam (E.M.M.S.), Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands; Department of Neurology and Center of Clinical Neuroscience (T.U.), and Department of Radiology (M.V.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; MS Center Amsterdam (M.M.S.), Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands; Centre for Medical Image Computing (J.H.C.), Department of Computer Science, and Dementia Research Centre (J.H.C., F.B.), UCL Queen Square Institute of Neurology, University College London, United Kingdom.

Published: November 2024

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, = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: = 0.06 [0.00-0.13], = 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], < 0.001). DD gap significantly explained EDSS changes ( = 0.060 [0.038-0.082], < 0.001), adding to BAG (Δ = 0.012, < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change ( = 0.50 [0.39-0.60], < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (Δ = 0.064, < 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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540460	PMC
http://dx.doi.org/10.1212/WNL.0000000000209976	DOI Listing

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