Multicenter Study

. 2024 Nov 26;103(10):e209976.

doi: 10.1212/WNL.0000000000209976. Epub 2024 Nov 4.

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

Giuseppe Pontillo¹, Ferran Prados¹, Jordan Colman¹, Baris Kanber¹, Omar Abdel-Mannan¹, Sarmad Al-Araji¹, Barbara Bellenberg¹, Alessia Bianchi¹, Alvino Bisecco¹, Wallace J Brownlee¹, Arturo Brunetti¹, Alessandro Cagol¹, Massimiliano Calabrese¹, Marco Castellaro¹, Ronja Christensen¹, Sirio Cocozza¹, Elisa Colato¹, Sara Collorone¹, Rosa Cortese¹, Nicola De Stefano¹, Christian Enzinger¹, Massimo Filippi¹, Michael A Foster¹, Antonio Gallo¹, Claudio Gasperini¹, Gabriel Gonzalez-Escamilla¹, Cristina Granziera¹, Sergiu Groppa¹, Yael Hacohen¹, Hanne F F Harbo¹, Anna He¹, Einar A Hogestol¹, Jens Kuhle¹, Sara Llufriu¹, Carsten Lukas¹, Eloy Martinez-Heras¹, Silvia Messina¹, Marcello Moccia¹, Suraya Mohamud¹, Riccardo Nistri¹, Gro O Nygaard¹, Jacqueline Palace¹, Maria Petracca¹, Daniela Pinter¹, Maria A Rocca¹, Alex Rovira¹, Serena Ruggieri¹, Jaume Sastre-Garriga¹, Eva M Strijbis¹, Ahmed T Toosy¹, Tomas Uher¹, Paola Valsasina¹, Manuela Vaneckova¹, Hugo Vrenken¹, Jed Wingrove¹, Charmaine Yam¹, Menno M Schoonheim¹, Olga Ciccarelli¹, James H Cole¹, Frederik Barkhof¹; MAGNIMS study group.¹

Collaborators, Affiliations

Collaborators

MAGNIMS study group.:
Ludwig Kappos, Tarek Yousry

Affiliation

¹ 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.

PMID: 39496109
PMCID: PMC11540460
DOI: 10.1212/WNL.0000000000209976

Multicenter Study

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

Giuseppe Pontillo et al. Neurology. 2024.

. 2024 Nov 26;103(10):e209976.

doi: 10.1212/WNL.0000000000209976. Epub 2024 Nov 4.

Authors

Collaborators

MAGNIMS study group.:
Ludwig Kappos, Tarek Yousry

Affiliation

¹ 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.

PMID: 39496109
PMCID: PMC11540460
DOI: 10.1212/WNL.0000000000209976

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: 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.

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Conflict of interest statement

G. Pontillo received research grants from ECTRIMS-MAGNIMS and ESNR. M. Calabrese received speaker honoraria from Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, and Roche, and receives research support from the Progressive MS Alliance and Italian Minister of Health. S. Cocozza discloses honoraria for advisory board participation from Amicus and research grants from Fondazione Italiana Sclerosi Multipla, and Telethon. R. Cortese received speaker honoraria and travel support from Roche, Merck, Sanofi-Genzyme, Novartis, and Janssen, and was awarded a MAGNIMS-ECTRIMS fellowship in 2019. M. Filippi is the Editor-in-Chief of the Journal of Neurology, an associate editor of Human Brain Mapping, Neurological Sciences, and Radiology; received compensation for consulting services from Alexion, Almirall, Biogen, Merck, Novartis, Roche, Sanofi; for speaking activities from Bayer, Biogen, Celgene, Chiesi Italia SpA, Eli Lilly, Genzyme, Janssen, Merck-Serono, Neopharmed Gentili, Novartis, Novo Nordisk, Roche, Sanofi, Takeda, and TEVA; for participation in advisory boards for Alexion, Biogen, Bristol-Myers Squibb, Merck, Novartis, Roche, Sanofi, Sanofi-Aventis, Sanofi-Genzyme, and Takeda; and for scientific direction of educational events for Biogen, Merck, Roche, Celgene, Bristol-Myers Squibb, Lilly, Novartis, and Sanofi-Genzyme; and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, the Italian Ministry of Health, the Italian Ministry of University and Research, and Fondazione Italiana Sclerosi Multipla. M.A. Foster has received speaker honoraria from Merck. C. Gasperini has received speaker honoraria and/or travel expenses for attending meeting from Bayer Schering Pharma, Sanofi-Aventis, Merck, Biogen, Novartis, Almirall, Bristol Myers Squibb; University Hospital Basel (USB), as the employer of C. Granziera, has received the following fees which were used exclusively for research support: (1) advisory board and consultancy fees from Actelion, Genzyme-Sanofi, Novartis, GeNeuro and Roche; (2) speaker fees from Genzyme-Sanofi, Novartis, GeNeuro and Roche; and (3) research support from Siemens, GeNeuro, Roche; is supported by the Swiss National Science Foundation (SNSF) grant PP00P3_176984, the Stiftung zur Forderung der gastroenterologischen und allgemeinen klinischen Forschung and the EUROSTAR E!113682 HORIZON2020. E.A. Hogestol received honoraria for lecturing and advisory board activity from Biogen, Merck and Sanofi-Genzyme and unrestricted research grant from Merck, and received honoraria for lecturing and advisory board activity from Biogen, Merck, and Sanofi-Genzyme, and unrestricted research grant from Merck. S. Llufriu received compensation for consulting services and speaker honoraria from Biogen Idec, Novartis, TEVA, Genzyme, Sanofi and Merck. C. Lukas received a research grant by the German Federal Ministry for Education and Research, BMBF, German Competence Network Multiple Sclerosis (KKNMS, grant no.01GI1601I) and has received consulting and speaker's honoraria from Biogen Idec, Bayer Schering, Daiichi Sanykyo, Merck Serono, Novartis, Sanofi, Genzyme and TEVA. S. Messina received honoraria for lecturing and advisory board activity from UCB and Biogen and travel grant from Roche and Merck. M. Moccia has received research grants from the ECTRIMS-MAGNIMS, the UK MS Society, and Merck and honoraria from Biogen, BMS Celgene, Ipsen, Merck, Novartis, and Roche. J. Palace has received support for scientific meetings and honoraria for advisory work from Merck Serono, Novartis, Chugai, Alexion, Roche, Medimmune, Argenx, Vitaccess, UCB, Mitsubishi, Amplo, Janssen; has received grants from Alexion, Argenx, Roche, Medimmune, Amplo biotechnology, and holds a patent (ref P37347WO) and licence agreement Numares multimarker MS diagnostics; holds shares in AstraZeneca. Her group has been awarded an ECTRIMS fellowship and a Sumaira Foundation grant to start later this year. A Charcot fellow worked in Oxford 2019–2021. She acknowledges partial funding to the trust by highly specialized services NHS England. She is on the medical advisory boards of the Sumaira Foundation and MOG project charities, is a member of the Guthy Jackon Foundation Charity, is on the board of the European Charcot Foundation, is on the steering committee of MAGNIMS, is on the UK NHSE IVIG Committee, is chairperson of the NHSE neuroimmunology patient pathway, has been an ECTRIMS council member on the educational committee since June 2023, and is on the ABN advisory groups for MS and neuroinflammation. M. Petracca discloses meeting expenses from Novartis, Janssen, Roche and Merck, speaking honoraria from HEALTH&LIFE S.r.l., AIM Education S.r.l., Biogen, Novartis and FARECOMUNICAZIONE E20, honoraria for consulting services and for advisory board participation from Biogen, and research grants from Italian MS Foundation, Baroni Foundation, and Italian Ministry of University and Research. D. Pinter has received funding for travel from Merck, Genzyme/Sanofi-Aventis and Biogen, as well as speaking honoraria from Biogen, Novartis and Merck. M.A. Rocca received consulting fees from Biogen, Bristol Myers Squibb, Eli Lilly, Janssen, Roche; and speaker honoraria from AstraZaneca, Biogen, Bristol Myers Squibb, Bromatech, Celgene, Genzyme, Horizon Therapeutics Italy, Merck Serono SpA, Novartis, Roche, Sanofi and Teva, receives research support from the MS Society of Canada, the Italian Ministry of Health, the Italian Ministry of University and Research, and Fondazione Italiana Sclerosi Multipla, and is an associate editor for Multiple Sclerosis and Related Disorders. M. Vaneckova received speaker honoraria, consultant fees and travel expenses from Biogen Idec, Novartis, Roche, Merck, and Teva, and has been supported by the Czech Ministry of Education—project Cooperatio LF1, research area Neuroscience, and the project National Institute for Neurologic Research (Programme EXCELES, ID project No LX22NPO5107)—funded by the European Union-Next Generation EU and Czech Ministry of Health—the institutional support of the research RVO VFN 64165. A. Rovira serves/ed on scientific advisory boards for BMS, Novartis, Sanofi-Genzyme, Synthetic MR, Tensor Medical, Roche, Biogen, and OLEA Medical, and has received speaker honoraria from Bayer, Sanofi-Genzyme, Merck-Serono, Teva Pharmaceutical Industries Ltd., Novartis, Roche, BMS, and Biogen. S. Ruggieri has received honoraria from Biogen, Merck Serono, Novartis, Bristol Myers Squibb, and Sanofi as consulting services, speaking and/or travel support. A.T. Toosy has received speaker honoraria from Merck, Biomedia, Sereno Symposia International Foundation, Bayer, and At the Limits, has received meeting expenses from Merck, Biogen Idec, and Novartis, was the UK PI for 2 clinical trials sponsored by MEDDAY pharmaceutical company (MD1003 in optic neuropathy [MS-ON: NCT02220244] and progressive MS [MS-SPI2: NCT02936037]), has been supported by recent grants from the MRC (MR/S026088/1), NIHR BRC (541/CAP/OC/818837) and RoseTrees Trust (A1332 and PGL21/10079), is an associate editor for Frontiers in Neurology: Neuro-ophthalmology section, and is on the editorial boards of Neurology® and Multiple Sclerosis Journal. P. Valsasina received speaker honoraria from Biogen Idec. M.M. Schoonheim serves on the editorial boards of Neurology® and Frontiers in Neurology, receives research support from the Dutch MS Research Foundation, Eurostars-EUREKA, ARSEP, Amsterdam Neuroscience, MAGNIMS, and ZonMW, and has served as a consultant for or received research support from Atara Biotherapeutics, Biogen, Celgene/Bristol Meyers Squibb, Genzyme, MedDay, and Merck. O. Ciccarelli is an NIHR Research Professor (RP-2017-08-ST2-004); acts as a consultant for Biogen, Merck, Novartis, Roche, and Teva; and has received research grant support from the MS Society of Great Britain and Northern Ireland, the NIHR UCLH Biomedical Research Centre, the Rosetree Trust, the National MS Society, and the NIHR-HTA. J.H. Cole is a scientific advisor to and shareholder in BrainKey and Claritas HealthTech PTE. F. Barkhof is a steering committee and iDMC member for Biogen, Merck, Roche, and EISAI; is a consultant for Roche, Biogen, Merck, IXICO, Jansen, and Combinostics; has research agreements with Novartis, Merck, Biogen, GE, and Roche; and is a cofounder and shareholder of Queen Square Analytics LTD. The remaining authors report no competing interests. Go to Neurology.org/N for full disclosures.

Figures

**Figure 1. Conceptual Framework of the Study**
With the brain-age paradigm, chronological age is modeled as a function of brain MRI scans in healthy individuals and the resulting model of normal brain aging is used for neuroimaging-based age prediction in unseen individuals. The extent to which an individual deviates from healthy brain aging, expressed as the difference between predicted and chronological age (the brain-age gap), is an age-adjusted global index of brain health. We proposed to complement this approach by further modeling DD in PwMS as a function of brain MRI scans, to provide a reference standard of multiple sclerosis–related brain damage accumulation. The error associated with the prediction of DD (the brain-predicted DD gap), quantifying the extent to which a patient deviates from the typical disease trajectory, is a DD-adjusted global measure of multiple sclerosis–specific brain damage. DD = disease duration; HI = healthy individuals; MS = multiple sclerosis; PwMS = patients with MS; T1w = T1-weighted.

**Figure 2. Modeling Disease Duration in Patients With Multiple Sclerosis**
(A) Scatterplot showing the relationship between the actual disease duration values in the test set (N = 878) and the ones predicted by the model. (B) Scatterplot showing the relationship between the disease duration gap and the brain-age gap (obtained with the DeepBrainNet model) in the test set; marginal density plots are also shown, portraying the distribution of the 2 variables. Linear fit lines are shown as solid lines (with corresponding 95% confidence intervals in gray) while dashed lines represent the line of identity (A) and horizontal and vertical zero reference lines (B), respectively. DD = disease duration; MAE = mean absolute error.

**Figure 3. Guided Backpropagation Analysis to Interrogate Brain Regions Influencing the Model for the Prediction of Disease Duration**
Lightbox view of selected slices from the quasi-raw T1w volumes (on the left) and corresponding guided backpropagation–derived saliency maps (on the right) of 2 representative PwMS exhibiting extremely positive (A) or negative (B) values of the DD gap. For saliency maps, both positive (positively correlated with the output, in red) and negative (negatively correlated with the outcome, in blue) magnitudes are shown. In both cases, the model focuses mostly on regions that seem to be related to (the widening of) the CSF spaces. BAG = brain-age gap; DD = disease duration; PwMS = patients with multiple sclerosis; T1w = T1-weighted.

**Figure 4. Correlations Between Brain-Age and Disease Duration Gaps and Regional Brain and Lesion Volumes**
In the upper row, plots show the correlations between brain-age gap values and cortical (A) and subcortical/lesion (B) volumes. In the bottom row, plots show the correlations between disease duration gap values and cortical (C) and subcortical/lesion (D) volumes. Shown are the Pearson correlation coefficients resulting from partial correlation analyses correcting for age, age², disease duration, sex, and estimated total intracranial volume. The cortex is parcellated according to the DKT atlas. BAG = brain-age gap; DD = disease duration.

**Figure 5. Impact of MS Lesions on Age and DD Predictions**
Bland-Altman plot of brain-predicted DD (A) and age (B) from unfilled and filled T1w scans. The plots show the mean value from the 2 measures for each patient (x-axis) and the difference between the 2 measures (y-axis). The mean difference lines are solid, and the corresponding limits of agreement (±1.96 × SD of difference) are dashed lines. DD = disease duration; MS = multiple sclerosis.

**Figure 6. Relationships Between Brain-Age and Disease Duration Gaps and Physical Disability**
In the upper row, scatterplots show the marginal effects on EDSS scores of the brain-age (A) and disease duration (B) gap metrics (regression models were corrected for the effects of age, age², disease duration, and sex). In the bottom row, scatterplots show the relationship between annualized changes in EDSS and brain-age (C) and disease duration (D) gaps. Linear fit lines are shown as solid lines (with corresponding 95% confidence intervals in gray). DD = disease duration; EDSS = Expanded Disability Status Scale.

See this image and copyright information in PMC

References

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

Collaborators

Affiliation

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

Authors

Collaborators

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Abstract

Conflict of interest statement

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