• This record comes from PubMed

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

. 2024 Nov 26 ; 103 (10) : e209976. [epub] 20241104

Language English Country United States Media print-electronic

Document type Journal Article, Multicenter Study

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.

See more in PubMed

Graves JS, Krysko KM, Hua LH, Absinta M, Franklin RJM, Segal BM. Ageing and multiple sclerosis. Lancet Neurol. 2023;22(1):66-77. doi:10.1016/S1474-4422(22)00184-3 PubMed DOI

Sanai SA, Saini V, Benedict RH, et al. . Aging and multiple sclerosis. Mult Scler. 2016;22(6):717-725. doi:10.1177/1352458516634871 PubMed DOI

Wyss-Coray T. Ageing, neurodegeneration and brain rejuvenation. Nature. 2016;539(7628):180-186. doi:10.1038/nature20411 PubMed DOI PMC

Cole JH, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 2017;40(12):681-690. doi:10.1016/j.tins.2017.10.001 PubMed DOI

Kaufmann T, van der Meer D, Doan NT, et al. . Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci. 2019;22(10):1617-1623. doi:10.1038/s41593-019-0471-7 PubMed DOI PMC

Høgestøl EA, Kaufmann T, Nygaard GO, et al. . Cross-sectional and longitudinal MRI brain scans reveal accelerated brain aging in multiple sclerosis. Front Neurol. 2019;10:450. doi:10.3389/fneur.2019.00450 PubMed DOI PMC

Cole JH, Raffel J, Friede T, et al. . Longitudinal assessment of multiple sclerosis with the brain-age paradigm. Ann Neurol. 2020;88(1):93-105. doi:10.1002/ana.25746 PubMed DOI

Azevedo CJ, Cen SY, Jaberzadeh A, Zheng L, Hauser SL, Pelletier D. Contribution of normal aging to brain atrophy in MS. Neurol Neuroimmunol Neuroinflamm. 2019;6(6):e616. doi:10.1212/NXI.0000000000000616 PubMed DOI PMC

Vidal-Pineiro D, Wang Y, Krogsrud SK, et al. . Individual variations in “brain age” relate to early-life factors more than to longitudinal brain change. Elife. 2021;10:e69995. doi:10.7554/eLife.69995 PubMed DOI PMC

Lublin FD, Reingold SC, Cohen JA, et al. . Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83(3):278-286. doi:10.1212/WNL.0000000000000560 PubMed DOI PMC

Roxburgh RH, Seaman SR, Masterman T, et al. . Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity. Neurology. 2005;64(7):1144-1151. doi:10.1212/01.WNL.0000156155.19270.F8 PubMed DOI

Confavreux C, Vukusic S. Age at disability milestones in multiple sclerosis. Brain. 2006;129(pt 3):595-605. doi:10.1093/brain/awh714 PubMed DOI

Thompson AJ, Banwell BL, Barkhof F, et al. . Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162-173. doi:10.1016/S1474-4422(17)30470-2 PubMed DOI

ANTsPyNet. Accessed January 8, 2024. github.com/ANTsX/ANTsPyNet.

Bashyam VM, Erus G, Doshi J, et al. . MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain. 2020;143(7):2312-2324. doi:10.1093/brain/awaa160 PubMed DOI PMC

Project MONAI. Accessed January 8, 2024. docs.monai.io/en/stable/_modules/monai/networks/nets/densenet.html.

Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. 2017:4700-4708. Accessed October 31, 2022. openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html.

Wood DA, Kafiabadi S, Busaidi AA, et al. . Accurate brain-age models for routine clinical MRI examinations. Neuroimage. 2022;249:118871. doi:10.1016/j.neuroimage.2022.118871 PubMed DOI

Paszke A, Gross S, Massa F, et al. . PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems. Vol. 32. Curran Associates, Inc.; 2019. Accessed October 31, 2022. proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html.

Cerri S, Puonti O, Meier DS, et al. . A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis. Neuroimage. 2021;225:117471. doi:10.1016/j.neuroimage.2020.117471 PubMed DOI PMC

Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M. FastSurfer: a fast and accurate deep learning based neuroimaging pipeline. Neuroimage. 2020;219:117012. doi:10.1016/j.neuroimage.2020.117012 PubMed DOI PMC

Wattjes MP, Ciccarelli O, Reich DS, et al. . 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 2021;20(8):653-670. doi:10.1016/S1474-4422(21)00095-8 PubMed DOI

Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M. Striving for simplicity: the all convolutional net. arXiv. 2015. doi:10.48550/arXiv.1412.6806 DOI

Battaglini M, Jenkinson M, De Stefano N. Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp. 2012;33(9):2062-2071. doi:10.1002/hbm.21344 PubMed DOI PMC

Gasperini C, Prosperini L, Tintoré M, et al. . Unraveling treatment response in multiple sclerosis: a clinical and MRI challenge. Neurology. 2019;92(4):180-192. doi:10.1212/WNL.0000000000006810 PubMed DOI PMC

Scalfari A, Lederer C, Daumer M, Nicholas R, Ebers GC, Muraro PA. The relationship of age with the clinical phenotype in multiple sclerosis. Mult Scler. 2016;22(13):1750-1758. doi:10.1177/1352458516630396 PubMed DOI

Hwang G, Abdulkadir A, Erus G, et al. . Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning. Brain Commun. 2022;4(3):fcac117. doi:10.1093/braincomms/fcac117 PubMed DOI PMC

Ran C, Yang Y, Ye C, Lv H, Ma T. Brain age vector: a measure of brain aging with enhanced neurodegenerative disorder specificity. Hum Brain Mapp. 2022;43(16):5017-5031. doi:10.1002/hbm.26066 PubMed DOI PMC

Brier MR, Li Z, Ly M, et al. . “Brain age” predicts disability accumulation in multiple sclerosis. Ann Clin Transl Neurol. 2023;10(6):990-1001. doi:10.1002/acn3.51782 PubMed DOI PMC

Denissen S, Engemann DA, De Cock A, et al. . Brain age as a surrogate marker for cognitive performance in multiple sclerosis. Eur J Neurol. 2022;29(10):3039-3049. doi:10.1111/ene.15473 PubMed DOI PMC

Peng H, Gong W, Beckmann CF, Vedaldi A, Smith SM. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal. 2021;68:101871. doi:10.1016/j.media.2020.101871 PubMed DOI PMC

Cole JH, Poudel RPK, Tsagkrasoulis D, et al. . Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017;163:115-124. doi:10.1016/j.neuroimage.2017.07.059 PubMed DOI

Leonardsen EH, Peng H, Kaufmann T, et al. . Deep neural networks learn general and clinically relevant representations of the ageing brain. Neuroimage. 2022;256:119210. doi:10.1016/j.neuroimage.2022.119210 PubMed DOI PMC

Yin C, Imms P, Cheng M, et al. . Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment. Proc Natl Acad Sci USA. 2023;120(2):e2214634120. doi:10.1073/pnas.2214634120 PubMed DOI PMC

Meyer-Moock S, Feng YS, Maeurer M, Dippel FW, Kohlmann T. Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis. BMC Neurol. 2014;14:58. doi:10.1186/1471-2377-14-58 PubMed DOI PMC

Coupé P, Planche V, Mansencal B, et al. . Lifespan neurodegeneration of the human brain in multiple sclerosis. Hum Brain Mapp. 2023;44(17):5602-5611. doi:10.1002/hbm.26464 PubMed DOI PMC

Makhani N, Tremlett H. The multiple sclerosis prodrome. Nat Rev Neurol. 2021;17(8):515-521. doi:10.1038/s41582-021-00519-3 PubMed DOI PMC

Cen S, Gebregziabher M, Moazami S, Azevedo CJ, Pelletier D. Toward precision medicine using a “digital twin” approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis. Sci Rep. 2023;13(1):16279. doi:10.1038/s41598-023-43618-5 PubMed DOI PMC

de Lange AMG, Cole JH. Commentary: correction procedures in brain-age prediction. Neuroimage Clin. 2020;26:102229. doi:10.1016/j.nicl.2020.102229 PubMed DOI PMC

Butler ER, Chen A, Ramadan R, et al. . Pitfalls in brain age analyses. Hum Brain Mapp. 2021;42(13):4092-4101. doi:10.1002/hbm.25533 PubMed DOI PMC

Salter A, Lancia S, Kowalec K, Fitzgerald KC, Marrie RA. Investigating the prevalence of comorbidity in multiple sclerosis clinical trial populations. Neurology. 2024;102(5):e209135. doi:10.1212/WNL.0000000000209135 PubMed DOI PMC

Faghani S, Moassefi M, Rouzrokh P, et al. . Quantifying uncertainty in deep learning of radiologic images. Radiology. 2023;308(2):e222217. doi:10.1148/radiol.222217 PubMed DOI

Ouyang J, Adeli E, Pohl KM, Zhao Q, Zaharchuk G. Representation disentanglement for multi-modal brain MRI analysis. Inf Process Med Imaging. 2021;12729:321-333. doi:10.1007/978-3-030-78191-0_25 PubMed DOI PMC

Liu X, Sanchez P, Thermos S, O'Neil AQ, Tsaftaris SA. Learning disentangled representations in the imaging domain. Med Image Anal. 2022;80:102516. doi:10.1016/j.media.2022.102516 PubMed DOI

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...