Giuseppe Pontillo, Ferran Prados, Jordan Colman, Baris Kanber, Omar Abdel-Mannan, Sarmad Al-Araji, Barbara Bellenberg, Alessia Bianchi, Alvino Bisecco, Wallace J 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 Gonzalez-Escamilla, Cristina Granziera, Sergiu Groppa, Yael Hacohen, Hanne F F Harbo, Anna He, Einar A Hogestol, Jens Kuhle, Sara Llufriu, Carsten Lukas, Eloy Martinez-Heras, Silvia Messina, Marcello Moccia, Suraya Mohamud, Riccardo Nistri, Gro O Nygaard, Jacqueline Palace, Maria Petracca, Daniela Pinter, Maria A Rocca, Alex Rovira, Serena Ruggieri, Jaume Sastre-Garriga, Eva M Strijbis, Ahmed T Toosy, Tomas Uher, Paola Valsasina, Manuela Vaneckova, Hugo Vrenken, Jed Wingrove, Charmaine Yam, Menno M Schoonheim, Olga Ciccarelli, James H Cole, Frederik Barkhof
{"title":"利用深度学习将多发性硬化症中的神经变性与衰老区分开来：大脑预测的疾病持续时间差距。","authors":"Giuseppe Pontillo, Ferran Prados, Jordan Colman, Baris Kanber, Omar Abdel-Mannan, Sarmad Al-Araji, Barbara Bellenberg, Alessia Bianchi, Alvino Bisecco, Wallace J 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 Gonzalez-Escamilla, Cristina Granziera, Sergiu Groppa, Yael Hacohen, Hanne F F Harbo, Anna He, Einar A Hogestol, Jens Kuhle, Sara Llufriu, Carsten Lukas, Eloy Martinez-Heras, Silvia Messina, Marcello Moccia, Suraya Mohamud, Riccardo Nistri, Gro O Nygaard, Jacqueline Palace, Maria Petracca, Daniela Pinter, Maria A Rocca, Alex Rovira, Serena Ruggieri, Jaume Sastre-Garriga, Eva M Strijbis, Ahmed T Toosy, Tomas Uher, Paola Valsasina, Manuela Vaneckova, Hugo Vrenken, Jed Wingrove, Charmaine Yam, Menno M Schoonheim, Olga Ciccarelli, James H Cole, Frederik Barkhof","doi":"10.1212/WNL.0000000000209976","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objectives: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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, <i>R</i><sup>2</sup> = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: <i>r</i> = 0.06 [0.00-0.13], <i>p</i> = 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], <i>p</i> < 0.001). DD gap significantly explained EDSS changes (<i>B</i> = 0.060 [0.038-0.082], <i>p</i> < 0.001), adding to BAG (Δ<i>R</i><sup>2</sup> = 0.012, <i>p</i> < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (<i>r</i> = 0.50 [0.39-0.60], <i>p</i> < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (Δ<i>R</i><sup>2</sup> = 0.064, <i>p</i> < 0.001).</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":19256,"journal":{"name":"Neurology","volume":"103 10","pages":"e209976"},"PeriodicalIF":7.7000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540460/pdf/","citationCount":"0","resultStr":"{\"title\":\"Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.\",\"authors\":\"Giuseppe Pontillo, Ferran Prados, Jordan Colman, Baris Kanber, Omar Abdel-Mannan, Sarmad Al-Araji, Barbara Bellenberg, Alessia Bianchi, Alvino Bisecco, Wallace J 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 Gonzalez-Escamilla, Cristina Granziera, Sergiu Groppa, Yael Hacohen, Hanne F F Harbo, Anna He, Einar A Hogestol, Jens Kuhle, Sara Llufriu, Carsten Lukas, Eloy Martinez-Heras, Silvia Messina, Marcello Moccia, Suraya Mohamud, Riccardo Nistri, Gro O Nygaard, Jacqueline Palace, Maria Petracca, Daniela Pinter, Maria A Rocca, Alex Rovira, Serena Ruggieri, Jaume Sastre-Garriga, Eva M Strijbis, Ahmed T Toosy, Tomas Uher, Paola Valsasina, Manuela Vaneckova, Hugo Vrenken, Jed Wingrove, Charmaine Yam, Menno M Schoonheim, Olga Ciccarelli, James H Cole, Frederik Barkhof\",\"doi\":\"10.1212/WNL.0000000000209976\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and objectives: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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, <i>R</i><sup>2</sup> = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: <i>r</i> = 0.06 [0.00-0.13], <i>p</i> = 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], <i>p</i> < 0.001). DD gap significantly explained EDSS changes (<i>B</i> = 0.060 [0.038-0.082], <i>p</i> < 0.001), adding to BAG (Δ<i>R</i><sup>2</sup> = 0.012, <i>p</i> < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (<i>r</i> = 0.50 [0.39-0.60], <i>p</i> < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (Δ<i>R</i><sup>2</sup> = 0.064, <i>p</i> < 0.001).</p><p><strong>Discussion: </strong>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.</p>\",\"PeriodicalId\":19256,\"journal\":{\"name\":\"Neurology\",\"volume\":\"103 10\",\"pages\":\"e209976\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540460/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1212/WNL.0000000000209976\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/11/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1212/WNL.0000000000209976","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/4 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.
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.
期刊介绍：
Neurology, the official journal of the American Academy of Neurology, aspires to be the premier peer-reviewed journal for clinical neurology research. Its mission is to publish exceptional peer-reviewed original research articles, editorials, and reviews to improve patient care, education, clinical research, and professionalism in neurology.
As the leading clinical neurology journal worldwide, Neurology targets physicians specializing in nervous system diseases and conditions. It aims to advance the field by presenting new basic and clinical research that influences neurological practice. The journal is a leading source of cutting-edge, peer-reviewed information for the neurology community worldwide. Editorial content includes Research, Clinical/Scientific Notes, Views, Historical Neurology, NeuroImages, Humanities, Letters, and position papers from the American Academy of Neurology. The online version is considered the definitive version, encompassing all available content.
Neurology is indexed in prestigious databases such as MEDLINE/PubMed, Embase, Scopus, Biological Abstracts®, PsycINFO®, Current Contents®, Web of Science®, CrossRef, and Google Scholar.
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