Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex
Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and...
| Autores: | , , , , , , , , , |
|---|---|
| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/381211 |
| Acceso en línea: | https://hdl.handle.net/2117/381211 https://dx.doi.org/10.1101/2022.06.23.22276492 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Alzheimer's disease Brain -- Aging Imaging systems in medicine Aprenentatge automàtic Alzheimer, Malaltia d' Cervell -- Envelliment Imatges mèdiques Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
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Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex |
| title |
Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex |
| spellingShingle |
Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex Cumplido Mayoral, Irene Machine learning Alzheimer's disease Brain -- Aging Imaging systems in medicine Aprenentatge automàtic Alzheimer, Malaltia d' Cervell -- Envelliment Imatges mèdiques Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| title_short |
Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex |
| title_full |
Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex |
| title_fullStr |
Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex |
| title_full_unstemmed |
Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex |
| title_sort |
Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex |
| dc.creator.none.fl_str_mv |
Cumplido Mayoral, Irene García Prat, Marina Operto, Grégory Falcón, Carles Shekari, Mahnaz Cacciaglia, Raffaele Milà Alomà, Marta Lorenzini, Luigi Ingala, Silvia Vilaplana Besler, Verónica|||0000-0001-6924-9961 |
| author |
Cumplido Mayoral, Irene |
| author_facet |
Cumplido Mayoral, Irene García Prat, Marina Operto, Grégory Falcón, Carles Shekari, Mahnaz Cacciaglia, Raffaele Milà Alomà, Marta Lorenzini, Luigi Ingala, Silvia Vilaplana Besler, Verónica|||0000-0001-6924-9961 |
| author_role |
author |
| author2 |
García Prat, Marina Operto, Grégory Falcón, Carles Shekari, Mahnaz Cacciaglia, Raffaele Milà Alomà, Marta Lorenzini, Luigi Ingala, Silvia Vilaplana Besler, Verónica|||0000-0001-6924-9961 |
| author2_role |
author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Machine learning Alzheimer's disease Brain -- Aging Imaging systems in medicine Aprenentatge automàtic Alzheimer, Malaltia d' Cervell -- Envelliment Imatges mèdiques Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| topic |
Machine learning Alzheimer's disease Brain -- Aging Imaging systems in medicine Aprenentatge automàtic Alzheimer, Malaltia d' Cervell -- Envelliment Imatges mèdiques Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| description |
Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-b, more advanced stages (AT) of AD pathology and APOE-e4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2022-06-28 2023 2023-01-26 |
| dc.type.none.fl_str_mv |
report http://purl.org/coar/resource_type/c_93fc AO http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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info:eu-repo/semantics/report |
| format |
report |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/381211 https://dx.doi.org/10.1101/2022.06.23.22276492 |
| url |
https://hdl.handle.net/2117/381211 https://dx.doi.org/10.1101/2022.06.23.22276492 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-116907RB-I00 INTELIGENCIA ARTIFICIAL INSESGADA Y EXPLICABLE PARA IMAGENES MEDICAS |
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open access http://purl.org/coar/access_right/c_abf2 |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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1869416376201904128 |
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Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sexCumplido Mayoral, IreneGarcía Prat, MarinaOperto, GrégoryFalcón, CarlesShekari, MahnazCacciaglia, RaffaeleMilà Alomà, MartaLorenzini, LuigiIngala, SilviaVilaplana Besler, Verónica|||0000-0001-6924-9961Machine learningAlzheimer's diseaseBrain -- AgingImaging systems in medicineAprenentatge automàticAlzheimer, Malaltia d'Cervell -- EnvellimentImatges mèdiquesÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeoÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticBrain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-b, more advanced stages (AT) of AD pathology and APOE-e4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17-519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017-SGR-892 and the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033. FB is supported by the NIHR biomedical research center at UCLH. MSC receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 948677), the Instituto de Salud Carlos III (PI19/00155), and from a fellowship from ”la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648 (LCF/BQ/PR21/11840004).Report de recerca signat per 27 autors/es: Irene Cumplido-Mayoral 1,2; Marina García-Prat 1; Grégory Operto 1,3,4; Carles Falcon 1,3,5; Mahnaz Shekari 1,2,3; Raffaele Cacciaglia 1,3,4; Marta Milà-Alomà 1,2,3,4; Luigi Lorenzini 6; Silvia Ingala 6; Alle Meije Wink 6; Henk JMM Mutsaerts 6; Carolina Minguillón 1,3,4; Karine Fauria 1,4; José Luis Molinuevo 1; Sven Haller 7; Gael Chetelat 8,10; Adam Waldman 9; Adam Schwarz 10; Frederik Barkhof 6,11; Ivonne Suridjan 12, 11; Gwendlyn Kollmorgen 13; Anna Bayfield 13; Henrik Zetterberg 14,15,16,17,18; Kaj Blennow 14,15 12; Marc Suárez-Calvet 1,3,4,19; Verónica Vilaplana 20; Juan Domingo Gispert 1,3,5; ALFA study; EPAD study; ADNI study; OASIS study // 1) Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; 2) Universitat Pompeu Fabra, Barcelona, Spain; 3) IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; 4) CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; 5) Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain; 6) Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; 7) CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland; 8) Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France; 9) Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of Edinburgh, Edinburgh, UK; 10) Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA; 11) Institutes of Neurology and Healthcare Engineering, University College London, London, UK; 12) Roche Diagnostics International Ltd, Rotkreuz, Switzerland; 13) Roche Diagnostics GmbH, Penzberg, Germany; 14) Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; 15) Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; 16) Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom; 17) UK Dementia Research Institute at UCL, London, United Kingdom; 18) Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China; 19) Servei de Neurologia, Hospital del Mar, Barcelona, Spain; 20) Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain.20222022-06-2820232023-01-26reporthttp://purl.org/coar/resource_type/c_93fcAOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/2117/381211https://dx.doi.org/10.1101/2022.06.23.22276492reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-116907RB-I00 INTELIGENCIA ARTIFICIAL INSESGADA Y EXPLICABLE PARA IMAGENES MEDICASopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3812112026-05-27T15:37:01Z |
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