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Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification

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Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification

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dc.contributor.author Hett, Kilian es_ES
dc.contributor.author Ta, Vinh-Thong es_ES
dc.contributor.author Catheline, Gwenaelle es_ES
dc.contributor.author Tourdias, Thomas es_ES
dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Coupé, Pierrick es_ES
dc.contributor.author Alzheimers Disease Neuroimaging Initiative es_ES
dc.date.accessioned 2021-02-04T04:32:34Z
dc.date.available 2021-02-04T04:32:34Z
dc.date.issued 2019-09-25 es_ES
dc.identifier.issn 2045-2322 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160695
dc.description.abstract [EN] Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer's disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus. es_ES
dc.description.sponsorship This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) thanks to the funding of the project DeepvolBrain (ANR-18-CE45-0013) and in the frame of the Investments for the future Program IdEx Bordeaux, Cluster of excellence CPU and labex TRAIL (BigDataBrain ANR-10-LABX-57). The study presented in this work is a part of the thesis entitled "Multi-scale and multimodal imaging biomarkers for the early detection of Alzheimer's disease" defended by the same author. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Biogen; Bristol-Myes Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffman-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Pharmaceutical Research & Development LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute of Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. es_ES
dc.language Inglés es_ES
dc.publisher Nature Publishing Group es_ES
dc.relation.ispartof Scientific Reports es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1038/s41598-019-49970-9 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-LABX-0057/FR/Translational Research and Advanced Imaging Laboratory/TRAIL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-18-CE45-0013/FR/Deep Learning for Volumetric Brain Analysis: Towards BigData in Neuroscience/DeepVolBrain/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//U01AG024904/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Hett, K.; Ta, V.; Catheline, G.; Tourdias, T.; Manjón Herrera, JV.; Coupé, P.; Alzheimers Disease Neuroimaging Initiative (2019). Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification. Scientific Reports. 9:1-16. https://doi.org/10.1038/s41598-019-49970-9 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1038/s41598-019-49970-9 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 16 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.identifier.pmid 31554909 es_ES
dc.identifier.pmcid PMC6761169 es_ES
dc.relation.pasarela S\403813 es_ES
dc.contributor.funder Pfizer es_ES
dc.contributor.funder National Institute on Aging, EEUU es_ES
dc.contributor.funder National Institutes of Health, EEUU es_ES
dc.contributor.funder Agence Nationale de la Recherche, Francia es_ES
dc.contributor.funder National Institute of Neurological Disorders and Stroke, EEUU es_ES
dc.contributor.funder National Institute of Biomedical Imaging and Bioengineering, EEUU es_ES
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