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dc.contributor.authorArco Martín, Juan Eloy 
dc.contributor.authorRamírez Pérez De Inestrosa, Javier 
dc.contributor.authorGorriz Sáez, Juan Manuel 
dc.contributor.authorRuz Cámara, María 
dc.contributor.authorAlzheimer’s Disease Neuroimaging Initiative
dc.date.accessioned2021-10-26T07:21:54Z
dc.date.available2021-10-26T07:21:54Z
dc.date.issued2021-12-15
dc.identifier.citationJ.E. Arco et al. Data fusion based on Searchlight analysis for the prediction of Alzheimer’s disease. Expert Systems With Applications 185 (2021) 115549. [https://doi.org/10.1016/j.eswa.2021.115549]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/71102
dc.descriptionThis work was supported by the MINECO/FEDER, Spain under the RTI2018-098913-B-I00 project, the General Secretariat of Universities, Research and Technology, Junta de Andalucia, Spain under the Excellence FEDER Project A-TIC-117-UGR18, and University of Granada, Spain through grant "Contratos puente'' to J.E.A. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative, United States (ADNI; National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, United States, the National Institute of Biomedical Imaging and Bioengineering, United States, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro-Imaging at the University of California, Los Angeles. This research was also supported by NIH, Spain grants P30 AG010129, K01 AG030514, and the Dana Foundation, United States.es_ES
dc.descriptionConceptualization, Methodology, Software, Investigation, Writing – original draft, Writing – review & editing. Javier Ramírez: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. Juan M. Górriz: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. María Ruz: Conceptualization, Validation, Supervision, Investigation, Writing – original draft, Writing – review & editing.es_ES
dc.description.abstractIn recent years, several computer-aided diagnosis (CAD) systems have been proposed for an early identification of dementia. Although these approaches have mostly used the transformation of data into a different feature space, more precise information can be gained from a Searchlight strategy. The current study presents a data fusion classification system that employs magnetic resonance imaging (MRI) and neuropsychological tests to distinguish between Mild-Cognitive Impairment (MCI) patients that convert to Alzheimer's disease (AD) and those that remain stable. Specifically, this method uses a nested cross-validation procedure to compute the optimum contribution of each data modality in the final decision. The model employs Support-Vector Machine (SVM) classifiers for both data modalities and is combined with Searchlight when applied to neuroimaging. We compared the performance of our system with an alternative based on Principal Component Analysis (PCA) for dimensionality reduction. Results show that Searchlight outperformed PCA both for uni/multimodal classification, obtaining a maximum accuracy of 80.9% when combining data from six and twelve months before patients converted to AD. Moreover, Searchlight allowed the identification of the most informative regions at different stages of the longitudinal study, which can be crucial for a better understanding of the development of AD. Additionally, results do not depend on the parcellations provided by a specific brain atlas, which manifests the robustness and the spatial precision of the method proposed.es_ES
dc.description.sponsorshipMINECO/FEDER, Spain RTI2018-098913-B-I00es_ES
dc.description.sponsorshipGeneral Secretariat of Universities, Research and Technologyes_ES
dc.description.sponsorshipJunta de Andalucia A-TIC-117-UGR18es_ES
dc.description.sponsorshipUniversity of Granada, Spain through grant "Contratos puente''es_ES
dc.description.sponsorshipAlzheimer's Disease Neuroimaging Initiative, United States (ADNI; National Institutes of Health) U01 AG024904es_ES
dc.description.sponsorshipNational Institute on Aging, United Stateses_ES
dc.description.sponsorshipNational Institute of Biomedical Imaging and Bioengineering, United Stateses_ES
dc.description.sponsorshipAbbott Laboratorieses_ES
dc.description.sponsorshipAstraZenecaes_ES
dc.description.sponsorshipBayer AGes_ES
dc.description.sponsorshipBristol-Myers Squibbes_ES
dc.description.sponsorshipEisai Co Ltdes_ES
dc.description.sponsorshipElan Corporationes_ES
dc.description.sponsorshipRoche Holding Genenteches_ES
dc.description.sponsorshipGeneral Electric GE Healthcarees_ES
dc.description.sponsorshipGlaxoSmithKlinees_ES
dc.description.sponsorshipInnogeneticses_ES
dc.description.sponsorshipJohnson & Johnson Johnson & Johnson USAes_ES
dc.description.sponsorshipEli Lillyes_ES
dc.description.sponsorshipMedpace, Inc.es_ES
dc.description.sponsorshipMerck & Companyes_ES
dc.description.sponsorshipNovartis AGes_ES
dc.description.sponsorshipPfizeres_ES
dc.description.sponsorshipF. Hoffman-La Rochees_ES
dc.description.sponsorshipMerck & Company Schering Plough Corporationes_ES
dc.description.sponsorshipSynarc, Inc.es_ES
dc.description.sponsorshipUnited States Department of Health & Human Services National Institutes of Health (NIH) - USAes_ES
dc.description.sponsorshipNorthern California Institute for Research and Educationes_ES
dc.description.sponsorshipUnited States Department of Health & Human Services National Institutes of Health (NIH) - USA P30 AG010129es_ES
dc.description.sponsorshipDana Foundation, United Stateses_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectAlzheimer´s diseasees_ES
dc.subjectData fusiones_ES
dc.subjectEnsemble classificationes_ES
dc.subjectMild cognitive impairmentes_ES
dc.subjectMRIes_ES
dc.subjectPredictiones_ES
dc.subjectSearchlightes_ES
dc.titleData fusion based on Searchlight analysis for the prediction of Alzheimer's diseasees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.eswa.2021.115549
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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