Data fusion based on Searchlight analysis for the prediction of Alzheimer's disease Arco Martín, Juan Eloy Ramírez Pérez De Inestrosa, Javier Gorriz Sáez, Juan Manuel Ruz Cámara, María Alzheimer’s Disease Neuroimaging Initiative Alzheimer´s disease Data fusion Ensemble classification Mild cognitive impairment MRI Prediction Searchlight This 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. Conceptualization, 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. In 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. 2021-10-26T07:21:54Z 2021-10-26T07:21:54Z 2021-12-15 info:eu-repo/semantics/article J.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] http://hdl.handle.net/10481/71102 10.1016/j.eswa.2021.115549 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España Elsevier