Data fusion based on Searchlight analysis for the prediction of Alzheimer's disease
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AuthorArco Martín, Juan Eloy; Ramírez Pérez De Inestrosa, Javier; Gorriz Sáez, Juan Manuel; Ruz Cámara, María; Alzheimer’s Disease Neuroimaging Initiative
Alzheimer´s diseaseData fusionEnsemble classificationMild cognitive impairmentMRIPredictionSearchlight
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]
SponsorshipMINECO/FEDER, Spain RTI2018-098913-B-I00; General Secretariat of Universities, Research and Technology; Junta de Andalucia A-TIC-117-UGR18; University of Granada, Spain through grant "Contratos puente''; Alzheimer's Disease Neuroimaging Initiative, United States (ADNI; National Institutes of Health) U01 AG024904; National Institute on Aging, United States; National Institute of Biomedical Imaging and Bioengineering, United States; Abbott Laboratories; AstraZeneca; Bayer AG; Bristol-Myers Squibb; Eisai Co Ltd; Elan Corporation; Roche Holding Genentech; General Electric GE Healthcare; GlaxoSmithKline; Innogenetics; Johnson & Johnson Johnson & Johnson USA; Eli Lilly; Medpace, Inc.; Merck & Company; Novartis AG; Pfizer; F. Hoffman-La Roche; Merck & Company Schering Plough Corporation; Synarc, Inc.; United States Department of Health & Human Services National Institutes of Health (NIH) - USA; Northern California Institute for Research and Education; United States Department of Health & Human Services National Institutes of Health (NIH) - USA P30 AG010129; Dana Foundation, United States
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.