Comparison of feature representations in MRI-based MCI-to-AD conversion prediction

dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.contributor.authorGómez Sancho, Marta
dc.contributor.authorGómez Verdejo, Vanessa
dc.contributor.authorTohka, Jussi
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.description.abstractAlzheimer's disease (AD) is a progressive neurological disorder in which the death of brain cells causes memory loss and cognitive decline. The identification of at-risk subjects yet showing no dementia symptoms but who will later convert to AD can be crucial for the effective treatment of AD. For this, Magnetic Resonance Imaging (MRI) is expected to play a crucial role. During recent years, several Machine Learning (ML) approaches to AD-conversion prediction have been proposed using different types of MRI features. However, few studies comparing these different feature representations exist, and the existing ones do not allow to make definite conclusions. We evaluated the performance of various types of MRI features for the conversion prediction: voxel-based features extracted based on voxel-based morphometry, hippocampus volumes, volumes of the entorhinal cortex, and a set of regional volumetric, surface area, and cortical thickness measures across the brain. Regional features consistently yielded the best performance over two classifiers (Support Vector Machines and Regularized Logistic Regression), and two datasets studied. However, the performance difference to other features was not statistically significant. There was a consistent trend of age correction improving the classification performance, but the improvement reached statistical significance only rarely.en
dc.description.sponsorshipData collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. J. Tohka's work was supported by the Academy of Finland and V. Gómez-Verdejo's work has been partly funded by the Spanish MINECO grant TEC2014-52289R, TEC2016-81900-REDT/AEI and TEC2017-83838-R.en
dc.identifier.bibliographicCitationGómez-Sancho, M., Tohka, J. & Gómez-Verdejo, V. (2018). Comparison of feature representations in MRI-based MCI-to-AD conversion prediction. Magnetic Resonance Imaging, 50, 84–95.
dc.identifier.publicationtitleMagnetic Resonance Imagingen
dc.relation.projectIDGobierno de España. TEC2017-83838-Res
dc.relation.projectIDGobierno de España. TEC2016-81900-REDTes
dc.relation.projectIDGobierno de España. TEC2014-52289-Res
dc.rights© 2018 Elsevier Inc. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.subject.otherAlzheimer's diseaseen
dc.subject.otherMagnetic resonance imagingen
dc.subject.otherMachine learningen
dc.subject.otherFeature representationsen
dc.titleComparison of feature representations in MRI-based MCI-to-AD conversion predictionen
dc.typeresearch article*
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