title: Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer's Disease.
abstract: Decision-making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy controls (CTRL) are becoming widespread prognostic tools for subjects with mild cognitive impairment (MCI). This study compares the performances of three classification methods based on support vector machines (SVMs), using as initial sets of brain voxels (ie, features): (1) the segmented grey matter (GM); (2) regions of interest (ROIs) by voxel-wise t-test filtering; (3) parceled ROIs, according to prior knowledge. The recursive feature elimination (RFE) is applied in all cases to investigate whether feature reduction improves the classification accuracy. We analyzed more than 600 AD Neuroimaging Initiative (ADNI) subjects, training the SVMs on the AD/CTRL dataset, and evaluating them on a trial MCI dataset. The classification performance, evaluated as the area under the receiver operating characteristic curve (AUC), reaches AUC = (88.9 ± .5)% in 20-fold cross-validation on the AD/CTRL dataset, when the GM is classified as a whole. The highest discrimination accuracy between MCI converters and nonconverters is achieved when the SVM-RFE is applied to the whole GM: with AUC reaching (70.7 ± .9)%, it outperforms both ROI-based approaches in predicting the AD conversion.
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Retico
authors.affiliation: Dipartimento di Fisica, Università degli Studi di Genova, Genova, Italy.|Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Genova, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Bosco
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy.
authors.forename: Piergiorgio
authors.initials: P
authors.lastname: Cerello
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy.|Dipartimento di Fisica, Università di Torino, Torino, Italy.
authors.forename: Elisa
authors.initials: E
authors.lastname: Fiorina
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Genova, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Chincarini
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.|Dipartimento di Fisica, Università di Pisa, Pisa, Italy.
authors.forename: Maria Evelina
authors.initials: ME
authors.lastname: Fantacci
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000473:pathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Alzheimer's disease; MRI; classification; mild cognitive impairment; support vector machines