Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI dataReportar como inadecuado




Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

BMC Neurology

, 12:46

First Online: 25 June 2012Received: 01 November 2011Accepted: 17 May 2012

Abstract

BackgroundPatients with Mild Cognitive Impairment MCI are at high risk of progression to Alzheimer’s dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD neuroimaging, demographic, genetic and cognitive measures may contain complementary information for diagnosis and prognosis of AD.

MethodsWe have conducted a comprehensive study using a large number of samples from the Alzheimer’s Disease Neuroimaging Initiative ADNI to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: 1 we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls 2 we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and 3 we apply sparse logistic regression with stability selection to ADNI data for robust feature selection.

ResultsWe have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features predictors including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587.

ConclusionsOur results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2377-12-46 contains supplementary material, which is available to authorized users.

Download fulltext PDF



Autor: Jieping Ye - Michael Farnum - Eric Yang - Rudi Verbeeck - Victor Lobanov - Nandini Raghavan - Gerald Novak - Allitia DiBer

Fuente: https://link.springer.com/



DESCARGAR PDF




Documentos relacionados