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Open Access Highly Accessed Research article

Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data

Jieping Ye1*, Michael Farnum2, Eric Yang2, Rudi Verbeeck2, Victor Lobanov2, Nandini Raghavan2, Gerald Novak2, Allitia DiBernardo2, Vaibhav A Narayan2 and for the Alzheimer’s Disease Neuroimaging Initiative

Author Affiliations

1 Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona, State University, Tempe, AZ, USA

2 Johnson & Johnson Pharmaceutical Research & Development, LLC, Titusville, NJ, USA

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BMC Neurology 2012, 12:46  doi:10.1186/1471-2377-12-46

Published: 25 June 2012

Abstract

Background

Patients 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.

Methods

We 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.

Results

We 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.

Conclusions

Our 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.