Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Jun 25:12:46.
doi: 10.1186/1471-2377-12-46.

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

Jieping Ye  1 , Michael Farnum, Eric Yang, Rudi Verbeeck, Victor Lobanov, Nandini Raghavan, Gerald Novak, Allitia DiBernardo, Vaibhav A Narayan; Alzheimer’s Disease Neuroimaging Initiative
Affiliations

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

Jieping Ye et al. BMC Neurol. .

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
The number of MCI to AD conversions at each time point (6, 12, 18, 24, 36, 48 months).
Figure 2
Figure 2
The top 15 features (included in Biosignature-15) identified by sparse logistic regression with stability selection. The vertical axis is the stability score multiplied by 100 (between 0 and 100) and indicates the importance of the features. WM indicates White Matter.
Figure 3
Figure 3
The AUC Curve of Biomarkers-15 by sparse logistic regression with stability selection.
Figure 4
Figure 4
The change of the AUC score when the number of selected features varies.
See this image and copyright information in PMC

References

    1. Alzheimer’s Association. Alzheimer’s Disease Facts and Figures. Alzheimer’s & Dementia. 2010;6(2):158–194. - PubMed
    1. Petersen RC. Mild cognitive impairment clinical trials. Nat Rev. 2003;2:646–653. doi: 10.1038/nrd1155. - DOI - PubMed
    1. Petersen RC. et al.Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56:1133–1142. doi: 10.1212/WNL.56.9.1133. - DOI - PubMed
    1. Devanand DP. et al.Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology. 2007;68:828–836. doi: 10.1212/01.wnl.0000256697.20968.d7. - DOI - PubMed
    1. Fennema-Notestine C. et al.Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Hum Brain Mapp. 2009;30(10):3238–3253. doi: 10.1002/hbm.20744. - DOI - PMC - PubMed

Publication types