A Composite Multivariate Polygenic and Neuroimaging Score for Prediction of Conversion to Alzheimer's Disease

• Published in: 2012 International Workshop on Pattern Recognition in NeuroImaging pp. 105 - 108
• Main Authors: Filipovych, R., Gaonkar, B., Davatzikos, C.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 2012
• Subjects: Accuracy; Alzheimer's disease; Genetics; Imaging; imaging genetics; mild cognitive impairment; multivariate analysis; Neuroimaging; pattern classification; Sociology; pattern classification; mild cognitive impairment; imaging genetics; Alzheimer's disease; multivariate analysis
• ISBN: 1467321826; 9781467321822
• ISSN: 2330-9989
• DOI: 10.1109/PRNI.2012.9

Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) are characterized by widespread pathological changes in the brain. At the same time, Alzheimer's disease is heritable with complex genetic underpinnings that may influence the timing of the related pathological changes in the brain and can affect the progression from MCI to AD. In this paper, we present a multivariate imaging genetics approach for prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. We employ multivariate pattern recognition approaches to obtain neuroimaging and polygenic discriminators between the healthy individuals and AD patients. We then design, in a linear manner, a composite imaging-genetic score for prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. We apply our approach within the Alzheimer's Disease Neuroimaging Initiative and show that the integration of polygenic and neuroimaging information improves prediction of conversion to AD.