Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

Center for Neuroimaging, Department of Radiology and Imaging Sciences, USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 09/2010; 13(Pt 3):611-8. DOI: 10.1007/978-3-642-15711-0_76
Source: PubMed


We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer's disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse models that is easy to interpret. PARD selects the model with the best estimate of the predictive performance instead of choosing the one with the largest marginal model likelihood. Comparative study with support vector machine (SVM) shows that ARD/PARD in general outperform SVM in terms of prediction accuracy. Additional comparison with surface-based general linear model (GLM) analysis shows that regions with strongest signals are identified by both GLM and ARD/PARD. While GLM P-map returns significant regions all over the cortex, ARD/PARD provide a small number of relevant and meaningful imaging markers with predictive power, including both cortical and subcortical measures.

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    • "Sparse learning methods enjoy strong theoretical properties (Candès and Wakin, 2008; Donoho, 2006) and are receiving increased attention in many application areas (Beck and Teboulle, 2009; Candès et al., 2006; Figueiredo et al., 2007; Wu et al., 2009). Sparse learning has also been applied in neuroimaging to study genetic influences on the brain (Hibar et al., 2011; Kohannim et al., 2011; Le Floch et al., 2011; Vounou et al., 2010, 2012; Wang et al., 2012a), functional connectivity (Huang et al., 2010; Ryali et al., 2012), and for outcome predictions (Shen et al., 2010; Stonnington et al., 2010; Sun et al., 2009a; Wang et al., 2010a, 2010b, 2011a). In many computer vision, medical imaging and bioinformatics applications, using sparsity as a prior leads to state-of-the-art results (Liu and Ye, 2010; Liu et al., 2010b; Sun et al., 2009a; Wright et al., 2009). "
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    • "Machine learning methods have been widely employed to predict Alzheimer's disease (AD) status using imaging genetics measures (Batmanghelich et al., 2009; Fan et al., 2008; Hinrichs et al., 2009b; Shen et al., 2010a). Since AD is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions, regression models have also been investigated to predict clinical scores from structural, such as magnetic resonance imaging (MRI), and/or molecular, such as fluorodeoxyglucose positron emission tomography (FDG-PET), neuroimaging data (Stonnington et al., 2010; Walhovd et al., 2010). "
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