Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

摘要：

We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimers 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 Pmap 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.

作者: Li Shen Yuan Qi Sungeun Kim Kwangsik Nho ingWan Shannon L.Risacher Andrew J.Saykin

作者单位: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Center for Computational Biol Departments of Computer Science, Statistics and Biology,Purdue University, 305 N.University Street, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Center for Neuroimaging, Department of Radiology and Imaging Sciences

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 611–618

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）