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Title | Sparse bayesian learning for identifying imaging biomarkers in AD prediction. |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Shen L, Qi Y, Kim S, Nho K, Wan J, Risacher SL, Saykin AJ |
Corporate Authors | ADNI |
Journal | Med Image Comput Comput Assist Interv |
Volume | 13 |
Issue | Pt 3 |
Pagination | 611-8 |
Date Published | 09/2010 |
Keywords | Algorithms, Alzheimer Disease, Artificial Intelligence, Bayes Theorem, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity |
Abstract | 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. |
DOI | 10.1007/978-3-642-15711-0_76 |
Alternate Journal | Med Image Comput Comput Assist Interv |
PubMed ID | 20879451 |
PubMed Central ID | PMC2951627 |
Grant List | 1RC 2AG036535 / RC / CCR NIH HHS / United States R01 AG019771 / AG / NIA NIH HHS / United States UL1 RR025761 / RR / NCRR NIH HHS / United States R03 EB008674-01 / EB / NIBIB NIH HHS / United States P30 AG10133 / AG / NIA NIH HHS / United States P30 AG010133 / AG / NIA NIH HHS / United States P30 AG010133-18S1 / AG / NIA NIH HHS / United States U01 AG032984 / AG / NIA NIH HHS / United States R03 EB008674 / EB / NIBIB NIH HHS / United States U01 AG024904 / AG / NIA NIH HHS / United States U19 AG010483 / AG / NIA NIH HHS / United States R01 AG019771-07 / AG / NIA NIH HHS / United States RC2 AG036535 / AG / NIA NIH HHS / United States UL1 TR001108 / TR / NCATS NIH HHS / United States U01 AG024904-06 / AG / NIA NIH HHS / United States UL1 RR025761-01 / RR / NCRR NIH HHS / United States R01 AG19771 / AG / NIA NIH HHS / United States U01 AG032984-01 / AG / NIA NIH HHS / United States RR025761 / RR / NCRR NIH HHS / United States RC2 AG036535-01 / AG / NIA NIH HHS / United States |