Sparse bayesian learning for identifying imaging biomarkers in AD prediction.

Li Shen, Yuan Qi, Sungeun Kim, Kwangsik Nho, Jing Wan, Shannon L. Risacher, Andrew Saykin, ADNI

Research output: Chapter in Book/Report/Conference proceedingChapter

19 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages611-618
Number of pages8
Volume13
EditionPt 3
StatePublished - 2010

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Linear Models
Alzheimer Disease
Biomarkers
Learning
Bayes Theorem
Support Vector Machine

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Shen, L., Qi, Y., Kim, S., Nho, K., Wan, J., Risacher, S. L., ... ADNI (2010). Sparse bayesian learning for identifying imaging biomarkers in AD prediction. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 13, pp. 611-618)

Sparse bayesian learning for identifying imaging biomarkers in AD prediction. / Shen, Li; Qi, Yuan; Kim, Sungeun; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Saykin, Andrew; ADNI.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 3. ed. 2010. p. 611-618.

Research output: Chapter in Book/Report/Conference proceedingChapter

Shen, L, Qi, Y, Kim, S, Nho, K, Wan, J, Risacher, SL, Saykin, A & ADNI 2010, Sparse bayesian learning for identifying imaging biomarkers in AD prediction. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 13, pp. 611-618.
Shen L, Qi Y, Kim S, Nho K, Wan J, Risacher SL et al. Sparse bayesian learning for identifying imaging biomarkers in AD prediction. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 13. 2010. p. 611-618
Shen, Li ; Qi, Yuan ; Kim, Sungeun ; Nho, Kwangsik ; Wan, Jing ; Risacher, Shannon L. ; Saykin, Andrew ; ADNI. / Sparse bayesian learning for identifying imaging biomarkers in AD prediction. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 3. ed. 2010. pp. 611-618
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