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Discriminative brain effective connectivity analysis for Alzheimer's disease: A kernel learning approach upon sparse gaussian bayesian network

Conference Paper


Abstract


  • Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivity-based biomarkers for the Alzheimer's disease (AD). In this regard, brain ``effective connectivity" analysis, which studies the causal relationship among brain regions, is highly challenging and of many research opportunities. Most of the existing works in this field use generative methods. Despite their success in data representation and other important merits, generative methods are not necessarily discriminative, which may cause the ignorance of subtle but critical disease-induced changes. In this paper, we propose a learning-based approach that integrates the benefits of generative and discriminative methods to recover effective connectivity. In particular, we employ Fisher kernel to bridge the generative models of sparse Bayesian networks (SBN) and the discriminative classifiers of SVMs, and convert the SBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. Our method is able to simultaneously boost the discriminative power of both the generative SBN models and the SBN-induced SVM classifiers via Fisher kernel. The proposed method is tested on analyzing brain effective connectivity for AD from ADNI data, and demonstrates significant improvements over the state-of-the-art work.

Publication Date


  • 2013

Citation


  • Zhou, L., Wang, L., Liu, L., Ogunbona, P. & Shen, D. (2013). Discriminative brain effective connectivity analysis for Alzheimer's disease: A kernel learning approach upon sparse gaussian bayesian network. IEEE Conference on Computer Vision and Pattern Recognition (pp. 2243-2250). Portland, United States: The Institute of Electrical and Electronics Engineers Inc.

Scopus Eid


  • 2-s2.0-84887361661

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1812

Has Global Citation Frequency


Start Page


  • 2243

End Page


  • 2250

Place Of Publication


  • Portland, United States

Abstract


  • Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivity-based biomarkers for the Alzheimer's disease (AD). In this regard, brain ``effective connectivity" analysis, which studies the causal relationship among brain regions, is highly challenging and of many research opportunities. Most of the existing works in this field use generative methods. Despite their success in data representation and other important merits, generative methods are not necessarily discriminative, which may cause the ignorance of subtle but critical disease-induced changes. In this paper, we propose a learning-based approach that integrates the benefits of generative and discriminative methods to recover effective connectivity. In particular, we employ Fisher kernel to bridge the generative models of sparse Bayesian networks (SBN) and the discriminative classifiers of SVMs, and convert the SBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. Our method is able to simultaneously boost the discriminative power of both the generative SBN models and the SBN-induced SVM classifiers via Fisher kernel. The proposed method is tested on analyzing brain effective connectivity for AD from ADNI data, and demonstrates significant improvements over the state-of-the-art work.

Publication Date


  • 2013

Citation


  • Zhou, L., Wang, L., Liu, L., Ogunbona, P. & Shen, D. (2013). Discriminative brain effective connectivity analysis for Alzheimer's disease: A kernel learning approach upon sparse gaussian bayesian network. IEEE Conference on Computer Vision and Pattern Recognition (pp. 2243-2250). Portland, United States: The Institute of Electrical and Electronics Engineers Inc.

Scopus Eid


  • 2-s2.0-84887361661

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1812

Has Global Citation Frequency


Start Page


  • 2243

End Page


  • 2250

Place Of Publication


  • Portland, United States