NDLI: Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network

Content Provider	IEEE Xplore Digital Library
Author	Luping Zhou Lei Wang Lingqiao Liu Ogunbona, P. Dinggang Shen
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia (Luping Zhou; Lei Wang; Ogunbona, P.) \|\| Res. Sch. of Eng., Australian Nat. Univ., Canberra, ACT, Australia (Lingqiao Liu) \|\| Dept. of Radiol. & BRIC, Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Dinggang Shen)
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.
Starting Page	2243
Ending Page	2250
File Size	391550
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Vectors Bayes methods Optimization Support vector machines Educational institutions Magnetic resonance imaging Alzheimer's Disease Discriminative learning sparse Bayesian Network Brain connectivity analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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