NDLI: Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network

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Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network

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Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network

Content Provider	ACM Digital Library
Author	Wu, Teresa Fleisher, Adam Chen, Kewei Li, Jing Huang, Shuai Ye, Jieping Reiman, Eric
Abstract	Recent studies have shown that Alzheimer's disease (AD) is related to alteration in brain connectivity networks. One type of connectivity, called effective connectivity, defined as the directional relationship between brain regions, is essential to brain function. However, there have been few studies on modeling the effective connectivity of AD and characterizing its difference from normal controls (NC). In this paper, we investigate the sparse Bayesian Network (BN) for effective connectivity modeling. Specifically, we propose a novel formulation for the structure learning of BNs, which involves one L1-norm penalty term to impose sparsity and another penalty to ensure the learned BN to be a directed acyclic graph - a required property of BNs. We show, through both theoretical analysis and extensive experiments on eleven moderate and large benchmark networks with various sample sizes, that the proposed method has much improved learning accuracy and scalability compared with ten competing algorithms. We apply the proposed method to FDG-PET images of 42 AD and 67 NC subjects, and identify the effective connectivity models for AD and NC, respectively. Our study reveals that the effective connectivity of AD is different from that of NC in many ways, including the global-scale effective connectivity, intra-lobe, inter-lobe, and inter-hemispheric effective connectivity distributions, as well as the effective connectivity associated with specific brain regions. These findings are consistent with known pathology and clinical progression of AD, and will contribute to AD knowledge discovery.
Starting Page	931
Ending Page	939
Page Count	9
File Format	PDF
ISBN	9781450308137
DOI	10.1145/2020408.2020562
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Neuroimaging Fdg-pet Sparse learning Alzheimer's disease Bayesian network Brain network
Content Type	Text
Resource Type	Article

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