Brain Effective Connectivity Modeling for Alzheimer's Disease by Sparse Gaussian Bayesian Network.
Brain Effective Connectivity Modeling for Alzheimer's Disease by Sparse Gaussian Bayesian Network.
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DOI：
10.1145/2020408.2020562
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Reiman E
中科院分区：
文献类型：
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作者：
Huang S;Li J;Ye J;Fleisher A;Chen K;Wu T;Reiman E
最近的研究表明，阿尔茨海默病（AD）与大脑连接网络的改变有关。一种被称为有效连接的连接，被定义为大脑区域之间的定向关系，对大脑功能至关重要。然而，关于AD有效连通性的建模以及与正常对照（NC）的差异表征的研究很少。在本文中，我们研究稀疏贝叶斯网络（BN）的有效连接建模。具体来说，我们提出了一种新的BN结构学习公式，其中包括一个l1范数惩罚项来施加稀疏性，另一个惩罚项来确保学习到的BN是一个有向无环图-这是BN的必要性质。通过对11个具有不同样本量的大中型基准网络的理论分析和广泛实验，我们表明，与10种竞争算法相比，所提出的方法大大提高了学习精度和可扩展性。将该方法应用于42例AD和67例NC受试者的FDG-PET图像，分别确定了AD和NC的有效连通性模型。我们的研究表明，AD的有效连通性与NC在全球范围内的有效连通性、叶内、叶间和半球间的有效连通性分布以及与特定大脑区域相关的有效连通性等方面存在许多不同。这些发现与已知的阿尔茨海默病病理和临床进展相一致，并将有助于阿尔茨海默病知识的发现。
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, interlobe, 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.