Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
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发表时间：
2009-12
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通讯作者：
Shuai Huang;Jing Li;Liang Sun;Jun Liu;Teresa Wu;Kewei Chen;A. Fleisher;E. Reiman;Jieping Ye-Jieping
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作者：
Shuai Huang;Jing Li;Liang Sun;Jun Liu;Teresa Wu;Kewei Chen;A. Fleisher;E. Reiman;Jieping Ye-Jieping
神经影像学技术的最新进展为阿尔茨海默病（AD）这一最常见的痴呆形式的有效诊断提供了巨大的潜力。以往的研究表明，AD与脑功能网络的改变密切相关，即，不同大脑区域之间的功能连接。在本文中，我们考虑从神经影像学中学习功能性脑连接的问题，这对于识别用于区分正常对照（NC），轻度认知障碍（MCI）患者和AD患者的基于图像的标记物具有很大的希望。更具体地说，我们研究稀疏逆协方差估计（SICE），也称为探索性高斯图形模型，用于大脑连接建模。特别是，我们应用SICE学习和分析功能的大脑连接模式，从不同的主题组，基于SICE的一个关键属性，称为“单调属性”，我们在本文中建立。我们对42例AD、116例MCI和67例NC受试者的神经影像PET数据的实验结果揭示了一些有趣的连接模式，这些模式与文献结果一致，也有助于AD的知识发现。
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer's disease (AD), the most common form of dementia. Previous studies have shown that AD is closely related to the alternation in the functional brain network, i.e., the functional connectivity among different brain regions. In this paper, we consider the problem of learning functional brain connectivity from neuroimaging, which holds great promise for identifying image-based markers used to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. More specifically, we study sparse inverse covariance estimation (SICE), also known as exploratory Gaussian graphical models, for brain connectivity modeling. In particular, we apply SICE to learn and analyze functional brain connectivity patterns from different subject groups, based on a key property of SICE, called the "monotone property" we established in this paper. Our experimental results on neuroimaging PET data of 42 AD, 116 MCI, and 67 NC subjects reveal several interesting connectivity patterns consistent with literature findings, and also some new patterns that can help the knowledge discovery of AD.