Ensemble Hierarchical High-Order Functional Connectivity Networks for MCI Classification.
Ensemble Hierarchical High-Order Functional Connectivity Networks for MCI Classification.
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DOI：
10.1007/978-3-319-46723-8_3
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Chen X;Zhang H;Shen D
传统的功能连接性（FC）和相应的网络专注于表征两个大脑区域之间的成对相关性，而高阶FC（HOFC）和网络可以对两个大脑区域“对”（即，四个地区）。它以其不可替代的功能为脑疾病分类提供独特新颖的信息，具有引人注目的临床应用前景。由于大脑区域对的数量非常大，通常使用聚类来减小HOFC网络的规模。然而，由特定聚类参数设置生成的单个HOFC网络可能丢失包含在其他HOFC网络中的多方面的、高度互补的信息。为了准确和全面地描述这种复杂的HOFC，以更好地区分大脑疾病，本文提出了一种新的基于HOFC的疾病诊断框架，该框架可以分层生成多个HOFC网络，并进一步使用选择性特征融合方法集成它们。具体来说，我们创建了一个多层HOFC网络的构建策略，其中上层的网络是由分层聚类的网络中的节点在较低的层形成。通过这种方式，通过有效地去除信息的最冗余部分并同时保留最独特的部分，信息从较低层传递到较高层。然后，从所有HOFC网络中保留的信息/特征被馈送到选择性特征融合方法中，该方法结合了顺序向前选择和稀疏回归，以进一步选择最具鉴别力的特征子集进行分类。实验结果证实，我们的新方法优于所有的单一HOFC网络对应于任何单一的参数设置在诊断轻度认知障碍（MCI）的主题。
Conventional functional connectivity (FC) and corresponding networks focus on characterizing the pairwise correlation between two brain regions, while the high-order FC (HOFC) and networks can model more complex relationship between two brain region “pairs” (i.e., four regions). It is eye-catching and promising for clinical applications by its irreplaceable function of providing unique and novel information for brain disease classification. Since the number of brain region pairs is very large, clustering is often used to reduce the scale of HOFC network. However, a single HOFC network, generated by a specific clustering parameter setting, may lose multifaceted, highly complementary information contained in other HOFC networks. To accurately and comprehensively characterize such complex HOFC towards better discriminability of brain diseases, in this paper, we propose a novel HOFC based disease diagnosis framework, which can hierarchically generate multiple HOFC networks and further ensemble them with a selective feature fusion method. Specifically, we create a multi-layer HOFC network construction strategy, where the networks in upper layers are formed by hierarchically clustering the nodes of the networks in lower layers. In such a way, information is passed from lower layers to upper layers by effectively removing the most redundant part of information and, at the same time, retaining the most unique part. Then, the retained information/features from all HOFC networks are fed into a selective feature fusion method, which combines sequential forward selection and sparse regression, to further select the most discriminative feature subset for classification. Experimental results confirm that our novel method outperforms all the single HOFC networks corresponding to any single parameter setting in diagnosis of mild cognitive impairment (MCI) subjects.