Chen Xiaobo, Zhang Han, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:18-25. doi: 10.1007/978-3-319-46723-8_3. Epub 2016 Oct 2.
Conventional functional connectivity (FC) and corresponding networks focus on characterizing the pairwise correlation between two brain regions, while the - 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 HOFC networks corresponding to any in diagnosis of mild cognitive impairment (MCI) subjects.
传统的功能连接性（FC）及其相应网络专注于刻画两个脑区之间的成对相关性，而高阶功能连接性（HOFC）及其网络能够对两个脑区“对”（即四个区域）之间更复杂的关系进行建模。它因其为脑疾病分类提供独特且新颖信息的不可替代功能，在临床应用中引人注目且前景广阔。由于脑区对的数量非常庞大，聚类方法常被用于缩减HOFC网络的规模。然而，由特定聚类参数设置生成的单个HOFC网络可能会丢失其他HOFC网络中包含的多方面、高度互补的信息。为了准确且全面地表征这种复杂的HOFC以更好地辨别脑疾病，在本文中，我们提出了一种基于HOFC的新型疾病诊断框架，该框架能够分层生成多个HOFC网络，并进一步通过选择性特征融合方法将它们集成起来。具体而言，我们创建了一种多层HOFC网络构建策略，其中上层网络是通过对下层网络的节点进行分层聚类形成的。通过这种方式，信息从下层传递到上层，有效去除了最冗余的信息部分，同时保留了最独特的部分。然后，将所有HOFC网络保留的信息/特征输入到一种选择性特征融合方法中，该方法结合了顺序前向选择和稀疏回归，以进一步选择最具判别力的特征子集用于分类。实验结果证实，我们的新方法在诊断轻度认知障碍（MCI）受试者方面优于对应任何[具体内容缺失]的所有HOFC网络。
