Conventional Functional connectivity (FC) analysis focuses on characterizing the correlation between two brain regions, whereas the high-order FC can model the correlation between two brain region pairs. To reduce the number of brain region pairs, clustering is applied to group all the brain region pairs into a small number of clusters. Then, a high-order FC network can be constructed based on the clustering result. By varying the number of clusters, multiple high-order FC networks can be generated and the one with the best overall performance can be finally selected. However, the important information contained in other networks may be simply discarded. To address this issue, in this paper, we propose to make full use of the information contained in all high-order FC networks. First, an agglomerative hierarchical clustering technique is applied such that the clustering result in one layer always depends on the previous layer, thus making the high-order FC networks in the two consecutive layers highly correlated. As a result, the features extracted from high-order FC network in each layer can be decomposed into two parts (blocks), i.e., one is redundant while the other might be informative or complementary, with respect to its previous layer. Then, a selective feature fusion method, which combines sequential forward selection and sparse regression, is developed to select a feature set from those informative feature blocks for classification. Experimental results confirm that our novel method outperforms the best single high-order FC network in diagnosis of mild cognitive impairment (MCI) subjects.

译文

传统的功能连通性 (FC) 分析侧重于表征两个大脑区域之间的相关性,而高阶FC可以对两个大脑区域对之间的相关性进行建模。为了减少大脑区域对的数量,应用聚类将所有大脑区域对分组为少量聚类。然后,可以根据聚类结果构造高阶FC网络。通过改变群集的数量,可以生成多个高阶FC网络,并最终选择具有最佳整体性能的网络。但是,其他网络中包含的重要信息可能会被简单地丢弃。为了解决这个问题,在本文中,我们建议充分利用所有高阶FC网络中包含的信息。首先,应用了一种聚集的层次聚类技术,使得一层中的聚类结果始终依赖于前一层,从而使两个连续层中的高阶FC网络高度相关。结果,从每一层中的高阶FC网络中提取的特征可以分解为两个部分 (块),即,相对于其先前的层,一个是冗余的,而另一个可能是信息性的或互补的。然后,开发了一种选择性特征融合方法,该方法结合了顺序前向选择和稀疏回归,以从那些信息性特征块中选择特征集进行分类。实验结果证实,我们的新方法在诊断轻度认知障碍 (MCI) 受试者方面优于最佳的单个高阶FC网络。

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