Table_4_Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data.docx

Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting state high-order functional hyper-networks (rs-HOFHNs) for patients with depression and normal subjects. Meanwhile, we also introduce a novel property (the shortest path) to extract local features with traditional local properties (cluster coefficients). A subgraph feature-based method was introduced to characterize information relating to global topology. Two features, local features and subgraph features that showed significant differences after feature selection were subjected to multi-kernel learning for feature fusion and classification. Compared with conventional hyper network models, the high-order hyper network obtained the best classification performance, 92.18%, which indicated that better classification performance can be achieved if we needed to consider multivariate interactions and the time-varying characteristics of neural interaction simultaneously when constructing a network.

静息态功能连接超网络（resting-state functional connectivity hypernetworks）是一类允许多个节点间建立连接的有效技术，可用于脑部疾病诊断与分类研究。传统功能超网络能够以静态形式刻画人脑内部的复杂交互作用。然而，越来越多的研究证据表明，即便处于静息状态，大脑的神经活动仍会呈现出瞬时且细微的动态变化。此类动态变化对于解析大脑组织的基本特性至关重要，且可能与脑部疾病的病理机制存在显著关联。因此，考虑到静息态下功能连接的动态变化特性，本研究提出了一种针对抑郁症患者与正常对照人群构建静息态高阶功能超网络（resting state high-order functional hyper-networks，rs-HOFHNs）的方法。同时，本研究引入了一种全新的属性——最短路径（the shortest path），结合传统局部属性聚类系数（cluster coefficients）来提取局部特征；此外还提出了一种基于子图特征的方法，用于刻画全局拓扑结构相关信息。经特征筛选后获得的具有显著组间差异的局部特征与子图特征，通过多核学习方法实现特征融合与分类任务。与传统超网络模型相比，本研究提出的高阶超网络取得了92.18%的最优分类性能，这表明在构建网络时若同时兼顾多元交互作用与神经交互的时变特性，可获得更优异的分类效果。