Data_Sheet_3_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）支持多节点间的连接，是用于脑部疾病诊断与分类研究的有效技术。常规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，即便处于静息状态下，大脑的神经活动仍表现出瞬时且细微的动态变化。这类动态变化对于解析大脑组织的基本特征至关重要，且可能与脑部疾病的病理机制存在显著关联。因此，考虑到静息态功能连接的动态变化特性，本研究提出了针对抑郁症患者与正常对照人群的静息态高阶功能超网络（resting state high-order functional hyper-networks，rs-HOFHNs）构建方法。与此同时，本研究引入了一种全新的属性——最短路径（shortest path），结合传统局部属性聚类系数（cluster coefficients）提取局部特征；此外还提出了基于子图特征的方法，用于刻画全局拓扑结构相关信息。经过特征筛选后，表现出显著差异的局部特征与子图特征被应用于多内核学习以实现特征融合与分类任务。与常规超网络模型相比，本研究所提出的高阶超网络取得了92.18%的最优分类性能，这表明在构建脑网络时，若同时兼顾多变量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。