Zhu Qi, Yang Jing, Xu Bingliang, Hou Zhenghua, Sun Liang, Zhang Daoqiang
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Department of Psychosomatics and Psychiatry, Affiliated Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Front Neurosci. 2021 Sep 29;15:734711. doi: 10.3389/fnins.2021.734711. eCollection 2021.
Brain network analysis has been proved to be one of the most effective methods in brain disease diagnosis. In order to construct discriminative brain networks and improve the performance of disease diagnosis, many machine learning-based methods have been proposed. Recent studies show that combining functional and structural brain networks is more effective than using only single modality data. However, in the most of existing multi-modal brain network analysis methods, it is a common strategy that constructs functional and structural network separately, which is difficult to embed complementary information of different modalities of brain network. To address this issue, we propose a unified brain network construction algorithm, which jointly learns both functional and structural data and effectively face the connectivity and node features for improving classification. First, we conduct space alignment and brain network construction under a unified framework, and then build the correlation model among all brain regions with functional data by low-rank representation so that the global brain region correlation can be captured. Simultaneously, the local manifold with structural data is embedded into this model to preserve the local structural information. Second, the PageRank algorithm is adaptively used to evaluate the significance of different brain regions, in which the interaction of multiple brain regions is considered. Finally, a multi-kernel strategy is utilized to solve the data heterogeneity problem and merge the connectivity as well as node information for classification. We apply the proposed method to the diagnosis of epilepsy, and the experimental results show that our method can achieve a promising performance.
脑网络分析已被证明是脑部疾病诊断中最有效的方法之一。为了构建具有判别力的脑网络并提高疾病诊断性能，人们提出了许多基于机器学习的方法。最近的研究表明，结合功能脑网络和结构脑网络比仅使用单模态数据更有效。然而，在现有的大多数多模态脑网络分析方法中，一个常见的策略是分别构建功能网络和结构网络，这很难嵌入脑网络不同模态的互补信息。为了解决这个问题，我们提出了一种统一的脑网络构建算法，该算法联合学习功能数据和结构数据，并有效地处理连通性和节点特征以提高分类性能。首先，我们在一个统一的框架下进行空间对齐和脑网络构建，然后通过低秩表示用功能数据构建所有脑区之间的相关模型，以便能够捕捉全局脑区相关性。同时，将具有结构数据的局部流形嵌入到这个模型中以保留局部结构信息。其次，自适应地使用PageRank算法来评估不同脑区的重要性，其中考虑了多个脑区的相互作用。最后，利用多核策略来解决数据异质性问题，并合并连通性以及节点信息用于分类。我们将所提出的方法应用于癫痫诊断，实验结果表明我们的方法能够取得良好的性能。
