Huang Yin, Li Ying, Yuan Yuting, Zhang Xingyu, Yan Wenjie, Li Ting, Niu Yan, Xu Mengzhou, Yan Ting, Li Xiaowen, Li Dandan, Xiang Jie, Wang Bin, Yan Tianyi
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.
School of Life Science, Beijing Institute of Technology, Beijing, China.
Front Neurosci. 2024 Mar 8;18:1303741. doi: 10.3389/fnins.2024.1303741. eCollection 2024.
Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.
脑网络分析为脑部疾病的诊断提供了重要见解。已证明，整合多种神经影像学模态比使用单一模态进行脑网络分析更有效。然而，大多数现有的基于多种模态的脑网络分析方法往往忽略了来自各种模态的互补信息和独特特征。为了解决这个问题，我们提出了贝塔信息扩散多层图嵌入（BID-MGE）方法。所提出的方法无缝集成了结构连接性（SC）和功能连接性（FC），以学习更全面的信息用于诊断神经精神疾病。具体而言，利用一种新颖的贝塔分布映射函数（贝塔映射）来增加重要信息并弱化无关连接。经过优化的信息有助于扩散过程专注于关键脑区，以捕获更具区分性的特征。为了最大程度地保留每种模态的独特特征，我们设计了一个最优尺度的多层脑网络，其层间连接取决于节点信息性。然后，提出了一种多层信息扩散方法，以从各种模态中捕获互补信息和独特特征，并通过将每个节点的特征与其连接节点的特征相结合来生成节点表示。最后，使用主成分分析（PCA）对节点表示进行重新配置，并参照多个模板计算余弦距离以进行统计分析和分类。我们将所提出的方法应用于神经精神疾病的脑网络分析。结果表明，我们的方法有效地识别了与疾病相关的关键脑区，为疾病病理学提供了有价值的见解，并且在分类性能上超过了其他先进方法。
