Kang Yanqing, Zhu Di, Zhang Haiyang, Shi Enze, Yu Sigang, Wu Jinru, Wang Ruoyang, Chen Geng, Jiang Xi, Zhang Tuo, Zhang Shu
Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Comput Biol Med. 2025 Mar;186:109629. doi: 10.1016/j.compbiomed.2024.109629. Epub 2024 Dec 27.
Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood. In contrast, self-supervised deep learning dispenses with manual features, allowing it to learn meaningful representations directly from the data. This approach enables the exploration of I-nodes for brain networks, which is also lacking in current studies.
This paper proposes a Self-Supervised Graph Reconstruction framework based on Graph-Transformer (SSGR-GT) to identify I-nodes, which has three main characteristics. First, as a self-supervised model, SSGR-GT extracts the importance of brain nodes to the reconstruction. Second, SSGR-GT uses Graph-Transformer, which is well-suited for extracting features from brain graphs, combining both local and global characteristics. Third, multimodal analysis of I-nodes uses graph-based fusion technology, combining functional and structural brain information.
The I-nodes we obtained are distributed in critical areas such as the superior frontal lobe, lateral parietal lobe, and lateral occipital lobe, with a total of 56 identified across different experiments. These I-nodes are involved in more brain networks than other regions, have longer fiber connections, and occupy more central positions in structural connectivity. They also exhibit strong connectivity and high node efficiency in both functional and structural networks. Furthermore, there is a significant overlap between the I-nodes and both the structural and functional rich-club.
Experimental results verify the effectiveness of the proposed method, and I-nodes are obtained and discussed. These findings enhance our understanding of the I-nodes within the brain network, and provide new insights for future research in further understanding the brain working mechanisms.
研究脑网络中的影响节点（I节点）在脑成像领域具有重要意义。大多数现有研究将脑连接枢纽视为I节点，例如具有高中心性或富俱乐部组织的区域。然而，这种方法严重依赖于图论的先验知识，这可能会忽略脑网络的内在特征，尤其是当其架构尚未完全理解时。相比之下，自监督深度学习无需人工特征，能够直接从数据中学习有意义的表示。这种方法能够探索脑网络的I节点，而这也是当前研究中所缺乏的。
本文提出了一种基于图变换器（Graph-Transformer）的自监督图重构框架（SSGR-GT）来识别I节点，该框架具有三个主要特征。首先，作为一个自监督模型，SSGR-GT提取脑节点对重构的重要性。其次，SSGR-GT使用图变换器，其非常适合从脑图中提取特征，结合了局部和全局特征。第三，对I节点的多模态分析使用基于图的融合技术，结合了脑功能和结构信息。
我们获得的I节点分布在诸如额上叶、顶叶外侧和枕叶外侧等关键区域，在不同实验中总共识别出56个。这些I节点比其他区域参与更多的脑网络，具有更长的纤维连接，并且在结构连接中占据更中心的位置。它们在功能和结构网络中也表现出很强的连接性和高节点效率。此外，I节点与结构和功能富俱乐部之间存在显著重叠。
实验结果验证了所提方法的有效性，并获得并讨论了I节点。这些发现增强了我们对脑网络中I节点的理解，并为未来进一步理解脑工作机制的研究提供了新的见解。
