超复杂图神经网络:迈向多模态脑网络的深度交叉。

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-01 DOI:10.1109/JBHI.2024.3490664
Yanwu Yang, Chenfei Ye, Guoqing Cai, Kunru Song, Jintao Zhang, Yang Xiang, Ting Ma
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引用次数: 0

摘要

多模态神经影像研究为了解大脑网络组织与行为表型之间的异模态关系提供了见解。整合各种模式的数据有助于描述大脑解剖、功能和生理改变或发展之间的相互作用。最近,图形神经网络(GNN)在分析和融合多模态、图形结构的大脑网络方面大受欢迎。然而,由于模态间存在复杂的异质依赖关系,有效学习其他模态的互补表征仍然是一项重大挑战。此外,大多数现有研究往往侧重于特定模态(例如,只有 fMRI 和 DTI),这限制了它们对其他类型大脑网络的扩展性。为了克服这些局限性,我们提出了超复杂图神经网络(HC-GNN),将多模态网络建模为超复杂张量图。在我们的方法中,HC-GNN 被概念化为一个动态空间图,其中用心学习的模式间关联被表示为邻接矩阵。HC-GNN 通过交叉嵌入和交叉聚合,利用超复杂运算来处理模态间的交叉,从而丰富了多模态表征的深度耦合。我们对突出图进行了统计分析,以关联疾病生物标记物。在三个数据集上进行的广泛实验证明了我们的方法具有卓越的分类性能,并可扩展到各种类型的模态。我们的工作为多模态大脑网络研究提供了一个强大的范例。
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Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-modal Brain Networks.

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks. However, effectively learning complementary representations from other modalities remains a significant challenge due to the sophisticated and heterogeneous inter-modal dependencies. Furthermore, most existing studies often focus on specific modalities (e.g., only fMRI and DTI), which limits their scalability to other types of brain networks. To overcome these limitations, we propose a HyperComplex Graph Neural Network (HC-GNN) that models multi-modal networks as hypercomplex tensor graphs. In our approach, HC-GNN is conceptualized as a dynamic spatial graph, where the attentively learned inter-modal associations are represented as the adjacency matrix. HC-GNN leverages hypercomplex operations for inter-modal intersections through cross-embedding and cross-aggregation, enriching the deep coupling of multi-modal representations. We conduct a statistical analysis on the saliency maps to associate disease biomarkers. Extensive experiments on three datasets demonstrate the superior classification performance of our method and its strong scalability to various types of modalities. Our work presents a powerful paradigm for the study of multi-modal brain networks.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
期刊最新文献
Front Cover Table of Contents Estimation and Conformity Evaluation of Multi-Class Counterfactual Explanations for Chronic Disease Prevention. Hierarchical graph representation learning with multi-granularity features for anti-cancer drug response prediction. Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation.
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