Kong Zhaoming, Zhou Rong, Luo Xinwei, Zhao Songlin, Ragin Ann B, Leow Alex D, He Lifang
School of Software Engineering, South China University of Technology, 382 Waihuan Dong Road, Guangzhou, 510006, China.
Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, 18015, PA, USA.
BioData Min. 2024 Dec 6;17(1):55. doi: 10.1186/s13040-024-00409-6.
Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. We evaluate TGNet on four datasets-HIV, Bipolar Disorder (BP), and Parkinson's Disease (PPMI), Alzheimer's Disease (ADNI)-demonstrating that it significantly outperforms existing methods for disease classification tasks, particularly in scenarios with limited sample sizes. The robustness and effectiveness of TGNet highlight its potential for advancing multimodal brain network analysis. The code is available at https://github.com/rongzhou7/TGNet .
多模态脑网络分析通过整合来自多种神经成像模态的信息，能够全面理解神经系统疾病。然而，现有方法往往难以有效地对多模态脑网络的复杂结构进行建模。在本文中，我们提出了一种新颖的基于张量的图卷积网络（TGNet）框架，该框架将张量分解与多层图卷积网络相结合，以捕捉多模态脑网络的同质性和复杂的图结构。我们在四个数据集——HIV、双相情感障碍（BP）、帕金森病（PPMI）、阿尔茨海默病（ADNI）上对TGNet进行了评估，结果表明它在疾病分类任务中显著优于现有方法，特别是在样本量有限的情况下。TGNet的鲁棒性和有效性凸显了其在推进多模态脑网络分析方面的潜力。代码可在https://github.com/rongzhou7/TGNet获取。
