Fig. 1

An overview of our proposed TGNet framework. The framework consists of three main components: Cross-Modality Bridging, Multi-GCN Aggregator, and Modality Pooling and Prediction. In the Cross-Modality Bridging section, multimodal brain network data from S subjects and M modalities are stacked into a 4D tensor \(\mathcal {X} \in \mathbb {R}^{N \times N \times M \times S}\). Higher-Order Singular Value Decomposition (HOSVD) is then applied to decompose the tensor and obtain node-level embeddings \(\textbf{U}_1\), which are used to construct a K-Nearest Neighbor (KNN) graph. In the Multi-GCN Aggregator, the KNN graph serves as input to multiple Graph Convolutional Network (GCN) layers, where graph convolution and ReLU activation are applied iteratively to learn multimodal representations of brain networks. In the final component, Modality Pooling and Prediction, the learned feature embeddings are weighted by trainable modality weights \(\alpha\) for quality-aware fusion. The weighted features are then passed through a Fully Connected Network (FCN) for disease prediction