Identifying the brain hubs that are crucial for integrating information and distribution is key to understanding how the brain works. In recent years, although various hub identification methods have been proposed in the field of brain imaging, they typically rely on static network representations and analyze using univariate node metrics, thereby neglecting the hub nodes that play a critical role in dynamic global information integration. Additionally, there is an urgent need for an efficient learning method to handle complex brain networks. In this paper, we propose a new reinforcement learning framework, named HubRL, to identify hub nodes that play a central role in coordinating information flow and static topological structures. The agent identifies the most critical brain network nodes by combining simulated information propagation to assess dynamic influence with graph theory metrics to evaluate static topological importance. The experimental results demonstrate that we have successfully identified 37 Task-General hubs in the brain network. Topologically, these hubs exhibit a core advantage over nonhub nodes, with a distribution ratio of approximately$2: 1$in the cerebral cortex gyri and sulci. They also feature significantly longer structural connection fiber bundles and overlap with the regions of the brain with the strongest functional connectivity by up to 80 %. This work frames hub identification as a data-driven sequential decision-making problem without relying on heuristic rules, representing a powerful new paradigm for exploring brain hubs and understanding the working mechanism of the brain.

Exploring the association between high-level semantic brain responses and multimedia features is crucial for understanding the human semantic processing mechanism. However, a significant “semantic gap” persists between abstract brain representations captured by functional Magnetic Resonance Imaging (fMRI) and concrete multimedia features, remaining both unclear and challenging to quantify. To address this, we introduce DyBrainFormer, a novel Transformer-based Brain Dynamics Decoder for Brain-Multimedia Association. Inspired by the topological structure and dynamic properties of the human brain, DyBrainFormer uniquely integrates Graph Convolutional Networks (GCNs) and Hierarchical Temporal Transformer (HTT). It first encodes each sequenced dynamic brain graph using GCNs to capture spatial dependencies and derive brain temporal node attention. Subsequently, these temporal graph representations are fed into the HTT, which excels at modeling complex dynamic changes and long-range temporal dependencies within brain networks. The learned temporal weights from HTT serve as interpretable semantic descriptors, forming a quantifiable bridge that links high-level brain semantics to dynamic multimedia features. Evaluated on the Healthy Brain Network naturalistic fMRI dataset, DyBrainFormer effectively learns distinguishable brain dynamics, achieving$\sim \mathbf{8 3 \%}$classification accuracy in differentiating between children and adolescents. Our analysis further identifies distinct age-related patterns in semantic processing, demonstrating that children emphasize perceptual features while adolescents focus on higher-level conceptual elements. This work provides important references for bridging the semantic gap by establishing a robust and interpretable link between high-level semantic features and multimedia features, offering a novel perspective to uncover the human semantic understanding mechanism.

The Motor Imagery (MI) decoding based on electroencephalogram (EEG), has promising applications. However, most current methods face two main issues: (1) They usually rely on convolutional neural networks to extract temporal features of MI signals without fully considering the brain’s functional connectivity during MI tasks. (2) They lack analysis and recognition of MI features slices and non-tasks slices within EEG signals, leading to poor generalization and robustness. To address these problems, we propose a novel deep learning model based on graph neural network to learn spatial features between multiple electrode channels and integrate the brain’s functional connectivity features. Additionally, it restructures time slices features segmented by the sliding time window algorithm to enhance MI temporal features in EEG signal. Therefor our model achieves the fusion of spatial and temporal features. To enhance the convergence effect of the model, we introduce electrode channel spatial positions as prior knowledge to initialize the parameters of the graph convolutional network parameters. Experimental evaluations on the publicly available EEG MI dataset from BCI Competition IV 2a show that our model achieves a four-class cross-session classification accuracy of 82.38%. Compared with other methods, our model yields the best results, demonstrating its superiority. Furthermore, the results indicate that the spatial feature obtained through our model bears resemblance to the brain functional connectivity patterns identified during MI tasks. To conclude, the fusion of spatial and temporal features with graph model shows the great application potential for EEG MI signals decoding and other EEG analysis.

Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.