Dynamic brain networks provide a powerful representation for capturing temporal variations in functional brain connectivity and have gained increasing attention in brain disease diagnosis. However, most existing methods extract features from isolated time windows, making it difficult to capture the high-order dynamic evolution of brain activity. Moreover, these methods often neglect the functional heterogeneity among brain regions, thereby limiting diagnostic performance. To address these limitations, we propose HyperDiag, a novel temporal-regional Hypergraph learning via topology-enhanced state propagation for brain disease Diagnosis. Specifically, we first design a dual-level hypergraph learning strategy: a temporally-evolving hypergraph message passing strategy to capture dynamic high-order dependencies within and across time windows, and meanwhile, a region-wise functional hypergraph learning strategy to capture regional dependencies. Subsequently, we construct a topology-enhanced selective state-space propagation network to integrate complementary information from both the temporally-evolving and region-wise features. Extensive experiments on four brain disorder datasets (ABIDE-I, ADNI, REST-meta-MDD, and Epilepsy) demonstrate that HyperDiag not only outperforms state-of-the-art methods but also identifies biologically meaningful abnormal connections, offering potential biomarkers for clinical interpretation.

With the development of deep brain stimulation technique, single-channel intracranial electroencephalography (iEEG) based seizure prediction is a necessary and urgent needed tool for epilepsy closed-loop neuromodulation. However, previous prediction methods based on multi-channel scalp signals heavily relied on the spatial information, failing to fully exploit the interdependencies between temporal scales and spectral rhythms of single-channel iEEG. Additionally, current contrastive learning strategies can lead to model overfitting by excessively learning the feature distances in small samples, limiting the precision of seizure prediction. To tackle above issues, based on a single-channel iEEG, we propose a novel dual-cross tri-level routing transformer based metric learning network (DC-TRT-MLNet) for epileptic seizure prediction. First, a scale-rhythm dual-cross (DC) graph attention network is introduced to construct the dependent relationships across multi-scale temporal and multi-rhythm spectral features. Second, we design a tri-level routing transformer (TRT) network to comprehensively refine the most seizure-potential routing features while eliminating redundant information. Finally, a hard triplet optimization based metric learning (ML) strategy is developed to iteratively optimize the intra-class and inter-class distances of inter-ictal and pre-ictal routing features. Competitive experimental results on a private Xuanwu Single-Channel iEEG dataset validate the effectiveness of our proposed method, demonstrating the superior prediction performance of our DC-TRT-MLNet compared with the state-of-the-art methods. Our study may offer a new solution for intracranial single-channel seizure prediction.

Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi-channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, their time-centered modeling approach also limits the effective representation of the dynamic spatial topology of brain activity. To address these challenges and fully exploit the potential of large-scale EEG models, we propose a novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations. The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels. This hierarchical structure enables a redefinition of autoregressive learning as a "next-scale-time prediction" problem, effectively capturing both spatial and temporal dynamics. Based on BTH, we design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for prediction. Through extensive large-scale pre-training on 17 datasets, followed by rigorous validation on 10 downstream datasets spanning 5 distinct tasks, THD-BAR consistently outperforms existing methods. These results highlight the superior generalization and modeling capabilities of our proposed approach.

Conventional functional connectivity network (FCN) based on resting-state fMRI (rs-fMRI) can only reflect the relationship between pairwise brain regions. Thus, the hyper-connectivity network (HCN) has been widely used to reveal high-order interactions among multiple brain regions. However, existing HCN models are essentially spatial HCN, which reflect the spatial relevance of multiple brain regions, but ignore the temporal correlation among multiple time points. Furthermore, the majority of HCN construction and learning frameworks are limited to using a single template, while the multi-template carries richer information. To address these issues, we first employ multiple templates to parcellate the rs-fMRI into different brain regions. Then, based on the multi-template data, we propose a spatio-temporal weighted HCN (STW-HCN) to capture more comprehensive high-order temporal and spatial properties of brain activity. Next, a novel deep fusion model of multi-template called spatio-temporal weighted multi-hypergraph convolutional network (STW-MHGCN) is proposed to fuse the STW-HCN of multiple templates, which extracts the deep interrelation information between different templates. Finally, we evaluate our method on the ADNI-2 and ABIDE-I datasets for mild cognitive impairment (MCI) and autism spectrum disorder (ASD) analysis. Experimental results demonstrate that the proposed method is superior to the state-of-the-art approaches in MCI and ASD classification, and the abnormal spatio-temporal hyper-edges discovered by our method have significant significance for the brain abnormalities analysis of MCI and ASD.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

Abstract Safety is an important issue in the mining industry and the Internet of Things (IoT) plays an important role to enhance the safety of the underground working environment. The IoT is used to transfer data generated by underground sensors to cloud storage for further processing. However, third‐party platforms are often a target for cyber attacks. Serious mining accidents might occur if the data were tampered with. In the overground scenario, the security of trading data is also an important issue. The Mine Consortium Blockchain (MCB) is proposed to solve the above problems. The MCB avoids the risk of centralized storage and enables data security, provenance and transparency by taking advantage of blockchain technology. The MCB platform ensures that only designated participants can process mineral data. Any violation is immutably recorded in the MCB and is easily traced back by other participants. Classical consensus mechanisms as the core technology of the blockchain cannot be directly and appropriately applied to the mining industry. A Block Alliance Consensus (BAC) mechanism, which is suitable for all consortium blockchain scenarios, is proposed to improve the performance of the MCB. In addition, the block structure of the underground sensor data is optimized: the blocks only contain a hash of the sensor data and the data being stored in the cloud. The efficiency of the BAC is demonstrated by simulation experiments where the performance of the BAC consensus mechanism is compared with with the performance of classical consensus mechanisms. The MCB and the BAC consensus mechanism were also implemented on Hyperledger Fabric. Finally the Hyperledger Caliper evaluation tool was used to evaluate the performance of the system.