Mild cognitive impairment (MCI), an early stage of Alzheimer's disease, is marked by subtle cognitive decline. While graph neural networks (GNNs) have shown promise in modeling neuroimaging-based brain networks for MCI detection, most treat the brain as a homogeneous graph, ignoring hemispheric structural and functional heterogeneity. To address this limitation, we propose a Hemispheric Heterogeneity-aware Graph Neural Network (HemiHeter-GNN) to explicitly model hemispheric structural and functional heterogeneity for improved MCI detection. Multimodal brain networks are constructed by integrating diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), and decomposed into left-, right, and inter-hemispheric subnetworks to capture hemispherespecific patterns. Each subnetwork is encoded by a dedicated graph encoder and refined by heat-kernel diffusion for topological regularity, while a cross-subnetwork attention mechanism then fuses them into a unified whole-brain embedding. Experiments on the ADNI dataset show that HemiHeter-GNN consistently outperforms state-of-the-art baselines, demonstrating the effectiveness of modeling hemispheric asymmetry for MCI detection.

Hypergraph-based models have shown great potential in capturing high-order interactions within brain functional networks for disease identification. However, most hypergraph neural networks (HGNNs) depend on static and heuristically constructed hypergraphs, which fail to model the brain's dynamic connectivity patterns. In this paper, we propose a Cluster-Aware Hypergraph Network that jointly learns functional clusters and adaptive high-order connections, enhanced by contrastive learning to preserve topological consistency. Comprehensive experiments conducted on three real-world datasets demonstrate the effectiveness of our proposed methods.

Hypergraphs provide excellent modeling ability for brain disorder identification, especially in capturing high-order interactions among regions of interest (ROIs). Nevertheless, existing methods overlook the impact of directional hyperedges learning on the brain network, leading to wasteful functional connectivity and poor identification performance. To address the above issue, this paper proposes a novel Brain Disorder Identification method via Directed Hypergraph Networks (D-HyperNet). Specifically, our methodology employs an Effective Network Construction module to capture causal dependencies and infer directional functional connectivity among ROIs. Followed by the Flow-aware Feature Aggregation module, which designs a novel directed hypergraph encoder that directionally aggregates node features, effectively improving the accuracy and reliability of brain network representations. Additionally, we are integrating the proposed encoder into a contrastive learning program to obtain a more robust whole-brain representation. Extensive experiments demonstrate the efficacy of our D-HyperNet approach. The code is available at https://github.com/Jia-Weiming/D-HyperNet.

Population graphs are crucial for assessing clinical risk and enhancing the accuracy of Autism Spectrum Disorder (ASD) detection. Nevertheless, the current population graph construction overlooks the balance between biological signals and clinical manifestations, leading to relationship deviation within the population graph and poor detection performance. To address this challenge, we propose a novel approach for ASD Detection in Population Graphs (PopuDet) via Micro-macro Relationship Construction (MmRC) and Multi-feature Fusion (MF). Specifically, our method utilizes the MmRC module to construct a multi-scale population graph balancing the relationships between biological signals synchrony and clinical subtype groups. Subsequently, the MF module learns high-level graph representations at different scales for adaptive fusion to achieve precise ASD detection. Extensive experiments validate the efficiency of PopuDet, highlighting its superior performance over current state-of-the-art methods. Our source code is available at https://github.com/xuting99/PopuDet.

The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we deal with a more arduous challenge, hypergraph modelling with synchronization between brain regions, and design a novel framework, i.e., A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion (MHSA), to tackle this challenge. Specifically, our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest (ROIs) and designs a multi-scale feature fusion mechanism to integrate dynamic connectivity features of functional magnetic resonance imaging (fMRI) from both the temporal and spectrum domains. To evaluate and op-timize the direct contribution of each ROI to phase synchronization in the temporal domain, we structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix. Experiments on the real-world dataset indicate the effectiveness of our strategy. The code is available at https://github.com/Jia-Weiming/MHSA.

Circular RNAs are widely existing in eukaryotes. However, there is as yet no tissue-specific Arabidopsis circular RNA database, which hinders the study of circular RNA in plants. Here, we used 622 Arabidopsis RNA sequencing data sets from 87 independent studies hosted at NCBI SRA and developed AtCircDB to systematically identify, store and retrieve circular RNAs. By analyzing back-splicing sites, we characterized 84 685 circular RNAs, 30 648 tissue-specific circular RNAs and 3486 microRNA-circular RNA interactions. In addition, we used a metric (detection score) to measure the detection ability of the circular RNAs using a big-data approach. By experimental validation, we demonstrate that this metric improves the accuracy of the detection algorithm. We also defined the regions hosting enriched circular RNAs as super circular RNA regions. The results suggest that these regions are highly related to alternative splicing and chloroplast. Finally, we developed a comprehensive tissue-specific database (AtCircDB) to help the community store, retrieve, visualize and download Arabidopsis circular RNAs. This database will greatly expand our understanding of circular RNAs and their related regulatory networks. AtCircDB is freely available at http://genome.sdau.edu.cn/circRNA.

BACKGROUND: One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. RESULTS: We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. CONCLUSION: ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.