Background: Schizophrenia is a complex disorder characterized by disruptions in cognition, behavior, and emotions. Extensive research has uncovered alterations in a single modality (either the brain structure or function) in schizophrenia. However, the limitation is that a single modality could not offer a synchronous result between the brain structure and function because of different samples. Here, a multiparametric approach is essential to understand the common and distinct alterations between the brain structure and function in schizophrenia. Methods: We analyzed structural and functional magnetic resonance imaging data from 146 participants (72 individuals with schizophrenia and 74 healthy controls). Individual morphological similarity and functional connectivity gradients were computed using a nonlinear dimensionality reduction technique with diffusion map embedding. Furthermore, to understand how the alterations may be related to genetic underpinnings, gene expression enrichment analyses were conducted using Allen Brain Human Atlas and GOrilla. Results: Compared with controls, patients with schizophrenia had reduced scores on the principal functional gradient of the visual network and elevated scores on the principal functional gradient of the limbic network, the frontoparietal control network, and the default mode network. Additionally, the main functional gradient in individuals with schizophrenia showed compression along the primary axis compared to the healthy control group. These changes were linked to genes involved in synaptic signaling and neuronal development. Conclusions: These results indicate connectome gradient dysfunction in schizophrenia and its linkage with gene expression profiles, supporting widespread network-level abnormalities. The integration of neuroimaging provides insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

Brain functional hypernetworks that can characterize the complex and multivariate interactions among multiple brain regions have been widely used in the diagnosis and prediction of brain diseases. However, there are few studies on the structure and dynamics of brain functional hypernetworks. Such studies can help to explore how the important functional features of brain functional hypernetworks characterize the working and pathological mechanisms of the human brain. Therefore, this article introduces the hypernetwork null model to analyze the dependencies between the features of interest. Specifically, first, based on the original brain functional hypernetwork, this article proposed the optimized hyper dK-series algorithm with hyperedges to construct null models that preserved the different node attributes and hyperedge attributes of the original brain functional hypernetwork, respectively. Next, based on the original hypernetwork model and the null model, multiple node attributes and hyperedge attributes were respectively introduced. Then, the level of similarity and correlation between the topological attributes of the original brain functional hypernetwork and the topological attributes of the brain functional hypernetwork null model were calculated to analyze the dependencies between the features of interest. The results showed that there were differences in the level of dependence between the features of interest. Node degree is the main dependency attribute for multiple metrics. Hyperedge degree, node degree-dependent redundancy coefficient, and hyperedge degree-dependent redundancy coefficient are partial dependency attributes for some metrics. The dependency attributes and level of dependency are the same for the hypernetwork clustering coefficients-HCC² and HCC³. This indicates that the node degree is redundant with respect to other attributes, while the hyperedge degree, node degree-dependent redundancy coefficient, and hyperedge degree-dependent redundancy coefficient perhaps contain other topology information. In addition, there is redundancy between HCC² and HCC³. Therefore, the effects of these redundant attributes need to be considered when performing network analysis.

Object detection in frame-based vision systems often faces the problem of significant performance degradation under challenging conditions due to the inherent sensing limitations of conventional cameras. Event cameras, with their advantages of high temporal resolution and sparse data output, offer a promising solution for addressing the performance degradation problem. However, due to the asynchronous and sparse nature of event data, event camera–based detection methods generally exhibit lower accuracy than their image-based counterparts. Therefore, effectively fusing data from both event and frame-based cameras represents the main challenge. To this end, this study introduces a hierarchical fusion enhancement network (FENET) designed to achieve more robust object detection through complementary multimodal fusion. First, to address the problem of underutilization of event timescales in conventional dense representations, this study designs an innovative, learnable multi-timescale event preprocessing module. This module generates temporal aggregation frames (TAFs) by leveraging event data from a spectrum of intervals, ranging from standard RGB exposure times down to finer timescales. This approach ensures the comprehensive capture and clear delineation of the target's structure, thereby enhancing the integrity of the visual information. Furthermore, a two-branch adaptive enhanced cross-modal aggregation module (ECAM), which guides the adaptive fusion of features from both modalities by first self-enhancing the features of each, is employed. The main goal of the ECAM is to achieve efficient feature aggregation by combining global context with local spatial information, subsequently using the aggregated features to steer the adaptive fusion process. Finally, by recognizing the scarcity of large-scale, high-quality annotated datasets for event-based object detection, the DSEC-D dataset comprising 60 sequences, with a total of 70,739 images and over 250,000 labels, is manually constructed. Extensive experimental results demonstrate that the proposed FENET can successfully overcome the perceptual limitations of conventional cameras. Notably, the proposed FENET surpasses current RGB-event fusion schemes, achieving an 8.6% improvement in the mAP50 metric and a 3.7% improvement in the mAP50:95 metric over the FRN baseline. We have released the code and the dataset at https://github.com/wkydawang/FENET.