Investigating abnormal brain network characteristics in schizophrenia can improve our understanding of disease mechanisms and help identify potential intervention targets. Graph learning techniques can capture high-dimensional features of large-scale brain networks and offer an inherent advantage for integrating multimodal data. To better integrate multimodal data and accurately localize network abnormalities associated with the disorder, this study proposes a channel-based graph neural network (C-GNN) model. First, node embedding of brain regions was constructed to capture structural connectivity patterns. Second, a branched attention module was introduced to adaptively identify important brain regions through channel attention. Finally, a graph feature-constraint module was developed to extract salient features by computing difference scores across feature channels. The C-GNN model achieved an accuracy of 84.37% in classifying individuals with schizophrenia. Interpretability analysis revealed key abnormal brain regions (e.g. orbital cortex, temporal fusiform cortex, lingual gyrus) and multimodal metrics (such as cortical thickness and ReHo) that contributed substantially to the classification. These findings offer insights into the underlying neural alterations in schizophrenia and may inform the development of targeted intervention strategies.

Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node-node and edge-edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.

In the field of remote sensing, detection in dimly lit or shadowed areas has traditionally been difficult because of detector noise. Given that noise in real-world images of remote sensing exhibits spatial correlation, existing self-supervised methods encounter difficulties in reconciling the suppression of spatially correlated noise with the preservation of local texture details. To address this challenge, we propose a self-supervised model that combines blind-spot feature extraction with diffusion-based texture generation to fine denoising of real-world images under adverse conditions. We first introduce a blind-spot feature extraction structure based on the fusion of U-Net with blind-spot net (UBSN) and blind transformer (BTF). In UBSN, we integrate multistride blind-spot convolution (BSC) + dilated convolution (DC) feature extraction nodes and employ a Reshuffle strategy in skip-layer connections to maintain large-scale blind-spot characteristics. Additionally, we design a transformer structure for blind spot between patches to remove the noise with spatial correlations while ensuring global feature acquisition. Subsequently, to restore texture details blurred by the blind-spot structures, we introduce a texture generation diffusion structure during model training, achieving a balance between large-scale blind-spot characteristics and local rich texture details. Experimental results demonstrate that our approach outperforms other self-supervised denoising methods, even some methods leveraging unpaired images, without the need for parameters related to on-orbit satellite detectors.

Infrared small target detection is widely used in remote sensing fields. However, the application scenes of space-based remote sensing imaging often lead to problems such as small target scale, weak energy, and serious influence by strong clutters. At present, traditional methods are often difficult to adapt to the change of target scale. And deep learning methods are often difficult to extract small target features, and the change in imaging characteristics also brings challenges to the generalization ability. To complement each other’s advantages, we propose an infrared small target detection method that combines the traditional methods with the deep learning methods. First, we construct a multi-stage feature extraction network for guiding the typical multi-scale traditional filtering results to progressively fuse. Secondly, we propose a multi-scale attention supervision module to adjust the semantic consistency of different stages, improving the network generalization ability. Next, a dynamic weight convolution module is utilized to obtain the optimal distribution of grayscale in the neighborhood. Finally, we use the background modelling results to suppress the background, effectively weakening the influence of background clutters and enhancing the target contrast. Experimental results show that the proposed method has good detection results for targets with different scales and signal-to-clutter ratios in a variety of complex scenes. Compared with the typical methods, our method has better detection performance and generalization ability.

Small target detection in remote sensing images has considerable significance in practical applications such as military dynamic discrimination and traffic monitoring. However, the limited appearance features of small-scale targets and the widespread false alarm sources make small target detection in remote sensing images a tough challenge. To address these problems, we propose a novel small detection method by employing an adaptive multi-level feature fusion module (AMFFM) and an attention-augmented high-resolution head (AAHRH). Specifically, AMFFM is designed to suppress the interference of false alarm sources in complicated scenes. We upsample the high-level features by the context modeling of semantic information and refine the low-level features for noise removal. Then the enhanced multi-level features are fused based on the spatial and channel significance. After that, AAHRH is put forward to enhance the perception of small targets by embedding cross-dimension interaction with the attention mechanism. The prediction heads are reconstructed with high-resolution layers to improve the detection performance in densely distributed scenes. We conduct dilated and comparison experiments on a constructed small car dataset, a public small ship dataset, and the VEDAI dataset. The experimental results on two datasets verify the effectiveness and robustness of the proposed method with the state-of-the-art performance.

Aircraft detection in remote sensing images is significant in both military and civilian fields, such as air traffic control and battlefield dynamic monitoring. Deep learning methods can achieve promising detection performance with sufficient and labeled samples. However, current aircraft datasets are mainly from a single data source and lack diverse scenes and targets, making it difficult to train a robust and generalized detector. Therefore, we manually label and construct a complex optical remote sensing aircraft target detection dataset (CORS-ADD) from Google Earth and multiple satellites such as WorldView-2, WorldView-3, Pleiades, Jilin-1, and IKONOS. It contains 7,337 images covering typical airports and various rare scenes, including the aircraft carrier, ocean and land with flying aircraft. The dataset consists of 32,285 civil and military aircraft instances, including bombers, fighters, and early warning aircraft. These targets range from 4×4 pixels to 240×240 pixels and are all labeled with both horizontal bounding box (HBB) and oriented bounding box (OBB) annotations. The various scenes and sufficient instances can fully support the training and evaluation of data-driven algorithms. Meanwhile, based on the constructed dataset, we train and evaluate several detectors to provide a benchmark and help promote the development of aircraft detection techniques.

With the continuous increase in the resolution of optical remote sensing satellites, the influence of space variations on the image quality cannot be ignored, especially in new imaging systems such as thin-film diffraction and rectangular rotating pupils. This paper was conducted to analyze the influence of space variability on restoration performances of different methods, then a new processing strategy of space-variant images is proposed. According to the analytical experiment results, we suggest using the block method when the PSV < 0.20% and otherwise selecting the global method. In order to ensure the final image quality, we also suggest controlling the PSV within 0.28% when designing optical systems. This study can provide a foundation for optimizing the design of front-end optical systems and selecting back-end processing methods in engineering applications.

Neuroimaging studies have suggested the presence of abnormalities in the prefrontal-thalamic-cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV). Granger causality analysis, seeded at regions with altered GMVs, was subsequently conducted. To discover the differences between the groups, ANCOVA and post hoc tests were performed. Then, the relationships between the structural changes, causal connectivity and clinical variables were investigated. Finally, a leave-one-out resampling method was implemented to test the consistency. Statistical analyses showed the GMV and causal connectivity changes in the prefrontal-thalamic-cerebellar circuit. Compared with HC, both SCH and DEP exhibited decreased GMV in middle frontal gyrus (MFG), and a lower GMV in MFG and medial prefrontal cortex (MPFC) in SCH than DEP. Compared with HC, both patient groups showed increased causal flow from the right cerebellum to the MPFC (common causal connectivity abnormalities). And distinct causal connectivity abnormalities (increased causal connectivity from the left thalamus to the MPFC in SCH than HC and DEP, and increased causal connectivity from the right cerebellum to the left thalamus in DEP than HC and SCH). In addition, the structural deficits in the MPFC and its causal connectivity from the cerebellum were associated with the negative symptom severity in SCH. This study found common/distinct structural deficits and aberrant causal connectivity patterns in the prefrontal-thalamic-cerebellar circuit in SCH and DEP, which may provide a potential direction for understanding the convergent and divergent psychiatric pathological mechanisms between SCH and DEP. Furthermore, concomitant structural and causal connectivity deficits in the MPFC may jointly contribute to the negative symptoms of SCH.

The purpose of this study was to investigate the functional connectivity (FC) of thalamic subdivisions in patients with juvenile myoclonic epilepsy (JME). Resting state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data were acquired from 22 JME and 25 healthy controls. We first divided the thalamus into eight subdivisions by performing independent component analysis on tracking fibers and clustering thalamus-related FC maps. We then analyzed abnormal FC in each subdivision in JME compared with healthy controls, and we investigated their associations with clinical features. Eight thalamic sub-regions identified in the current study showed unbalanced thalamic FC in JME: decreased FC with the superior frontal gyrus and enhanced FC with the supplementary motor area in the posterior thalamus increased thalamic FC with the salience network (SN) and reduced FC with the default mode network (DMN). Abnormalities in thalamo-prefrontocortical networks might be related to the propagation of generalized spikes with frontocentral predominance in JME, and the network connectivity differences with the SN and DMN might be implicated in emotional and cognitive defects in JME. JME was also associated with enhanced FC among thalamic sub-regions and with the basal ganglia and cerebellum, suggesting the regulatory role of subcortical nuclei and the cerebellum on the thalamo-cortical circuit. Additionally, increased FC with the pallidum was positive related with the duration of disease. The present study provides emerging evidence of FC to understand that specific thalamic subdivisions contribute to the abnormalities of thalamic-cortical networks in JME. Moreover, the posterior thalamus could play a crucial role in generalized epileptic activity in JME.