Major Depressive Disorder (MDD) is a common mental disorder, and making an early and accurate diagnosis is crucial for effective treatment. Functional Connectivity Network (FCN) constructed based on functional Magnetic Resonance Imaging (fMRI) have demonstrated the potential to reveal the mechanisms underlying brain abnormalities. Deep learning has been widely employed to extract features from FCN, but existing methods typically operate directly on the network, failing to fully exploit their deep information. Although graph coarsening techniques offer certain advantages in extracting the brain's complex structure, they may also result in the loss of critical information. To address this issue, we propose the Multi-Granularity Brain Networks Fusion (MGBNF) framework. MGBNF models brain networks through multi-granularity analysis and constructs combinatorial modules to enhance feature extraction. Finally, the Constrained Attention Pooling (CAP) mechanism is employed to achieve the effective integration of multi-channel features. In the feature extraction stage, the parameter sharing mechanism is introduced and applied to multiple channels to capture similar connectivity patterns between different channels while reducing the number of parameters. We validate the effectiveness of the MGBNF model on multiple classification tasks and various brain atlases. The results demonstrate that MGBNF outperforms baseline models in terms of classification performance. Ablation experiments further validate its effectiveness. In addition, we conducted a thorough analysis of the variability of different subtypes of MDD by multiple classification tasks, and the results support further clinical applications.

Magnetic resonance imaging (MRI) is of great value in clinical diagnosis due to its ability to present tissue structure and functional information of the brain. However, the acquisition of high-resolution MRI images remains challenging due to constraints in scanning time and hardware limitations. In response, multi-contrast super-resolution (SR) reconstruction has emerged as a promising technique for enhancing image quality. The effectiveness of this approach largely depends on the ability to fully leverage the complementary information across different modalities and to achieve accurate structure matching. To address this challenge, we propose a Mamba-enhanced large-window Transformer network (MC-MambaTrans), which effectively improves the reconstruction accuracy through multi-modal deep feature extraction and structure-guided matching. Specifically, MC-MambaTrans employs the large-window Transformer to model cross-modal multiscale global contextual information, and at the same time introduces the Mamba mechanism-driven coarse-to-fine matching strategy to enhance the guidance of structural information from the reference image slice-by-slice. Ultimately, high-quality SR images are recovered by the multi-scale feature fusion and up-sampling module. Experiments on several publicly available multi-contrast brain MRI datasets show that the method in this paper significantly outperforms the existing state-of-theart methods in terms of reconstruction quality, demonstrating its broad application prospects in medical image reconstruction tasks.

Transcranial direct current stimulation (tDCS) has shown promise in treating neurological disorders, particularly through dorsolateral prefrontal cortex (DLPFC) targeting. However, the effects of DLPFC-tDCS on brain functional networks and the underlying propagation mechanisms remain poorly understood. We present a novel tDCS hybrid brain model (tDCS-HBM) that incorporates tDCS-induced gray matter electric fields into a large-scale brain network model, considering their relationship with membrane potential to effectively predict spatiotemporal dynamics. Using this model, we simulated brain activity in response to tDCS over the left (F3-Fp2) and right DLPFC (F4-Fp1). Our results demonstrate that tDCS enhances brain complexity and flexibility, leading to increased functional connectivity (FC) across the whole brain and an improvement in global network efficiency. Dynamic analysis reveals an initial FC decline, followed by widespread enhancement originating from inferior and orbital frontal regions. Importantly, right DLPFC-tDCS induces strong FC associated with the ventral attention network. These changes in topological metrics and spatiotemporal patterns are consistent with prior modeling and empirical findings, validating the utility of our tDCS-HBM in understanding propagation mechanisms. Our hybrid model holds the potential to predict the stimulation effects of modulation protocols, providing precise guidance for clinical neuromodulation interventions.

The satellite TIR remote sensing has become a promising technique for monitoring tectonic activities and detecting earthquake precursors due to the advantages of large observation area and short observation period. In order to identify and to extract the TIR anomaly, several presented methods are tested and compared in this paper. The comparison results indicate that Robust AVHRR Technology (RAT) is a better method for detecting pre-earthquake thermal anomaly. Anyway RAT is not able to quantitatively analyze the TIR anomaly before shock. RAT method is hence improved for obtaining the value of area and average temperature of TIR anomaly. Based on the analysis of some tectonic earthquakes, an empirical quantitative model for tectonic activity analysis and earthquake magnitude predication is established.

The phenomenon of a satellite thermal infrared (TIR) anomaly before earthquakes has been reported since the late 1980s. The reported increase of surface temperatures reaches 2-4 degC, occasionally higher. Usually, the anomaly appears one month to several days before the earthquake. Several mechanisms of hypothesis have been put forward to interpret the reported temperature increase. In this paper, the satellite TIR anomaly features of four earthquakes in the Taiwan region are first analyzed. To study the mechanisms of infrared anomaly a group of physical simulation experiments are carried out. The mechanism of the satellite Infrared anomaly before an earthquake in the Taiwan region is discussed based on the experimental results. Furthermore, a preliminary model for tectonic activity analysis and for short-term earthquake prediction based on the analysis of the satellite infrared anomaly before an earthquake in the Taiwan region is presented.

This paper briefly introduces the general scientific facts from the reports on satellite thermal infrared (TIR) anomaly before earthquake and the presented mechanism and hypothesis for interpretation the TIR anomaly. The spatio-temporal features of infrared (IR) radiation from loaded rock, stick-sliding rock and simulated active fault motion based on experimental IR detection are also introduced. Especially, the experiments on simulated fault activity shows that the TIR anomaly is likely to develop along the primary fault and the both sides of wedge-shaped acute geo-block in condition of intersected fault system, and that the intersection location is usually the coming epicenter. The theoretical mechanism of TIR anomaly before tectonic earthquake is hence suggested to be stress-thermal effect accompanied and strengthened by hydro-geological effect, greenhouse effect, P-hole effect and so on. As a case, the spatio-temporal features of TIR before Zhangbei Ms6.2 1998 earthquake is theoretically analyzed based on the overlay of active fault system on NOAA-AVHHR satellite TIR images. It was discovered that TIR anomaly is controlled by a potential great deep active fault and that the epicenter is exactly the intersection point of the primary great fault and two secondary faults being tow sides of an acute wedge-shaped active geo-block. It is concluded that although the earthquake is a complex and uncertain process of crust motion, the predication of earthquake is not impossible. Referring to the developing GEOSS and generalized remote sensing (GRS), a preliminary procedure for comprehensive multiple parameters analysis on fault activities and earthquake early warning is presented, which is based on massive data fusion.