Ting Wu

Objective: To address the limitations of existing deep learning methods for brain networks in jointly modeling directional interregional connectivity, multiband information, and short-term dynamic features, this study proposes a multiscale directed temporal graph convolutional network (MSD-STGNN) for the three-class classification of healthy controls (HCs), patients with left temporal lobe epilepsy (lTLE), and patients with right temporal lobe epilepsy (rTLE). Methods: Resting-state magnetoencephalography (MEG) data were obtained from 43 subjects, including 14 HCs, 13 patients with lTLE, and 16 patients with rTLE. Based on 26 predefined default mode network (DMN)-related brain regions, directed effective connectivity networks were constructed in six frequency bands using the directed transfer function (DTF), and indices including information-flow strength, directional preference, and hemispheric asymmetry were used as node features. MSD-STGNN separately modeled incoming and outgoing connectivity information through directed graph convolution, fused frequency-band information using a hierarchical multiband attention mechanism with gated residual correction, and employed gated recurrent units (GRUs) to extract short-term dynamic features from consecutive brain-network slices. Model performance was evaluated using subject-level stratified fivefold cross-validation, with predictions from multiple temporal groups of each subject aggregated to obtain the final subject-level prediction. Frequency-band masking and node-level fusion-weight analyses were further performed to evaluate the model’s dependence on different frequency bands and information from the predefined brain regions. Results: In the subject-level fivefold cross-validation, MSD-STGNN achieved an accuracy of 0.836 ± 0.067, a macro-F1 of 0.830 ± 0.065, and a macro-AUC of 0.900 ± 0.055 using the one-vs-rest strategy, with the highest fivefold mean values across all evaluation metrics among the baseline models and ablation configurations investigated in this study. Post-training frequency-band masking showed that the model exhibited relatively high dependence on the low-gamma (30–80 Hz) and beta (13–30 Hz) bands. Node-level fusion-weight analysis showed that, within the 26 predefined DMN-related brain regions, orbitofrontal, cingulate, medial temporal, and parietal regions exhibited relatively high overall fusion weights across different frequency bands, although the exact top 5 regional rankings varied across folds. Significance: Within a unified framework, MSD-STGNN integrates directional connectivity, multiband information, and short-term dynamic features derived from MEG-based directed brain networks, providing a modeling approach with a certain degree of interpretability for the three-class classification of HCs, patients with lTLE, and patients with rTLE. The current findings are based on internal cross-validation of a small, single-center cohort; therefore, the classification performance and the observed frequency-band and brain-region attention patterns require further validation in larger, independent multicenter datasets.

Osteonecrosis of the femoral head (ONFH) may progress to collapse and disability. While total hip arthroplasty (THA) is standard in older patients, surgica

Background: Fibromyalgia syndrome (FMS) is a prevalent central chronic pain condition of unknown pathophysiology. White matter (WM) plays a crucial role in brain signal transmission, and WM structural abnormalities have been reported in FMS. However, functional WM changes in FMS patients have not been investigated. This study aimed to investigate whether functional neural changes in WM accompany structural changes observed in FMS. Methods: The study included 43 FMS patients and 43 healthy controls. Structural and functional analyses of WM were assessed using fractional anisotropy (FA) and amplitude of low-frequency fluctuation (ALFF), respectively, to explore WM alterations in FMS patients. Results: Structural analysis revealed altered FA values in the left corticospinal tract (CST), right cingulate/hippocampus, right fornix/stria terminalis (FX/ST), superior occipitofrontal fasciculus (SOFF), right superior corona radiata (SCR), right posterior corona radiata (PCR), sagittal stratum, and left medial lemniscus (ML) in FMS patients. Some regions showing structural changes also showed changes in resting-state functional activation. Functional analysis showed that FMS patients have reduced ALFF values in the left CST, left ML, right PCR, right cingulate/hippocampus, and right FX/ST. Furthermore, the degree of ALFF reduction in the right cingulate/hippocampus and left ML was positively correlated with anxiety, depression, and pain severity scores. Conclusion: This study provides a preliminary exploration of the mechanisms underlying FMS in terms of WM functional signals. The observed reduction in WM functional activation offers novel insights into the pathophysiology of FMS and highlights potential targets for WM-focused therapeutic strategies.

This study investigates the network mechanisms of temporal lobe epilepsy (TLE) using MEG data, focusing on directed connectivity networks across different frequency bands. Unlike previous studies that primarily localize epileptogenic zones, this research aims to explore whole-brain network differences between left TLE (lTLE), right TLE (rTLE), and healthy controls (HCs). MEG data from 13 lTLE patients, 21 rTLE patients, and 14 HCs were source-reconstructed to 116 brain regions (AAL116). Directed Transfer Function (DTF) was used to construct directed connectivity networks, followed by networks and graph-theoretical analyses. The results indicate that, compared to HCs, TLE subjects exhibited a significant increase in average connectivity strength in the Low Gamma band. The connectivity patterns across frequency bands in TLE patients were found to be unstable. Both HC and TLE subjects demonstrated left hemisphere lateralization. In the mid-to-low frequency bands, TLE subjects showed increases in global clustering coefficient (GCC), global characteristic path length (GCPL), and local efficiency (LE) compared to HCs, which is attributed to enhanced synchronization between local brain regions in TLE subjects.

Temporal lobe epilepsy (TLE) is considered a network disorder rather than a localized lesion, making it essential to study the network mechanisms underlying TLE. In this study, we constructed directed brain networks based on clinical MEG data using the Granger Causality Analysis (GCA) method, aiming to provide new insights into the network mechanisms of TLE. MEG data from 13 lTLE and 21 rTLE patients and 14 healthy controls (HCs) were analyzed. The preprocessed MEG data were used to construct directed brain networks using the GCA method and undirected brain networks using the Pearson Correlation Coefficient (PCC) method. Graph theoretical analysis extracted global and local topologies from the binary matrix, and SVM classified topologies with significant differences (p < 0.05). Comparative studies were performed on connectivity strengths, graph theory metrics, and SVM classifications between GCA and PCC, with an additional analysis of GCA-weighted network connectivity. The results show that TLE patients showed significantly increased functional connectivity based on GCA compared to the control group; similarities of the hub brain regions between lTLE and rTLE patients and the cortical–limbic–thalamic–cortical loop were identified; TLE patients exhibited a significant increase in GCA-based Global Clustering Coefficient (GCC) and Global Local Efficiency (GLE); most brain regions with abnormal local topological properties in TLE patients overlapped with their hub regions. The directionality of brain connectivity has played a significantly more pivotal role in research on TLE. GCA may be a potential tool in MEG analysis to distinguish TLE patients and HC effectively.

Autophagy leads to cellular tolerance of the therapeutic pressure of chemotherapeutic drugs, resulting in treatment resistance. Therefore, the effective monitoring of the autophagy status of tumors in vivo and the regulating of the autophagy level are crucial for improving the efficacy of chemotherapy. In this work, we grafted nitroxide radicals onto the surface of iron oxide nanoparticles (Fe₃O₄ NPs) using dendrimer polymers, yielding Fe₃O₄-NO· NPs that are responsive to reactive oxygen species (ROS) and possess enhanced T1 and T2 signal capabilities in a magnetic resonance imaging (MRI) measurement. The ROS in tumor cells generated by autophagy quenches the nitroxide radicals, thereby weakening the T1 signal. In contrast, Fe₃O₄ NPs are unaffected by intracellular ROS, leading to a stable T2 signal. By comparing the intensity ratio of T1 to T2 in Fe₃O₄-NO· NPs, we can evaluate the in vivo autophagy status within tumors in real time. It also revealed that Fe₃O₄-NO· NPs loaded with doxorubicin (Dox) and combining the autophagy inhibitor exhibited high antitumor activity in cells and tumor-bearing mice. This system, which combines real-time monitoring of tumor cell autophagy with the delivery of chemotherapeutic drugs, provides an innovative and effective strategy for tumor treatment with potential clinical application prospects.

Objectives: Fibromyalgia (FM) has been associated with decreased hippocampal volume; however, the atrophy patterns of hippocampal subregions have not yet been identified. We therefore aimed to evaluate the volumes of hippocampal subregions in FM patients with mild cognitive impairment (MCI), and to explore the relationship between different subregional alterations and cognitive function.Methods: The study included 35 FM patients (21 with MCI and 14 without MCI) and 35 healthy subjects. All subjects performed the Montreal Cognitive Assessment (MoCA) to assess cognitive function. FreeSurfer V.7.3.2 was used to calculate hippocampal subregion volumes. We then compared hippocampal subregion volumes between the groups, and analyzed the relationship between hippocampal subregion volume and cognitive function using a partial correlation analysis method.Results: Compared with the healthy subjects, FM patients with MCI had smaller hippocampal volumes in the left and right CA1 head, Molecular layer head, GC-DG head, and CA4 head, and in the left Presubiculum head. Poorer executive function, naming ability, and attention were associated with left CA1 head and left Molecular layer head atrophy. By contrast, hippocampal subregion volumes in the FM patients without MCI were slightly larger than or similar to those in the healthy subjects, and were not significantly correlated with cognitive function.Conclusion: Smaller volumes of left CA1 head and left Molecular layer head were associated with poorer executive function, naming ability, and attention in FM patients with MCI. However, these results were not observed in the FM patients without MCI. These findings suggest that the hippocampal subregions of FM patients might present compensatory mechanisms before cognitive decline occurs.

Background and purpose: Fibromyalgia is a commonly observed pain disorder linked to cognitive impairments, encompassing challenges in attention, memory, and executive processing. It has been proposed that fibromyalgia may be caused by changes in attention network. However, the neural network mechanisms underlying the relationship between the intrinsic fluctuations of functional connectivity in the brain network and clinical symptoms remain unclear. Methods: 46 fibromyalgia patients and 36 healthy controls underwent resting-state functional MRI (rs-fMRI). We employed static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) to investigate the potential relationship between alterations in sensory integration and attention networks in fibromyalgia and clinical features. Results: Patients with fibromyalgia had reduced functional connectivity within the visual network (VIN), the auditory network (AUN), the default mode network (DMN), and the sensorimotor network (SMN). The decrease in functional connectivity in VIN and DMN was correlated with pain levels, while an increase in functional connectivity between SMN and the posterior dorsal attention network (DAN) was associated with symptoms. dFNC further observed that patients in state 4 show increased functional coupling between the ventral attention network (VAN) and both SMN and DMN, and the number of transitions between states positively correlated with pain levels. Conclusion: dFNC offered additional insights for understanding the pathophysiological mechanisms underlying fibromyalgia. Fibromyalgia patients exhibited increased in the attention network and reduced functionality in visual and auditory networks, which might contribute to the diminished attention towards external environmental stimuli and heightened sensitivity self-perception sensations observed in fibromyalgia.