Influencing factors of corticomuscular coherence in stroke patients.

Stroke, also known as cerebrovascular accident, is an acute cerebrovascular disease with a high incidence, disability rate, and mortality. It can disrupt the interaction between the cerebral cortex and external muscles. Corticomuscular coherence (CMC) is a common and useful method for studying how the cerebral cortex controls muscle activity. CMC can expose functional connections between the cortex and muscle, reflecting the information flow in the motor system. Afferent feedback related to CMC can reveal these functional connections. This paper aims to investigate the factors influencing CMC in stroke patients and provide a comprehensive summary and analysis of the current research in this area. This paper begins by discussing the impact of stroke and the significance of CMC in stroke patients. It then proceeds to elaborate on the mechanism of CMC and its defining formula. Next, the impacts of various factors on CMC in stroke patients were discussed individually. Lastly, this paper addresses current challenges and future prospects for CMC.

Multi feature fusion network for schizophrenia classification and abnormal brain network recognition.

Schizophrenia classification and abnormal brain network recognition have an important research significance. Researchers have proposed many classification methods based on machine learning and deep learning. However, fewer studies utilized the advantages of complementary information from multi feature to learn the best representation of schizophrenia. In this study, we proposed a multi-feature fusion network (MFFN) using functional network connectivity (FNC) and time courses (TC) to distinguish schizophrenia patients from healthy controls. DNN backbone was adopted to learn the feature map of functional network connectivity, C-RNNAM backbone was designed to learn the feature map of time courses, and Deep SHAP was applied to obtain the most discriminative brain networks. We proved the effectiveness of this proposed model using the combining two public datasets and evaluated this model quantitatively using the evaluation indexes. The results showed that the functional network connectivity generated by independent component analysis has advantage in schizophrenia classification by comparing static and dynamic functional connections. This method obtained the best classification accuracy (ACC=87.30%, SPE=89.28%, SEN=85.71%, F1 =88.23%, and AUC=0.9081), and it demonstrated the superiority of this proposed model by comparing state-of-the-art methods. Ablation experiment also demonstrated that multi feature fusion and attention module can improve classification accuracy. The most discriminative brain networks showed that default mode network and visual network of schizophrenia patients have aberrant connections in brain networks. In conclusion, this method can identify schizophrenia effectively and visualize the abnormal brain network, and it has important clinical application value.

Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion.

Introduction: At present, elucidating the cortical origin of EEG microstates is a research hotspot in the field of EEG. Previous studies have suggested that the prefrontal cortex is closely related to EEG microstate C and D, but whether there is a causal link between the prefrontal cortex and microstate C or D remains unclear. Methods: In this study, pretrial EEG data were collected from ten patients with prefrontal lesions (mainly located in inferior and middle frontal gyrus) and fourteen matched healthy controls, and EEG microstate analysis was applied. Results: Our results showed that four classical EEG microstate topographies were obtained in both groups, but microstate C topography in patient group was obviously abnormal. Compared to healthy controls, the average coverage and occurrence of microstate C significantly reduced. In addition, the transition probability from microstate A to C and from microstate B to C in patient group was significantly lower than those of healthy controls. Discussion: The above results demonstrated that the damage of prefrontal cortex especially inferior and middle frontal gyrus could lead to abnormalities in the spatial distribution and temporal dynamics of microstate C not D, showing that there is a causal link between the inferior and middle frontal gyrus and the microstate C. The significance of our findings lies in providing new evidence for elucidating the cortical origin of microstate C.

[Factors related to biologic behavior in giant cell tumor of bone].

OBJECTIVE: To study the histogenesis of giant cell tumor (GCT) and factors related to tumor recurrence, invasiveness and malignant transformation. METHODS: The clinical features, radiologic classification, surgical approach, pathologic findings, immunophenotypes and follow-up data of 123 cases of GCT were analyzed. RESULTS: There was a significant correlation between tumor recurrence and radiographic classification (P = 0.032), over-expression of CD147 (P = 0.034) and p53 (P = 0.005), and surgical approach (P = 0.0048) in GCT. The biologic behavior showed no correlation with intramedullary infiltration, cortical bone involvement, parosteal soft tissue extension, tumor thrombi, fusiform changes of mononuclear tumor cells, mitotic count, Ki-67 index, coagulative tumor necrosis, secondary aneurysmal bone cyst formation, and adjoining bony reaction. The positive rate of p63 in stromal cells of GCT (79.7%, 94/118) was significantly higher than that in chondroblastoma (44.7%, 21/47), osteosarcoma (22.2%, 10/45) and other giant cell-rich tumors. CONCLUSIONS: GCT is a bone tumor of low malignant potential. It is sometimes characterized by locally invasive growth, active proliferation, coagulative necrosis, secondary aneurysmal bone cyst and surrounding bony reaction. It is difficult to predict the biologic behavior of GCT. Over-expression of p53 in the tumor cells and CD147 in all components of GCT correlate with tumor invasiveness, recurrence and malignant transformation. Selection of suitable surgical approach with reference to radiologic classification is considered as an important factor in reducing the recurrence rate.