Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment.

OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) has recently emerged as a novel treatment option for patients with major depressive disorder (MDD), but clinical observations reveal variability in patient's responses to rTMS. Therefore, it is clinically significant to investigate the baseline neuroimaging differences between patients with (Responder) and without (NonResponder) response to rTMS treatment and predict rTMS treatment outcomes based on baseline neuroimaging data. METHOD: Baseline resting-state EEG data and Beck Depression Inventory (BDI) were collected from 74 rTMS Responder, 43 NonResponder, and 47 matched healthy controls (HC). EEG microstate analysis was applied to analyze common and differential microstate characteristics of Responder and NonResponder. In addition, the microstate temporal parameters were sent to four machine learning models to classify Responder from NonResponder. RESULT: There exists some common and differential EEG microstate characteristics for Responder and NonResponder. Specifically, compared to the HC group, both Responder and NonResponder exhibited a significant increase in the occurrence of microstate A. Only Responder showed an increase in the coverage of microstate A, occurrence of microstate D, transition probability (TP) from A to D, D to A, and C to A, and a decrease in the duration of microstates B and E, TP from A to B and C to B compared to HC. Only NonResponder exhibited a significant decrease in the duration of microstate D, TP from C to D, and an increase in the occurrence of microstate E, TP from C to E compared to HC. The primary differences between the Responder and NonResponder are that Responder had higher parameters for microstate D, TP from other microstates to D, and lower parameters for microstate E, TP from other microstates to E compared to NonResponder. Baseline parameters of microstate D showed significant correlation with Beck Depression Inventory (BDI) reduction rate. Additionally, these microstate features were sent to four machine learning models to predict rTMS treatment response and classification results indicate that an excellent predicting performance (accuracy = 97.35 %, precision = 96.31 %, recall = 100 %, F1 score = 98.06 %) was obtained when using AdaBoost model. These results suggest that baseline resting-state EEG microstate parameters could serve as robust indicators for predicting the effectiveness of rTMS treatment. CONCLUSION: This study reveals significant baseline EEG microstate differences between rTMS Responder, NonResponder, and healthy controls. Microstates D and E in baseline EEG can serve as potential biomarkers for predicting rTMS treatment outcomes in MDD patients. These findings may aid in identifying patients likely to respond to rTMS, optimizing treatment plans and reducing trial-and-error approaches in therapy selection.

Visual Feedback Gain Modulates the Activation of Task-Related Networks and the Suppression of Non-Task Networks During Precise Grasping.

Visual feedback gain is a crucial factor influencing the performance of precision grasping tasks, involving multiple brain regions of the visual motor system during task execution. However, the dynamic changes in brain network during this process remain unclear. The aim of this study is to investigate the impact of changes in visual feedback gain during precision grasping on brain network dynamics. Sixteen participants performed precision grip tasks at 15% of MVC under low (0.1°), medium (1°), and high (3°) visual feedback gain conditions, with simultaneous recording of EEG and right-hand precision grip data during the tasks. Utilizing electroencephalogram (EEG) microstate analysis, multiple parameters (Duration, Occurrence, Coverage, Transition probability(TP)) were extracted to assess changes in brain network dynamics. Precision grip accuracy and stability were evaluated using root mean square error(RMSE) and coefficient of variation(CV) of grip force. Compared to low visual feedback gain, under medium/high gain, the Duration, Occurrence, and Coverage of microstates B and D increase, while those of microstates A and C decrease. The Transition probability from microstates A, C, and D to B all increase. Additionally, RMSE and CV of grip force decrease. Occurrence and Coverage of microstates B and C are negatively correlated with RMSE and CV. These findings suggest that visual feedback gain affects the brain network dynamics during precision grasping; moderate increase in visual feedback gain can enhance the accuracy and stability of grip force, whereby the increased Occurrence and Coverage of microstates B and C contribute to improved performance in precision grasping. Our results play a crucial role in better understanding the impact of visual feedback gain on the motor control of precision grasping.

Short-term antipsychotic treatment reduces functional connectivity of the striatum in first-episode drug-naïve early-onset schizophrenia.

BACKGROUND: The striatum is thought to play a critical role in the pathophysiology and antipsychotic treatment of schizophrenia. Previous studies have revealed abnormal functional connectivity (FC) of the striatum in early-onset schizophrenia (EOS) patients. However, no prior studies have examined post-treatment changes of striatal FC in EOS patients. METHODS: We recruited 49 first-episode drug-naïve EOS patients to have resting-state functional magnetic resonance imaging scans at baseline and after 8 weeks of treatment with antipsychotics, along with baseline scanning of 34 healthy controls (HCs) for comparison purposes. We examined the FC values between each seed in striatal subregion and the rest of the brain. The Positive and Negative Syndrome Scale (PANSS) was applied to measure psychiatric symptoms in patients. RESULTS: Compared with HCs at baseline, EOS patients exhibited weaker FC of striatal subregions with several brain regions of the salience network and default mode network. Meanwhile, FC between the dorsal caudal putamen (DCP) and left supplementary motor area, as well as between the DCP and right postcentral gyrus, was negatively correlated with PANSS negative scores. Furthermore, after 8 weeks of treatment, EOS patients showed decreased FC between subregions of the putamen and the triangular part of inferior frontal gyrus, middle frontal gyrus, supramarginal gyrus and inferior parietal lobule. CONCLUSIONS: Decreased striatal FC is evident, even in the early stages of schizophrenia, and enhance our understanding of the neurodevelopmental abnormalities in schizophrenia. The findings also demonstrate that reduced striatal FC occurs after antipsychotic therapy, indicating that antipsychotic effects need to be accounted for when considering striatal FC abnormalities in schizophrenia.

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.