The aim of this study is to explore the effects of continuous theta-burst transcranial magnetic stimulation (cTBS) on functional brain network in emotion processing. Before and after the intervention of cTBS over left dorsolateral prefrontal cortex (DLPFC) of ten participants who were asked to perform the emotion gender recognition task, we recorded their scalp electroencephalograms (EEG). Then we used the phase synchronization of EEG to measure the connectivity between two nodes. We then calculated the network efficiency to describe the efficiency of information transmission in brain regions. Our research showed that after the intervention of cTBS and the stimulation of the emotion face picture, there was an obvious enhancement in the event-related spectral perturbation after stimuli onset in beta band in 100–300 ms. Under the stimulation of different emotion picture, the values of global phase synchronization for negative and neutral stimuli were enhanced compared to positive ones. And the increased small-worldness was found in emotional processing. In summary, based on the effect of activity change in the left DLPFC on emotion processing brain network, the emotional processing mechanism of brain networks were preliminary explored and it provided the reference for the research of emotion processing brain network in the future.
Cognitive reappraisal is an important strategy for emotion regulation. Studies show that even healthy people may not be able to implement this strategy successfully, but the underlying neural mechanism behind the behavioral observation of success or failure of reappraisal is unclear. In this paper, 28 healthy college students participated in an experiment of emotional regulation with the cognitive reappraisal strategy. They were asked to complete the cognitive psychological questionnaires before the experiment. Their behavioral scores and scalp electroencephalogram (EEG) signals were collected simultaneously during the experiment. We divided all the subjects into two groups, according to the statistical test of valence scores. Then we analyzed their questionnaires, early event-related potential (ERP) components N200, P200, and late positive potential (LPP), and calculated the correlation between the valence score and the amplitude of LPP. The results showed that, in both groups, compared with negative-watching, the reappraisal induced larger N200 and P200 components and there were two modulation patterns (“increase” and “decrease”) of the reappraisal effect on the amplitude of early LPP (300−1 000 ms after stimulus onset). Moreover, correlation analysis showed that significant positive correlation between two differences in the successful group, i.e., the greater difference in the valence scoresin between reappraisal and negative-watching, the greater difference in the amplitude of early LPP between reappraisal and negative-watching; but no such effect was found in the failure group. These results indicated that, whether reappraisal was successful or not, no significant effect on early ERP components was found; and there were different patterns of the reappraisal effect on early LPP. The difference between successful and failure groups was mainly reflected in early LPP, that is, the EEG characteristics and behavioral scores of successful group were significantly positively correlated. Furthermore, the small sample analysis showed that this correlation only existed in the pattern of "increase". In the future, more research of this modulation mode is necessary in order to find more stable EEG characteristics under successful cognitive reappraisal in emotion regulation.
ObjectiveTo summarize the value of imaging in the evaluation of non-surgical therapy for pancreatic cancer.MethodThe relevant literatures about imaging evaluation of non-surgical therapy for pancreatic cancer were collected to make an review.ResultsAt present, most of the imaging evaluation of non-surgical therapy for pancreatic cancer were based on the assessment of morphological characteristics of tumors, such as contrast-enhanced CT and MRI. However, only morphological changes of tumors could not accurately evaluate the response of pancreatic cancer after non-surgical treatment. A few studies had explored the value of functional imaging and artificial intelligence.ConclusionsNon-surgical therapy provides new treatment opportunities for unresectable pancreatic cancer, especially the proposed of neoadjuvant therapy, which provides the possibility of operation for patients with advanced pancreatic cancer. More imaging indicators with stronger objectivity, higher accuracy, and wider universality need to be improved and developed in the future.
Autophagy is a programmed cell degradation process that is involved in a variety of physiological and pathological processes including malignant tumors. Abnormal induction of autophagy plays a key role in the development of hepatocellular carcinoma (HCC). We established a prognosis prediction model for hepatocellular carcinoma based on autophagy related genes. Two hundred and four differentially expressed autophagy related genes and basic information and clinical characteristics of 377 registered hepatocellular carcinoma patients were retrieved from the cancer genome atlas database. Cox risk regression analysis was used to identify autophagy-related genes associated with survival, and a prognostic model was constructed based on this. A total of 64 differentially expressed autophagy related genes were identified in hepatocellular carcinoma patients. Five risk factors related to the prognosis of hepatocellular carcinoma patients were determined by univariate and multivariate Cox regression analysis, including TMEM74, BIRC5, SQSTM1, CAPN10 and HSPB8. Age, gender, tumor grade and stage, and risk score were included as variables in multivariate Cox regression analysis. The results showed that risk score was an independent prognostic risk factor for patients with hepatocellular carcinoma (HR = 1.475, 95% CI = 1.280–1.699, P < 0.001). In addition, the area under the curve of the prognostic risk model was 0.739, indicating that the model had a high accuracy in predicting the prognosis of hepatocellular carcinoma. The results suggest that the new prognostic risk model for hepatocellular carcinoma, established by combining the molecular characteristics and clinical parameters of patients, can effectively predict the prognosis of patients.