RESUMEN
Objective:To investigate the effects of single intermittent theta-burst stimulation on functional connectivity in patients with mild cognitive impairment(MCI).Methods:From July to November 2020, forty MCI patients were selected and randomly divided into iTBS true stimulation group and iTBS sham-stimulation group, with 20 patients in each group.iTBS targeted the left dorsolateral prefrontal cortex (DLPFC). Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE), activity of daily living scale(ADL), Hamilton depression scale (HAMD) and Hamilton anxiety scale (HAMA) were evaluated at baseline.The resting state electroencephalography (rsEEG) was collected for 5 minutes before and after iTBS in the two groups.The phase lag index(PLI) of EEG functional connectivity was calculated, and the functional connectivity matrix diagram was drawn.SPSS 26.0 software was used for statistical analysis.Data were statistically analyzed by χ2 test, Wilcoxon rank sum test and independent sample t-test. Results:There were no significant differences in scores of MoCA, ADL, HAMD and HAMA between the two groups(all P>0.05). In the iTBS true stimulation group, compared with that before iTBS treatment(0.140(0.133, 0.144)), the PLI of β band increased significantly after iTBS treatment(0.146(0.136, 0.167))( P<0.05). The region of increased PLI was mainly concentrated in the central region(C3/C4-T7/T8). Compared with that before iTBS treatment(0.251(0.232, 0.299)), the PLI of α band increased after iTBS treatment(0.286(0.241, 0.359)), but the difference was not statistically significant( P>0.05). Conclusion:Single iTBS treatment can significantly increase the EEG functional connectivity in patients with MCI, indicating that iTBS targeting the left DLPFC can effectively regulate the EEG functional connectivity in patients with MCI, which may reveal the mechanism of iTBS in improving cognitive function in patients with MCI.
RESUMEN
Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of "small world" and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.