Deep brain stimulation in the globus pallidus alleviates motor activity defects and abnormal electrical activities of the parafascicular nucleus in parkinsonian rats.

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD). The most common sites targeted for DBS in PD are the globus pallidus internal (GPi) and subthalamic nucleus (STN). However, STN-DBS and GPi-DBS have limited improvement in some symptoms and even aggravate disease symptoms. Therefore, discovering new targets is more helpful for treating refractory symptoms of PD. Therefore, our study selected a new brain region, the lateral globus pallidus (GP), as the target of DBS, and the study found that GP-DBS can improve motor symptoms. It has been reported that the thalamic parafascicular (PF) nucleus is strongly related to PD pathology. Moreover, the PF nucleus and GP have very close direct and indirect fiber connections. However, whether GP-DBS can change the activity of the PF remains unclear. Therefore, in this study, we monitored the activity changes in the PF nucleus in PD rats during a quiet awake state after GP-DBS. We found that GP-DBS could reverse the electrical activity of the PF nucleus in PD model rats, including the discharge pattern of the neurons and the local field potential (0.7-12 and 12-70 Hz). Based on the results mentioned above, PF activity in PD model rats could be changed by GP-DBS. Thus, the normalization of PF neuronal activity may be a potential mechanism for GP-DBS in the treatment of PD; these findings lay the foundation for PD treatment strategies.

Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment.

To analyze the application value of artificial intelligence model based on Visual Geometry Group- (VGG-) 16 combined with quantitative electroencephalography (QEEG) in cerebral small vessel disease (CSVD) with cognitive impairment, 72 patients with CSVD complicated by cognitive impairment were selected as the research subjects. As per Diagnostic and Statistical Manual (5th Edition), they were divided into the vascular dementia (VD) group of 34 cases and vascular cognitive impairment with no dementia (VCIND) group of 38 cases. The two groups were analyzed for the clinical information, neuropsychological test results, and monitoring results of QEEG based on intelligent algorithms for more than 2 hours. The accuracy rate of VGG was 84.27% and Kappa value was 0.7, while that of modified VGG (nVGG) was 88.76% and Kappa value was 0.78. The improved VGG algorithm obviously had higher accuracy. The test results found that the QEEG identified 8 normal, 19 mild, 10 moderate, and 0 severe cases in the VCIND group, while in the VD group, the corresponding numbers were 4, 13, 11, and 7; in the VCIND group, 7 cases had the normal QEEG, 11 cases had background changes, 9 cases had abnormal waves, and 11 cases had in both background changes and abnormal waves, and in the VD group, the corresponding numbers were 5, 2, 5, and 22, respectively; in the VCIND group, QEEG of 18 patients had no abnormal waves, QEEG of 11 patients had a few abnormal waves, and QEEG of 9 patients had many abnormal waves, and QEEG of 0 people had a large number of abnormal waves, and in the VD group, the corresponding numbers were 7, 6, 12, and 9. The above data were statistically different between the two groups (P < 0.05). Hence, QEEG based on intelligent algorithms can make a good assessment of CSVD with cognitive impairment, which had good clinical application value.