Epilepsy lesion localization method based on brain function network.

Objective: In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. Method: To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. Results: The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Conclusion: Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Effects of N6 -(4-hydroxybenzyl) adenine riboside in stress-induced insomnia in rodents.

Adenosine exhibits a somnogenic effect; however, there is no adenosinergic hypnotic because of cardiovascular effects. This study investigated whether N6-(4-hydroxybenzyl) adenine riboside (T1-11), extracted from Gastrodia elata, produces somnogenic effects in rodents. We determined the involvement of adenosine 2A receptors (A2ARs) in GABAergic neurons of the ventrolateral preoptic area (VLPO) and the cardiovascular effects. Change of cage bedding is employed as a stressor to induce insomnia in rodents, and electroencephalograms and electromyograms were used to acquire and analyse sleep-wake activity. We found that intracerebroventricular administration of T1-11 before a dark period increased non-rapid eye movement (NREM) and rapid eye movement (REM) sleep during a dark period, and T1-11-induced sleep increases were blocked by the A2AR antagonist, SCH58261, in naïve rats. Oral administration of T1-11 increased NREM sleep during both dark and light periods. Microinjection of the A2AR antagonist, SCH58261, into the VLPO blocked sleep effects of T1-11. In addition to the somnogenic effect in naïve mice, T1-11 suppressed the stress-induced insomnia and this suppressive effect was blocked by SCH58261. C-fos expression in GABAergic neurons of VLPO was increased after administration of T1-11 in Gad2-Cre::Ai14 mice, suggesting the activation of GABAergic neurons in the VLPO. T1-11 exhibited no effects on heart rate and the low frequency/high frequency ratio of heart rate variability. We concluded that T1-11 elicited somnogenic effects and effectively ameliorated acute stress-induced insomnia. The somnogenic effect is mediated by A2ARs to activate GABAergic neurons in the VLPO. This adenosine analogue could be a potential hypnotic because of no sympathetic and parasympathetic effects on the cardiovascular system.

Correlation of vitamin D receptor gene (ApaI) polymorphism with periodontitis: A meta-analysis of Chinese population.

Many studies have tried to elucidate the connection between vitamin D receptor (VDR) gene (ApaI) polymorphism and periodontitis; however, so far there is no consensus. To further assess the impact of ApaI polymorphism on periodontitis risk, we have conducted a meta-analysis of Chinese population. Relevant literatures were searched according to PubMed and Chinese database in January 2019. The strength of correlation was evaluated by combining odds ratio (ORs) and 95% confidence interval (CIs). Six case-control studies were identified with inclusion criteria, including 734 cases of periodontitis and 687 controls. Based on the overall analysis, the VDR ApaI polymorphism was not due to the risk of periodontitis in all models. Subgroup analysis showed that the risk of periodontitis in North China was significantly reduced. To sum up, the study shows that VDR-ApaI polymorphism may be connected with a lower risk of periodontitis in northern China. It is suggested that inferential studies should be conducted in other ethnic groups.

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization.

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S-transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S-transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.