The effects of attention in auditory-visual integration revealed by time-varying networks.

Attention and audiovisual integration are crucial subjects in the field of brain information processing. A large number of previous studies have sought to determine the relationship between them through specific experiments, but failed to reach a unified conclusion. The reported studies explored the relationship through the frameworks of early, late, and parallel integration, though network analysis has been employed sparingly. In this study, we employed time-varying network analysis, which offers a comprehensive and dynamic insight into cognitive processing, to explore the relationship between attention and auditory-visual integration. The combination of high spatial resolution functional magnetic resonance imaging (fMRI) and high temporal resolution electroencephalography (EEG) was used. Firstly, a generalized linear model (GLM) was employed to find the task-related fMRI activations, which was selected as regions of interesting (ROIs) for nodes of time-varying network. Then the electrical activity of the auditory-visual cortex was estimated via the normalized minimum norm estimation (MNE) source localization method. Finally, the time-varying network was constructed using the adaptive directed transfer function (ADTF) technology. Notably, Task-related fMRI activations were mainly observed in the bilateral temporoparietal junction (TPJ), superior temporal gyrus (STG), primary visual and auditory areas. And the time-varying network analysis revealed that V1/A1↔STG occurred before TPJ↔STG. Therefore, the results supported the theory that auditory-visual integration occurred before attention, aligning with the early integration framework.

Microstate feature fusion for distinguishing AD from MCI.

Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (TPs), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of TPs and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of TPs were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (TTPs) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.

[Effects of Mijiandao suppository on intestinal laxation in rats and its mechanisms].

Objective: To investigate the effects of Mijian Daotong Bowel Suppository (MJDs) on the compound diphenoxylate induced constipation model of male rats and its mechanisms. Methods: Sixty SD male rats were randomly divided into blank group, model group, positive group and MJDs group. The constipation model was established by using compound diphenoxylate gavage. The rats in blank group and model group were treated with saline by enema, the rats in positive group and MJDs group were given Kaisailu and honey decoction laxative suppository by enema, respectively, once a day for 10 days. The body weight, fecal water content, gastric emptying rate (GER) and carbon ink propulsion rate (CIPR) of rats were observed during modeling and administration. The effects of MJDs on the pathological changes of colon tissue in constipation rats were investigated by hematoxylin-eosin (HE) staining. The effect of MJDs on 5-hydroxytryptamine (5-HT) in the colon of constipation rats was investigated by ELISA kit. The effects of MJDs on the expressions of aquaporins 3 (AQP3) and aquaporins 4 (AQP4) in the colon of constipation rats were detected by immunohistochemistry. Results: After 10 days of administration, compared with the blank group, the body weight, fecal water content, carbon ink propulsion rate and colon 5-HT content in the model group were decreased significantly, while the expression levels of AQP3 and AQP4 in the colon were increased significantly (P＜0.05, P＜0.01). Compared with the model group, the fecal water content and colon 5-HT content in the positive group were increased significantly, and the expressions of AQP3 and AQP4 in the colon were decreased significantly. The body weight, fecal water content and colon 5-HT content in the MJDs group were increased significantly, and the expressions of AQP3 and AQP4 was decreased significantly (P＜0.05, P＜0.01). Compared with the positive group, the fecal water content of the MJDs group was decreased significantly, and the expressions of AQP3 and AQP4 in the colon of the MJDs group was decreased significantly (P＜0.05, P＜0.01). Gastric emptying rate was not statistically significant difference between the groups. Conclusion: MJDs has good therapeutic effects on constipation, and its mechanisms may be related to up-regulating the content of 5-HT in the colon and down-regulating the expressions of AQP3 and AQP4 in the colon.

Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task.

Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e., before and after training. According to Signed Residual Time (SRT) scores based on response speed and accuracy trade-off, 20 subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks. We found that spectral entropy derived from the retention period of WM on channel FC4 exhibited a high correlation with SRT scores. To this end, spectral entropy was used in support vector machine classifier with linear kernel to differentiate these two groups. Receiver operating characteristics analysis and leave-one out cross-validation (LOOCV) demonstrated that the averaged classification accuracy (CA) was 90.0 and 92.5% for intra-session and inter-session, respectively, indicating that spectral entropy could be used to distinguish these two different WM performance groups successfully. Furthermore, the support vector regression prediction model with radial basis function kernel and the root-mean-square error of prediction revealed that spectral entropy could be utilized to predict SRT scores on individual WM performance. After testing the changes in SRT scores and spectral entropy for each subject by short-time training, we found that 16 in 20 subjects' SRT scores were clearly promoted after training and 15 in 20 subjects' SRT scores showed consistent changes with spectral entropy before and after training. The findings revealed that spectral entropy could be a promising indicator to predict individual's WM changes by training and further provide a novel application about WM for brain-computer interfaces.

Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics.

Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.