Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: ① compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.
ObjectivesTo systematically review the efficacy of seven types of cognitive interventions for older adults with mild to moderate Alzheimer's Disease (AD).MethodsWe searched The Cochrane Library, PubMed, EMbase, CNKI, WanFang Data, VIP and CBM databases to collect randomized controlled trials on cognitive interventions for mild to moderate Alzheimer's Disease (AD) from inception to January 2018. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies. STATA 14.0 software was then used to perform a meta-analysis.ResultsA total of 49 randomized controlled trials (RCTs) were included. The results of network meta-analysis revealed that each cognitive intervention had significantly improved the cognitive ability of AD patients. Specifically, nursing intervention (NI) (MD=3.01, 95%CI 1.70 to 4.50, P<0.005) was the most effective enhancer of cognitive ability, followed by music therapy (MT) (MD=2.60, 95%CI 0.96 to 4.30, P<0.001), physical exercise (PE) (MD=2.4, 95%CI 1.0 to 3.9, P<0.001), cognitive rehabilitation (CR) (MD=2.3, 95% CI 0.92 to 3.7, P=0.013), cognitive simulation (CS) (MD=1.7, 95%CI 1.2 to 2.3, P=0.037), computerized cognitive training (CCT) (MD=1.6, 95%CI 0.42 to 2.8, P<0.001), and pharmacological therapies (PT) (MD=1.5, 95%CI 0.24 to 2.8, P=0.041).ConclusionsThe seven types of cognitive interventions are helpful in improving the cognitive ability of Alzheimer's patients, and nursing intervention is the most effective cognitive intervention. Moreover, non-pharmacological therapies may be better than pharmacological therapies.
Objective To analyze the substitution mechanism of surrogate endpoints for traditional Chinese medicine (TCM) clinical efficacy evaluation of chronic heart failure (CHF). Methods To obtain data from the occurrence of surrogate endpoints and cardiogenic death of patients with CHF in 7 hospitals. The causal relationship between surrogate endpoints and cardiogenic mortality was inferred by the Bayesian network model, and the interaction among surrogate endpoints was analyzed by non-conditional logistic regression model. Results A total of 2 961 patients with CHF were included. The results of Bayesian network causal inference showed that cardiogenic mortality had a causal relationship with the surrogate endpoints including NYHA classification (P=0.46), amino-terminal pro-B-type natriuretic peptide (NT-proBNP) (P=0.24), left ventricular ejaculation fraction (LVEF) (P=0.19), and hemoglobin (HB) (P=0.11); non-conditional logistic regression analysis showed that NYHA classification had interaction with NT-proBNP, LVEF, and HB prior to and after adjusting confounders. Conclusions The substitution capability of surrogate endpoints for TCM clinical efficacy evaluation of CHF for cardiogenic mortality are NYHA classification, NT-proBNP, LVEF, and HB in turn, and there is a multiplicative interaction between the main surrogate endpoint NYHA classification and the secondary surrogate endpoints including NT-proBNP, LVEF, and HB, suggesting that when the two surrogate endpoints with interaction exist at the same time, it can enhance the substitution capability of surrogate endpoints for cardiogenic mortality.
This study introduced the construction of individualized risk assessment model based on Bayesian networks, comparing with traditional regression-based logistic models using practical examples. It evaluates the model's performance and demonstrates its implementation in the R software, serving as a valuable reference for researchers seeking to understand and utilize Bayesian network models.