Improved brain community structure detection by two-step weighted modularity maximization

by Zhitao Guo, Xiaojie Zhao, Li Yao, Zhiying Long

The human brain can be regarded as a complex network with interacting connections between brain regions. Complex brain network analyses have been widely applied to functional magnetic resonance imaging (fMRI) data and have revealed the existence of community structures in brain networks. The identification of communities may provide insight into understanding the topological functions of brain networks. Among various community detection methods, the modularity maximization (MM) method has the advantages of model conciseness, fast convergence and strong adaptability to large-scale networks and has been extended from single-layer networks to multilayer networks to investigate the community structure changes of brain networks. However, the problems of MM, suffering from instability and failing to detect hierarchical community structure in networks, largely limit the application of MM in the community detection of brain networks. In this study, we proposed the weighted modularity maximization (WMM) method by using the weight matrix to weight the adjacency matrix and improve the performance of MM. Moreover, we further proposed the two-step WMM method to detect the hierarchical community structures of networks by utilizing node attributes. The results of the synthetic networks without node attributes demonstrated that WMM showed better partition accuracy than both MM and robust MM and better stability than MM. The two-step WMM method showed better accuracy of community partitioning than WMM for synthetic networks with node attributes. Moreover, the results of resting state fMRI (rs-fMRI) data showed that two-step WMM had the advantage of detecting the hierarchical communities over WMM and was more insensitive to the density of the rs-fMRI networks than WMM.

Application of direct observation of operational skills in nursing skill evaluation of pressure injury: A randomized clinical trial

Abstract

This was a non-blinded, single-centre, randomized, controlled clinical trial that compared the effectiveness of direct observation of procedural skills (DOPSs)with traditional assessment methods in pressure injury (PI) care skills. The study population included 82 nursing professionals randomly assigned to the study group (n = 41) and the control group (n = 41). Both groups of nurses underwent a 6-month training in PI care skills and were subsequently evaluated. The main outcome variables were the PI skill operation scores and theoretical scores. Secondary outcome variables included satisfaction and critical thinking abilities. Independent sample t-tests and chi-square tests were used to assess differences between the two groups of nurses. The results showed no statistically significant difference in PI skill operation scores between the two groups of nurses (p > 0.05). When comparing the PI theoretical scores, the study group scored higher than the control group, and this difference was statistically significant (p < 0.05). In terms of satisfaction assessment, the study group and the control group showed differences in improving self-directed learning, enhancing communication skills with patients, improving learning outcomes and increasing flexibility in clinical application (p < 0.05). When comparing critical thinking abilities between the two groups of nurses, there was no statistically significant difference at the beginning of the training, but after 3 months following the training, there was a statistically significant difference between the two groups (p < 0.01).The results indicated that the DOPS was effective in improving PI theoretical scores, increasing nurse satisfaction with the training and enhancing critical thinking abilities among nurses.