Shu Zhang

Chronic stress impairs working memory, but few studies have explored the protective factors of the impairment. We aimed to investigate the effect of self-awareness on WM processing in people under chronic stress. Participants under chronic stress completed an n-back task after a self-awareness priming paradigm during which electroencephalograms were recorded. The behavioral results showed that participants whose self-awareness was primed reacted faster and more accurately than the controls. Event-related potentials revealed the following P2 was more positive in the self-awareness group than in the controls, indicating that self-awareness enhanced allocation of attention resources at the encoding stage. N2 was attenuated in the self-awareness group compared with the controls, indicating that smaller attention control efforts were required to complete WM tasks adequately after self-awareness priming; and enhanced late positive potential was evoked in the self-awareness group compared with the controls, suggesting self-awareness enabled participants to focus attention resources on the information at the maintenance stage. Critically, mediational analyses showed that LPP mediated the relationship between self-awareness and WM response times. This result suggests that the fact that participants whose self-awareness was primed were able to achieve better behavioral performances may be attributed to their mobilization of sustained attention resources at the maintenance stage. In summary, self-awareness exerted a protective effect on WM in those under chronic stress, which may be due to the enhancements in the allocation and mobilization of attention. These results could be used to develop more specific coping strategies for people under chronic stress.

Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test–retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level semantic features from spatial or temporal representations via N-fMRI. However, integrating both spatial and temporal characteristics of brain activities for better interpreting the patterns under naturalistic stimuli is still underexplored. In this work, a novel hybrid learning framework that comprehensively investigates both the spatial (via Predictive Model) and the temporal [via convolutional neural network (CNN) model] characteristics of the brain is proposed. Specifically, to focus on certain relevant regions from the whole brain, regions of significance (ROS), which contain common spatial activation characteristics across individuals, are selected via the Predictive Model. Further, voxels of significance (VOS), whose signals contain significant temporal characteristics under naturalistic stimuli, are interpreted via one-dimensional CNN (1D-CNN) model. In this article, our proposed framework is applied onto the N-fMRI data during naturalistic classical/pop/speech audios stimuli. The promising performance is achieved via the Predictive Model to differentiate the different audio categories. Especially for distinguishing the classic and speech audios, the accuracy of classification is up to 92%. Moreover, spatial ROS and VOS are effectively obtained. Besides, temporal characteristics of the high-level semantic features are investigated on the frequency domain via convolution kernels of 1D-CNN model, and we effectively bridge the “semantic gap” between high-level semantic features of N-fMRI and low-level acoustic features of naturalistic audios in the frequency domain. Our results provide novel insights on characterizing spatiotemporal patterns of brain activities via N-fMRI and effectively explore the high-level semantic features under naturalistic stimuli, which will further benefit the understanding of the brain working mechanism and the advance of naturalistic stimuli clinical application.

Online social network services have brought a kind of new lifestyle to the world that is parallel to people’s daily offline activities. Social network analysis provides a useful perspective on a range of social computing applications. Social interaction on the Web includes both positive and negative relationships, which is certainly important to social networks. The authors of this article found that the accuracy of the signs of links in the underlying social networks can be predicted. The trust that other users impart on a node is an important attribute of networks. In this article, the authors present a model to compute the prestige of nodes in a trust-based network. The model is based on the idea that trustworthy nodes weigh more. To fulfill this task, the authors first attempt to infer the attitude of one user toward another by predicting signed edges in networks. Then, the authors propose an algorithm to compute the prestige and trustworthiness where the edge weight denotes the trust score. To prove the algorithm’s effectiveness, the authors conducted experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.