Siyu Zhai, Shuang Liu, Jingjing Tong, Xiaoya Liu, Dong Ming
{"title":"Positive Emotion Impairs Verbal Working Memory Updating: A Brain Network Study","authors":"Siyu Zhai, Shuang Liu, Jingjing Tong, Xiaoya Liu, Dong Ming","doi":"10.1109/CIVEMSA45640.2019.9071608","DOIUrl":null,"url":null,"abstract":"Emotion affects the cognition and behavior of human beings directly or indirectly, as an inevitable fact penetrating into different aspects in daily life. As the rapid development of brain imaging techniques in recent years, increasing attention has paid to the relationship between cognition and emotion with neurophysiological approach. Here, we mainly focused on the effect of different emotions on working memory(WM), which termed as an integral part in the study of cognitive activities. 16 subjects were recruited to experience positive, neutral or negative emotions evoked by pictures from International Affective Picture System(IAPS) firstly, and then required to perform the four-digit verbal working memory task, a virtual measurement of emotional impact on working memory updating was developed by analyzing the brain network characteristic parameters. Partial directed coherence(PDC) was employed to compute the brain network characteristic parameters, including node degree, shortest path length, clustering coefficient and global efficiency during the period of working memory updating under three kinds of emotion states. The results showed that the node degree under positive state was significantly smaller than the neutral and negative states. The global efficiency of positive state was significantly less than the neutral state(p=0.047), as well as the negative state(p=0.027). The complexity of brain network connectivity on positive emotion state is significantly declined, indicating positive emotion impairs verbal working memory during updating period.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIVEMSA45640.2019.9071608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Emotion affects the cognition and behavior of human beings directly or indirectly, as an inevitable fact penetrating into different aspects in daily life. As the rapid development of brain imaging techniques in recent years, increasing attention has paid to the relationship between cognition and emotion with neurophysiological approach. Here, we mainly focused on the effect of different emotions on working memory(WM), which termed as an integral part in the study of cognitive activities. 16 subjects were recruited to experience positive, neutral or negative emotions evoked by pictures from International Affective Picture System(IAPS) firstly, and then required to perform the four-digit verbal working memory task, a virtual measurement of emotional impact on working memory updating was developed by analyzing the brain network characteristic parameters. Partial directed coherence(PDC) was employed to compute the brain network characteristic parameters, including node degree, shortest path length, clustering coefficient and global efficiency during the period of working memory updating under three kinds of emotion states. The results showed that the node degree under positive state was significantly smaller than the neutral and negative states. The global efficiency of positive state was significantly less than the neutral state(p=0.047), as well as the negative state(p=0.027). The complexity of brain network connectivity on positive emotion state is significantly declined, indicating positive emotion impairs verbal working memory during updating period.