讲师(高校)
性别:男
出生日期:1987-04-06
毕业院校:The University of Tokyo
学历:博士研究生
学位:工学博士学位
入职时间:2017-09-19
所在单位:华侨大学信息学院
办公地点:华侨大学厦门校区机电信息大楼B544
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在职信息:在岗
职务:讲师
学科:模式识别与智能系统
信号与信息处理生物医学工程生物医学工程 学术荣誉:2018 当选:福建省引进高层次人才
2023-09-30 厦门市高层次人才B类
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2022-09-30 厦门市高层次人才C类
2022-09-30 福建省高层次人才C类
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发表刊物:Brain and Cognition
关键字:Small-worldFunctional networkMEGChildVisual processing (Gv) abilityFree viewing of video
摘要:Application of graph theory to analysis of functional networks in the brain is an important research trend. Extensive research on the resting state has shown a "small-world" organization of the brain network as a whole. However, the small-worldness of children's brain networks in a working state has not yet been well characterized. In this paper, we used a custom-made, child-sized magnetoencephalography (MEG) device to collect data from children while they were watching cartoon videos. Network structures were analyzed and compared with scores on the Kaufman Assessment Battery for Children (K-ABC). The results of network analysis showed that (1) the small-world scalar showed a negative correlation with the simultaneous processing raw score, a measure of visual processing (Gv) ability, and (2) the children with higher simultaneous processing raw scores possessed network structures that can be more efficient for local information processing than children with lower scores. These results were compatible with previous studies on the adult working state. Additional results obtained from further analysis of the frontal and occipital lobes indicated that high cognitive performance could represent better local efficiency in task-related sub-networks. Under free viewing of cartoon videos, brain networks were no longer confined to their strongest small-world states; connections became clustered in local areas such as the frontal and occipital lobes, which might be a more useful configuration for handling visual processing tasks.
论文类型:期刊论文
是否译文:否
发表时间:2014-04-01
收录刊物:SCI、SSCI
DOI码:10.1016/j.bandc.2014.01.011