| Literature DB >> 28890001 |
Bingqing Jiao1, Delong Zhang1, Aiying Liang2, Bishan Liang3, Zengjian Wang1, Junchao Li1, Yuxuan Cai1, Mengxia Gao1, Zhenni Gao1, Song Chang1, Ruiwang Huang4, Ming Liu5.
Abstract
Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability.Entities:
Keywords: Connectome; Creativity; Individual difference; Multiple linear regression model; Resting-state fMRI
Mesh:
Year: 2017 PMID: 28890001 DOI: 10.1016/j.biopsycho.2017.09.003
Source DB: PubMed Journal: Biol Psychol ISSN: 0301-0511 Impact factor: 3.251