Literature DB >> 31982579

Community structure of the creative brain at rest.

Yoed N Kenett1, Richard F Betzel2, Roger E Beaty3.   

Abstract

Recent studies have provided insight into inter-individual differences in creative thinking, focusing on characterizations of distributed large-scale brain networks both at the local level of regions and their pairwise interactions and at the global level of the brain as a whole. However, it remains unclear how creative thinking relates to mesoscale network features, e.g. community and hub organization. We applied a data-driven approach to examine community and hub structure in resting-state functional imaging data from a large sample of participants, and how they relate to individual differences in creative thinking. First, we computed for every participant the co-assignment probability of brain regions to the same community. We found that greater capacity for creative thinking was related to increased and decreased co-assignment of medial-temporal and subcortical regions to the same community, respectively, suggesting that creative capacity may be reflected in inter-individual differences in the meso-scale organization of brain networks. We then used participant-specific communities to identify network hubs-nodes whose connections form bridges across the boundaries of different communities-quantified based on their participation coefficients. We found that increased hubness of DMN and medial-temporal regions were positively and negatively related with creative ability, respectively. These findings suggest that creative capacity may be reflected in inter-individual differences in community interactions of DMN and medial-temporal structures. Collectively, these results demonstrate the fruitfulness of investigating mesoscale brain network features in relation to creative thinking. Published by Elsevier Inc.

Entities:  

Keywords:  Community structure; Creativity; Default mode network; Divergent thinking; Resting-state

Mesh:

Year:  2020        PMID: 31982579     DOI: 10.1016/j.neuroimage.2020.116578

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  5 in total

1.  A mixed-modeling framework for whole-brain dynamic network analysis.

Authors:  Mohsen Bahrami; Paul J Laurienti; Heather M Shappell; Dale Dagenbach; Sean L Simpson
Journal:  Netw Neurosci       Date:  2022-06-01

2.  Creative Connections: Computational Semantic Distance Captures Individual Creativity and Resting-State Functional Connectivity.

Authors:  William Orwig; Ibai Diez; Patrizia Vannini; Roger Beaty; Jorge Sepulcre
Journal:  J Cogn Neurosci       Date:  2020-12-07       Impact factor: 3.225

3.  Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition.

Authors:  Abhishek Uday Patil; Sejal Ghate; Deepa Madathil; Ovid J L Tzeng; Hsu-Wen Huang; Chih-Mao Huang
Journal:  Sci Rep       Date:  2021-01-08       Impact factor: 4.379

4.  Neural alignment predicts learning outcomes in students taking an introduction to computer science course.

Authors:  Meir Meshulam; Liat Hasenfratz; Hanna Hillman; Yun-Fei Liu; Mai Nguyen; Kenneth A Norman; Uri Hasson
Journal:  Nat Commun       Date:  2021-03-26       Impact factor: 14.919

5.  Subcortical structures and visual divergent thinking: a resting-state functional MRI analysis.

Authors:  Zhenni Gao; Xiaojin Liu; Delong Zhang; Ming Liu; Ning Hao
Journal:  Brain Struct Funct       Date:  2021-08-03       Impact factor: 3.270

  5 in total

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