Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN, 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA; Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
Neuroimage. 2020 Apr 15;210:116578. doi: 10.1016/j.neuroimage.2020.116578. Epub 2020 Jan 23.
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.
最近的研究深入探讨了创造性思维的个体差异,重点在于描述局部区域水平及其两两相互作用的分布式大规模脑网络,以及整个大脑的全局水平。然而,目前尚不清楚创造性思维与中尺度网络特征(如社区和枢纽组织)如何相关。我们应用了一种数据驱动的方法,来检查来自大量参与者的静息态功能成像数据中的社区和枢纽结构,以及它们与创造性思维个体差异的关系。首先,我们为每个参与者计算了大脑区域分配到同一社区的共同分配概率。我们发现,创造性思维能力越强,内侧颞叶和皮质下区域分配到同一社区的概率越高,反之越低,这表明创造性能力可能反映在大脑网络中尺度组织的个体差异中。然后,我们使用参与者特定的社区来识别网络枢纽——节点,这些节点的连接形成了不同社区之间的桥梁,其连接程度是基于它们的参与系数来量化的。我们发现,默认模式网络和内侧颞叶区域的枢纽度增加与创造性能力呈正相关,而减少则呈负相关。这些发现表明,创造性能力可能反映在默认模式网络和内侧颞叶结构的社区相互作用的个体差异中。总的来说,这些结果表明,研究中尺度脑网络特征与创造性思维之间的关系是富有成效的。