Connectome-Based Predictive Modeling of Creativity Anxiety

Zhiting Ren, Richard J. Daker, Liang Shi, Jiangzhou Sun, Roger E. Beaty, Xinran Wu, Qunlin Chen, Wenjing Yang, Ian M. Lyons, Adam E. Green, Jiang Qiu

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or “edges” that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages.

Original languageEnglish (US)
Article number117469
StatePublished - Jan 15 2021

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience


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