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Predicting creative behavior using resting-state electroencephalography
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DOI：10.1038/s42003-024-06461-6.png)
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Abstract
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Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity. Combining HD-EEG with connectome modeling predicts individual creative behavior from resting-state functional connectivity, highlighting the importance of optimal activation interplay across networks in creative cognition.
Keywords:
FUNCTIONAL BRAIN NETWORKS
EEG ALPHA-ACTIVITY
SUSTAINED ATTENTION
NEURAL ACTIVITY
CONNECTIVITY
INFORMATION
PATTERNS
CORTEX
MEMORY
OSCILLATIONS
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