Predicting real-life creativity using resting state electroencephalography

Neuroscience research has shown that specific functional brain patterns can be related 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 real-life creativity using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity. We acquired resting-state HD-EEG data from 90 participants who completed a creativity questionnaire. 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 revealed functional connectivity patterns related to high and low creativity in the gamma frequency band. In leave-one-out cross-validation, the combined model of high and low creativity networks predicted creativity scores with very good accuracy (r= 0.34, p= 0.0009). Furthermore, the models predictive power was established by an external validation on an independent dataset (N= 41), where we found a statistically significant relationship between the observed and predicted creativity scores (r= 0.37, p= 0.01). These findings reveal large-scale networks that could predict individual real-life creativity at rest, providing a crucial foundation for developing EEG network-based markers of creativity.