Abstract Details

Title Predicting Real-Life Creativity Using Resting State Electroencephalography

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P9 - Poster Session 9 (11:45 AM-12:45 PM)

Poster/Presentation
Number 3-014

Objective The aim of this study is to uncover large-scale 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 in novel subjects.

Background Creativity is a fundamental ability for human progress. Cognitive 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.

Design/Methods We acquired resting state HD-EEG data from 90 healthy participants who completed the «Inventory of Creative Activities and Achievements» questionnaire. We then employed connectome-based predictive modeling (CPM); a machine-learning technique that predicts behavioral measures from brain connectivity features.

Results Using a support vector regression, our results revealed patterns of functional connectivity related to both high and low creativity, in the gamma frequency band (30-45Hz). In leave-one-out cross-validation, the combined model of high and low creativity networks predicted individual creativity scores with very good accuracy (r= 0.34, p= 0.0009). Furthermore, the predictive power of the model was established by an external validation on a second independent resting state EEG dataset (N= 41), where we found a statistically significant relationship between the observed and predicted creativity scores (r= 0.37, p= 0.01).

Conclusions These findings reveal large-scale electrophysiological networks that could predict individual real-life creativity at rest, providing a crucial foundation for developing EEG network-based markers of creativity.

Authors/Disclosures
Fatima Chhade (CIC-IT INSERM 1414, Université de Rennes) PRESENTER	No disclosure on file
Judie Tabbal, PhD	Dr. Tabbal has nothing to disclose.
Veronique Paban, PhD	Mrs. Paban has nothing to disclose.
Manon Auffret, PhD, PharmD, DAAN	Dr. Auffret has received personal compensation for serving as an employee of France Développement Electronique. Dr. Auffret has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Aguettant. The institution of Dr. Auffret has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Aguettant. The institution of Dr. Auffret has received research support from Oxylis Medical. Dr. Auffret has received publishing royalties from a publication relating to health care. Dr. Auffret has received publishing royalties from a publication relating to health care. Dr. Auffret has received publishing royalties from a publication relating to health care. Dr. Auffret has received publishing royalties from a publication relating to health care.
Mahmoud Hassan, PhD	Mr. Hassan has nothing to disclose.
Marc Verin (CHU Hopital Pontchaillou)	Mr. Verin has nothing to disclose.

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