Your browser doesn't support javascript.
loading
Predicting creative behavior using resting-state electroencephalography.
Chhade, Fatima; Tabbal, Judie; Paban, Véronique; Auffret, Manon; Hassan, Mahmoud; Vérin, Marc.
Afiliação
  • Chhade F; CIC-IT INSERM 1414, Université de Rennes, Rennes, France. fatima1chhade@outlook.com.
  • Tabbal J; Institute of Clinical Neurosciences of Rennes (INCR), Rennes, France.
  • Paban V; MINDIG, Rennes, France.
  • Auffret M; CRPN, CNRS-UMR 7077, Aix Marseille Université, Marseille, France.
  • Hassan M; CIC-IT INSERM 1414, Université de Rennes, Rennes, France.
  • Vérin M; France Développement Électronique, Monswiller, France.
Commun Biol ; 7(1): 790, 2024 Jun 29.
Article em En | MEDLINE | ID: mdl-38951602
ABSTRACT
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Descanso / Encéfalo / Criatividade / Eletroencefalografia Limite: Adult / Female / Humans / Male Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Descanso / Encéfalo / Criatividade / Eletroencefalografia Limite: Adult / Female / Humans / Male Idioma: En Ano de publicação: 2024 Tipo de documento: Article