Task-based functional connectivity can predict creativity-related semantic memory network properties that facilitate real-life creativity, according to new research from Science Advances.

Researchers in France led individuals through a task-based functional magnetic resonance imaging (fMRI) session and a semantic relatedness judgment task (RJT) that involved 35 words (595 word pairs). The participants reported, via the Inventory of Creative Activities and Achievements (ICAA) questionnaire, the frequency with which they performed creative activities (C-Act) and their creative achievements (C-Ach).

The researchers discovered in analyzing semantic networks (SemNets) metric predictions of real-life creativity, that real-life creative activities and achievements are predicted through different individual metrics.

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Weighted undirected networks (WUN) average shortest path length (ASPL) and unweighted undirected networks (UUN) modularity (Q) predicted C-Act, and WUN Q and UUN Q predicted C-Ach.

More creative individuals tended to have fewer modular SemNets, even after researchers controlled for variables linked with SemNets metrics or creativity scores.

Connectome predictive modeling (CPM) analyses of task-based functional connectivity indicated brain connectivity clustering coefficient (CC) and efficiency (Eff) promotes reliable predictions of SemNet Q.

For the WUN Q metric, the CPM-based prediction from brain-CC and predictions from brain-Eff were significant. Predictions from brain-Eff were also significant for the UUN Q metric. The CPM-based prediction from brain-CC and brain-Eff were significant for WUN Q during resting state.

SemNet WUN Q and UUN Q metrics predicted C-Ach score. The regression coefficient between brain-Eff and UUN QR was significant. As efficiency of the negative model network that predicts UUN Q increased, modularity of SemNet decreased and real-life creative achievements increased.

Limitations of the study include a small sample, generalization across different creative domains, and the need to test stability of SemNet properties with different words.

“We show that brain connectivity during semantic relatedness judgments predicted individual differences in the modularity of SemNets that was identified as a behavioral marker of individual differences in real-life creativity,” the researchers noted.  

“Specifically, more efficient and denser functional connectivity between the default, control, salience, motor, and visual networks predicted a more integrated semantic memory structure (less modular SemNets) that, in turn, predicted more creative behaviors. These findings provide an unprecedented understanding of how brain and semantic memory networks relate to real-life creative behavior.”


Ovando-Tellez M, Kenett YN, Benedek M, et al. Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure. Sci Adv. Published online February 4, 2022. doi:10.1126/sciadv.abl4294

This article originally appeared on Psychiatry Advisor