Schedule of Events | Search Abstracts | Invited Symposia | Symposia | Poster Sessions | Data Blitz Sessions
MEG Resting State Functional Connectivity Predicts Metacognition in Self-Control
Poster Session A - Saturday, March 29, 2025, 3:00 – 5:00 pm EDT, Back Bay Ballroom/Republic Ballroom
Benjamin Kinder1, Dong James Sung1, Uri Maoz2, Aaron Schurger2, Mathieu Landry3; 1Montreal Neurological Institute, McGill University, 2Chapman University, 3Université du Québec à Trois-Rivières
The ability to regulate one's actions is crucial for success in many aspects of life. A key component of developing and maintaining self-control is metacognition or thinking about one's capacity for self-regulation. We investigated the neural correlates of metacognition in self-control in a group of 60 (28 female) individuals using magnetoencephalography (MEG) resting state functional connectivity. Participants completed the Metacognition in Self-Control Scale (MISCS) (Bürgler et al., 2022) and 5 minutes of resting state in MEG. MEG data was pre-processed following best practices (Gross et al., 2013) and source localized using Brainstorm (Tadel et al., 2013). We computed connectivity using the weighted phase lag index (wPLI) for each of the canonical frequency bands (δ, θ, α, β, γ) within each voxel and derived clustering coefficients (Brain Connectivity Toolbox; Rubinov & Sporns, 2010) for each parcel of the Desikan-Killiany atlas. Using a Leave One Out paradigm, we trained a model to predict MISCS scores for each participant. Using this method, the model achieved a Spearman correlation of 0.339 (p = 0.0129) and a Pearson correlation of 0.328 (p = 0.0144). These findings show that resting-state MEG functional connectivity predicts metacognition in self-control scores.
Topic Area: EXECUTIVE PROCESSES: Monitoring & inhibitory control