Unraveling the Cognitive Secrets of Chess Experts: Investigating Dynamic Functional Brain Connectivity through rs-fMRI Analysis

Abstract

This study investigates the dynamic functional connectivity (DFC) in resting-state fMRI (rs-fMRI) data of chess players using a Vector Auto-Regression (VAR) model. The VAR model was constructed using the Group Lasso and Sliding Window technique. The study included 116 brain regions, and their correlation was examined in the context of their dynamic connection. Statistical feature selection techniques were used to determine which dynamic connections of brain areas were crucial in discriminating chess masters from novice players. After identifying key DFCs related to these brain regions, a classification model was built to classify chess experts and normal control individuals. Our classification m odel a chieved a n a ccuracy o f 96.33% under a 10-fold cross-validation framework. This performance represents a substantial improvement over previous studies utilizing only rs-fMRI data, which reported a maximum accuracy of 85.45%, indicating a 10.88% enhancement in accuracy. Moreover, our model outperformed methods that combined rs-fMRI with T1-weighted MRI data, which achieved an accuracy of 88%, yielding an additional 8.33% improvement. These results demonstrate that our approach, relying solely on rs-fMRI data, offers a notable advancement in the classification o f c hess expertise.