Cognitive flexibility, a core dimension of executive functions, refers to one's ability to switch between multiple tasks and sets in a quick and flexible manner. However, whether objective neuroimaging can be used to quantitatively predict cognitive flexibility at the individual level remains largely unexplored. High-resolution magnetic resonance imaging data of 100 healthy young participants from the Human Connectome Project (HCP) dataset were used to calculate gray matter volume (GMV). Cognitive flexibility was assessed by the Dimensional Change Card Sort Test (DCCS). Using a multivariate machine learning technique known as relevance vector regression (RVR), we examined the relationship between GMV and cognitive flexibility performance. We found that the application of RVR to GMV allowed quantitative prediction of the DCCS scores with statistically significant accuracy (correlation = 0.41, P = 0.0001; mean squared error = 73.35, P = 0.0001). Accurate prediction was mainly based on GMV in the temporal regions. In addition, a univariate approach also revealed an inverse association between DCCS scores and GMV in the temporal areas. Our findings provide preliminary support to the development of neuroimaging techniques as a useful means to inform the cognitive assessment of individuals. Furthermore, the significant contribution of temporal regions suggests the prominent role of temporal cortex morphology in individual differences in cognitive flexibility.
Keywords: Cognitive flexibility; Gray matter volume; Magnetic resonance imaging; Prediction; Relevance vector regression.