56: Functional Connectivity Measured by Cross-Validated Graphical Lasso Enhances Prediction of Cognition

Music City Center 

Understanding how functional magnetic resonance imaging (fMRI)-based functional connectivity underlies complex cognitive functions is crucial for identifying disease-related changes in cognition. Traditional approaches to quantifying functional connectivity (FC), such as Pearson's correlation and Tikhonov regularization, have been used to predict fluid and crystallized cognitive scores with limited success. Recent advancements have leveraged latent space representations to enhance prediction, but at the cost of FC network interpretability. We propose using graphical LASSO with a cross-validated tuning parameter to measure FC networks. Graphical LASSO estimates the inverse covariance matrix under a multivariate normal model by maximizing the l1-penalized log-likelihood, providing a sparse solution in high-dimensional settings. We demonstrate that this approach improves the prediction of individual crystallized, fluid and total cognitive scores in 997 healthy young adults from the Human Connectome Project. Results are compared to Pearson's correlation and Tikhonov regularization across different high-dimensional grey matter parcellation choices.

Gaussian graphical model

high-dimensional statistics

network analysis

graphical LASSO

functional connectivity

prediction 

Section on Statistics in Imaging