Dynamics of functional network organization through graph mixture learning

•We estimate multiple functional states with the Graph Laplacian Mixture Model (GLMM).•GLMM learns the graph structure of the states by estimating the Laplacian matrices.•Each state is characterized by a graph and a probability that captures its dynamic.•On task fMRI, GLMM reveals the experimental paradigm unknown to the method.•DMN was found to be the most prominent network and therefore used for comparisons.