Motivated by a study on adolescent mental health, we conduct a dynamic connectivity analysis using resting-state functional magnetic resonance imaging (fMRI) data. A dynamic connectivity analysis investigates how the interactions between different regions of the brain, represented by the different dimensions of a multivariate time series, change over time. We will discuss the modeling framework from two distinct approaches: Hidden Markov models (HMMs), and changepoint analysis. We will discuss the strengths and limitations of these approaches, and show their utility in the analysis of resting-fMRI data from two studies: the Brain Imaging Development of Girls’ Emotion and Self (BRIDGES) and the Adolescent Brain Cognitive Development Study (ABCD).