Hidden Markov model approaches and Bayesian methods to investigate brain dynamics and cognitive-state switching in children

Characterizing the temporal dynamics of functional interactions between distributed brain regions is of fundamental importance for understanding human brain organization underlying higher-order cognition. Progress in the field has been hampered both by a lack of strong computational techniques to investigate brain dynamics and an inadequate focus on core brain systems involved in higher-order cognition. Here we address these gaps by developing a novel variational Bayesian Hidden Markov Model (VB-HMM) that uncovers non-stationary dynamical functional networks in human fMRI data. VB-HMM revealed multiple short-lived cognitive states characterized by rapid switching and transient connectivity between the salience (SN), default mode (DMN), and central executive (CEN) networks—three brain systems critical for higher-order cognition. Notably, in children, relative to adults, VB-HMM revealed immature dynamic interactions between SN, CEN, and DMN, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our findings suggest that the flexibility of switching between distinct cognitive states is weaker in childhood, and they provide a novel framework for modeling immature brain network organization in children. More generally, the approach used here may be useful for investigating brain dynamics in neurodevelopmental disorders with deficits in higher-order cognition.