Dynamic temporal patterns of DMN connectivity in epilepsy using hidden (semi-) Markov models

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Altered connectivity within the default mode network (DMN) has been associated with epilepsy, highlighting its role in seizure propagation. In this study, we investigate the temporal patterns of DMN connectivity in epilepsy patients compared to healthy controls using data-driven models of dynamic functional connectivity (dFC). Specifically, we employ one Hidden Markov Model (HMM) and two Hidden Semi-Markov Models (HSMMs) with Gamma and Poisson sojourn distributions to capture latent brain state transitions, as well as hidden connectivity states and their temporal properties. Dynamic metrics (i.e., fractional occupancy, switching rate, and mean lifetime) were derived for each subject, revealing prolonged dwell times in low-connectivity states and reduced flexibility in state transitions, particularly in low-connectivity DMN states. HSMMs, especially the Gamma variant, demonstrated superior sensitivity in capturing these alterations compared to the standard HMM, highlighting the importance of flexible sojourn modeling in dynamic functional connectivity analysis. Additionally, group-specific transition patterns suggested disrupted temporal progression of DMN state transitions. Our findings highlight the potential of HSMMs in capturing alterations in functional brain states and provide new insights into the dynamic reorganization of the DMN in epilepsy.

The online version contains supplementary material available at 10.1007/s11571-025-10382-3.