Breathing rhythmically coordinates neural oscillations across the brain, yet how the breathing mode (nasal vs. oral) modulates large-scale functional networks over time remains unclear. Building on prior static connectivity findings, this study applied dynamic functional connectivity (dFC) analysis using a hidden Markov model (HMM) to resting-state fMRI data from 20 healthy adults during nasal and oral breathing, focusing on the 0.1-0.2 Hz frequency band. Three recurrent brain states were identified: (1) a weakly connected, segregated state; (2) a globally integrated state dominated by default mode, frontoparietal, salience, and limbic networks; and (3) a partially segregated intermediate state. Compared with oral breathing, nasal breathing stabilized the integrated state, increasing its lifetime (p-FDR = 0.03) and reducing switching rates (p-FDR = 0.002). Oral breathing showed greater fractional occupancy of the intermediate state (p-FDR = 0.03) and a higher probability of transitions from integration to fragmentation (p-FDR = 0.02). Graph-theoretic analysis also revealed that nasal breathing supported a configuration with higher efficiency and lower modularity. Taken together, this study provides the first respiration-entrained, HMM-based dFC analysis of resting-state fMRI, demonstrating that nasal breathing entrains a stable, globally coherent state, whereas oral breathing disrupts this stability and promotes fragmented network organization.
Keywords: Brain function; Brain networks; Breathing; Dynamic functional connectivity; Hidden Markov model; Resting-state fMRI.
© 2026. The Author(s).