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<p>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&#xa0;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.</p>

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Nasal and oral breathing modes reconfigure brain network dynamics between stabilizing integration and promoting fragmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • Sadeq Mohammadi,
  • Gholam-Ali Hossein-Zadeh,
  • Mohammad Reza Raoufy

摘要

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.