Abstract
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.
Data availability
Data are available from the corresponding author upon reasonable request.
References
Tort, A. B., Laplagne, D. A., Draguhn, A. & Gonzalez, J. Global coordination of brain activity by the breathing cycle. Nat Rev. Neurosci. 1–21 (2025).
Heck, D. H. et al. Recent insights into respiratory modulation of brain activity offer new perspectives on cognition and emotion. Biol. Psychol. 170, 108316 (2022).
Tort, A. B., Brankačk, J. & Draguhn, A. Respiration-entrained brain rhythms are global but often overlooked. Trends Neurosci. 41, 186–197 (2018).
Heck, D. H. et al. Breathing as a fundamental rhythm of brain function. Front. Neural Circuits. 10, 115 (2017).
Zelano, C. et al. Nasal respiration entrains human limbic oscillations and modulates cognitive function. J. Neurosci. 36, 12448–12467 (2016).
Grosmaitre, X., Santarelli, L. C., Tan, J., Luo, M. & Ma, M. Dual functions of mammalian olfactory sensory neurons as odor detectors and mechanical sensors. Nat. Neurosci. 10, 348–354 (2007).
Karalis, N. & Sirota, A. Breathing coordinates cortico-hippocampal dynamics in mice during offline states. Nat. Commun. 13, 467 (2022).
Folschweiller, S. & Sauer, J. F. Respiration-Driven Brain Oscillations in Emotional Cognition. Front. Neural Circuits. 15, 761812 (2021).
Herrero, J. L., Khuvis, S., Yeagle, E., Cerf, M. & Mehta, A. D. Breathing above the brain stem: volitional control and attentional modulation in humans. J. Neurophysiol. (2018).
Goheen, J., Anderson, J. A., Zhang, J. & Northoff, G. From lung to brain: respiration modulates neural and mental activity. Neurosci. Bull. 39, 1577–1590 (2023).
Mohammadi, S., Hossein-Zadeh, G. A. & Raoufy, M. R. Breathing mode selectively modulates brain-wide functional connectivity. PLoS One 20, e0334165 (2025).
Klimesch, W. The frequency architecture of brain and brain body oscillations: an analysis. Eur. J. Neurosci. 48, 2431–2453 (2018).
Vidaurre, D., Smith, S. M. & Woolrich, M. W. Brain network dynamics are hierarchically organized in time. Proc. Natl. Acad. Sci. 114, 12827–12832 (2017).
Allen, E. A. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663–676 (2014).
Lurie, D. J. et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw. Neurosci. 4, 30–69 (2020).
Hutchison, R. M. et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013).
Vidaurre, D. et al. Spectrally resolved fast transient brain states in electrophysiological data. Neuroimage 126, 81–95 (2016).
Baker, A. P. et al. Fast transient networks in spontaneous human brain activity. elife 3, e01867 (2014).
Geng, L. et al. Depression links to unstable resting-state brain dynamics: insights from hidden markov models and functional network variability. Psychol. Med. 55, e200 (2025).
Zhang, X. et al. Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach. Translational Psychiatry. 14, 507 (2024).
Kottaram, A. et al. Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network. Hum. Brain Mapp. 40, 2212–2228 (2019).
Zheng, X. et al. Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI study. Sci. Rep. 11, 12042 (2021).
Sterling, M. General health questionnaire–28 (GHQ-28). J. Physiother 57, 259 (2011).
Goldberg, D. P. & Hillier, V. F. A scaled version of the general health questionnaire. Psychol. Med. 9, 139–145 (1979).
Parkitny, L. & McAuley, J. The depression anxiety stress scale (DASS). J. Physiother. 56, 204 (2010).
Lovibond, P. F. & Lovibond, S. H. The structure of negative emotional states: Comparison of the depression anxiety stress scales (DASS) with the beck depression and anxiety inventories. Behav. Res. Ther. 33, 335–343 (1995).
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods. 16, 111–116 (2019).
Gorgolewski, K. et al. Nipype. Zenodo https://doi.org/10.5281/zenodo.596855 (2018).
Esteban, O. et al. fMRIPrep. Zenodo https://doi.org/10.5281/zenodo.852659 (2018).
Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform 5, 13 (2011).
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
Cox, R. W. & Hyde, J. S. Software tools for analysis and visualization of fMRI data. NMR Biomedicine: Int. J. Devoted Dev. Applic. Magn. Reson. Vivo 10, 171–178 (1997).
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63–72 (2009).
Avants, B. B., Epstein, C. L., Grossman, M. & Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008).
Nieto-Castanon, A. & Whitfield-Gabrieli, S. CONN functional connectivity toolbox: RRID SCR_009550, release 22. (2022).
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141 (2012).
Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. Statistical parametric mapping: the analysis of functional brain images (Elsevier, 2011).
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).
Qiu, Z. et al. Altered brain dynamics in chronic neck and shoulder pain revealed by hidden Markov model. Sci. Rep. 15, 18018 (2025).
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. (2011).
Tian, Y., Margulies, D. S., Breakspear, M. & Zalesky, A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat. Neurosci. 23, 1421–1432 (2020).
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex. 28, 3095–3114 (2018).
Bonkhoff, A. K. et al. Acute ischaemic stroke alters the brain’s preference for distinct dynamic connectivity states. Brain 143, 1525–1540 (2020).
Vidaurre, D. et al. Discovering dynamic brain networks from big data in rest and task. Neuroimage 180, 646–656 (2018).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).
Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).
E Newman, M. Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006).
Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One. 8, e68910 (2013).
González, J. et al. Breathing modulates gamma synchronization across species. Pflugers Arch. 475, 49–63 (2023).
Del Negro, C. A., Funk, G. D. & Feldman, J. L. Breathing matters. Nat. Rev. Neurosci. 19, 351–367 (2018).
Salimi, M. et al. Nasal airflow promotes default mode network activity. Respir Physiol. Neurobiol. 307, 103981 (2023).
Shine, J. M. et al. The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92, 544–554 (2016).
Salimi, M. et al. Nasal air puff promotes default mode network activity in mechanically ventilated comatose patients: a noninvasive brain stimulation approach. Neuromodulation 25, 1351–1363 (2022).
Zaccaro, A., Piarulli, A., Melosini, L., Menicucci, D. & Gemignani, A. Neural correlates of non-ordinary states of consciousness in pranayama practitioners: the role of slow nasal breathing. Front. Syst. Neurosci. 16, 803904 (2022).
Zhou, G. et al. Human hippocampal connectivity is stronger in olfaction than other sensory systems. Prog Neurobiol. 201, 102027 (2021).
Bayrak, Ö., Polastri, M. & Pehlivan, E. Effects of nasal and oral breathing on respiratory muscle and brain function: A review. Thorac. Res. Pract. 26, 145 (2025).
Kuroishi, R. C. S., Garcia, R. B., Valera, F. C. P., Anselmo-Lima, W. T. & Fukuda, M. T. H. Deficits in working memory, reading comprehension and arithmetic skills in children with mouth breathing syndrome: analytical cross-sectional study. Sao Paulo Med. J. 133, 78–83 (2014).
Ghazvineh, S., Mooziri, M., Salimi, A., Mirnajafi–Zadeh, J. & Raoufy, M. R. Olfactory epithelium electrical stimulation mitigates memory and synaptic deficits caused by mechanical ventilation. Sci. Rep. 15, 12197 (2025).
Ghazvineh, S. et al. Rhythmic air-puff into nasal cavity modulates activity across multiple brain areas: A non-invasive brain stimulation method to reduce ventilator-induced memory impairment. Respir Physiol. Neurobiol. 287, 103627 (2021).
Salimi, M. et al. Olfactory bulb stimulation mitigates Alzheimer’s-like disease progression. CNS Neurosci. Ther. 30, e70056 (2024).
Khodadadi, M. et al. Effect of low frequency stimulation of olfactory bulb on seizure severity, learning, and memory in kindled rats. Epilepsy Res. 188, 107055 (2022).
Bastani, M. et al. Deep brain stimulation of the olfactory bulb alleviates depressive-like behaviors and alters prefrontal cortex hippocampal coherence. Brain Res. 149902 (2025).
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The authors would like to acknowledge NBML for providing data acquisition facilities.
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MR.R. conceptualized the study. S.M. and GA.HZ. designed the study. S.M. performed the experiments, analyzed the data, and drafted the manuscript. GA.HZ. supervised the study. All authors discussed and interpreted the results, reviewed the manuscript, and approved the final version.
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Mohammadi, S., Hossein-Zadeh, GA. & Raoufy, M.R. Nasal and oral breathing modes reconfigure brain network dynamics between stabilizing integration and promoting fragmentation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43617-2
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DOI: https://doi.org/10.1038/s41598-026-43617-2