Abstract
Asthma involves not only airway inflammation but also aberrant central nervous system regulation. While static functional connectivity studies have revealed brain network abnormalities in asthma patients, the transient temporal dynamics of brain functional states remain largely unexplored. To investigate brain dynamic functional connectivity characteristics in asthma patients using Hidden Markov Models (HMM) and to identify potential neurobiological markers associated with clinical symptoms. Resting-state fMRI data were acquired from an initial pool of participants, with 120 age- and gender-matched individuals (60 asthma patients and 60 healthy controls) included after stringent quality control and head-motion scrubbing. HMM was applied to identify recurring brain states based on the Schaefer-142 parcellation. We compared groups on fractional occupancy (FO), mean dwell time (MDT), and transition probabilities. Exploratory correlation analyses were performed to evaluate the relationship between HMM-derived metrics and clinical scores (ACT and pulmonary function). HMM identified nine distinct functional states. Asthma patients exhibited a significantly increased MDT and FO in State 2 (characterized by somatomotor and dorsal attention network involvement) compared to healthy controls (p < 0.05). Exploratory analysis revealed a nominal positive correlation between the MDT of State 2 and Asthma Control Test (ACT) scores (r = 0.30, p < 0.05, uncorrected), suggesting a potential compensatory role of this state in symptom monitoring. Our findings reveal altered brain state dynamics in asthma, particularly the prolonged occupancy in a sensory-attention-related state. While the brain-clinical associations are exploratory, these dynamic metrics provide novel insights into the central mechanisms of asthma and may serve as preliminary neurobiological markers for symptom control.
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References
Wang, T., Huang, X. & Wang, J. Asthma’s effect on brain connectivity and cognitive decline. Front. Neurol. 13, 1065942 (2023).
Li, S. et al. Cerebral regional and network characteristics in asthma patients: A resting-state fMRI study. Front. Med. 14(6), 792–801 (2020).
Rosenkranz, M. A. & Davidson, R. J. Affective neural circuitry and mind-body influences in asthma. Neuroimage 47(3), 972–980 (2009).
Uzunoğlu, B. et al. Sensory processing disorder could be a comorbidity of childhood asthma: A cross-sectional study. Pediatr. Pulmonol. 60(7), e71203 (2025).
Wang, Y. et al. Brain response in asthma: The role of “lung-brain” axis mediated by neuroimmune crosstalk. Front. Immunol. 14, 1240248 (2023).
Zhang, Y. et al. Coupling of spatial and directional functional network connectivity reveals a physiological basis for salience network hubs in asthma. Brain Imag. Behav. 16(1), 176–185 (2022).
Zhang, Y. et al. Abnormal functional connectivity of ventral anterior insula in asthmatic patients with depression. Neural Plast. 2017, 7838035 (2017).
Preti, M. G., Bolton, T. A. & Van De Ville, D. The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage 160, 41–54 (2017).
Calhoun, V. D. et al. The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84(2), 262–274 (2014).
Baker, A. P. et al. Fast transient networks in spontaneous human brain activity. Elife 3, e1867 (2014).
Finnegan, S. L. et al. Brain activity measured by functional brain imaging predicts breathlessness improvement during pulmonary rehabilitation. Thorax 78(9), 852–859 (2023).
Vidaurre, D., Smith, S. M. & Woolrich, M. W. Brain network dynamics are hierarchically organized in time. Proc. Natl. Acad. Sci. U.S.A. 114(48), 12827–12832 (2017).
Hutchison, R. M. et al. Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage 80, 360–378 (2013).
Quinn, A. J. et al. Task-evoked dynamic network analysis through hidden Markov modeling. Front. Neurosci. 12, 603 (2018).
Voytek, B. & Knight, R. T. Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol. Psychiat. 77(12), 1089–1097 (2015).
Zhang, S. et al. Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder. Eur. Arch. Psychiatry Clin. Neurosci. 272(8), 1547–1557 (2022).
Kottaram, A. et al. Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network. Hum. Brain Mapp. 40(7), 2212–2228 (2019).
Guo, T. et al. The lung-brain axis: Genetic evidence for causal effects of asthma, allergic rhinitis, and chronic rhinosinusitis on brain structure. Int. Arch. Allergy Immunol. 186(7), 619–630 (2025).
Vidaurre, D. et al. Discovering dynamic brain networks from big data in rest and task. Neuroimage 180(Pt B), 646–656 (2018).
Zhang, Y. et al. Asthma-specific temporal variability reveals the effect of group cognitive behavior therapy in asthmatic patients. Front. Neurol. 12, 615820 (2021).
Bateman, E. D. et al. Global strategy for asthma management and prevention: GINA executive summary. Eur. Respir. J. 31(1), 143–178 (2008).
Yan, C. et al. DPABI: Data processing & analysis for (Resting-State) brain imaging. Neuroinformatics 14(3), 339–351 (2016).
Dosenbach, N. U. et al. Prediction of individual brain maturity using fMRI. Science 329(5997), 1358–1361 (2010).
Zhan, K. et al. Altered static and dynamic functional network connectivity and combined machine learning in asthma. Neuroscience 576, 223–233 (2025).
Zhang, Z. et al. Neuroimaging insights into lung disease-related brain changes: From structure to function. Front. Aging Neurosci. 17, 1550319 (2025).
Lurie, D. J. et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw. Neurosci. (Cambridge, Mass.) 4(1), 30–69 (2020).
Ritz, T. et al. Central nervous system signatures of affect in asthma: Associations with emotion-induced bronchoconstriction, airway inflammation, and asthma control. J. Appl. Physiol. 126(6), 1725–1736 (2019).
von Leupoldt, A. et al. Down-regulation of insular cortex responses to dyspnea and pain in asthma. Am. J. Respir. Crit. Care Med. 180(3), 232–238 (2009).
Rosenkranz, M. A. et al. Neural circuitry underlying the interaction between emotion and asthma symptom exacerbation. Proc. Natl. Acad. Sci. U.S.A. 102(37), 13319–13324 (2005).
Rajan, A. et al. The microstructure of attentional control in the dorsal attention network. J. Cogn. Neurosci. 33(6), 965–983 (2021).
Vossel, S., Geng, J. J. & Fink, G. R. Dorsal and ventral attention systems: Distinct neural circuits but collaborative roles. Neuroscientist 20(2), 150–159 (2014).
Chan, P. S., Lee, L. & Davenport, P. W. Neural mechanisms of respiratory interoception. Auton. Neurosci. Basic Clin. 253, 103181 (2024).
Chen, W. G. et al. The emerging science of interoception: Sensing, integrating, interpreting, and regulating signals within the self. Trends. Neurosci. 44(1), 3–16 (2021).
Chan, P. S. et al. Cortical and subcortical neural correlates for respiratory sensation in response to transient inspiratory occlusions in humans. Front. Physiol. 9, 1804 (1804).
Benjamini, Y. et al. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125(1–2), 279–284 (2001).
Harrison, O. K. et al. Dissociating breathlessness symptoms from mood in asthma. Biol. Psychol. 165, 108193 (2021).
Dixon, M. L. et al. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc. Natl. Acad. Sci. U.S.A. 115(7), E1598–E1607 (2018).
Sheline, Y. I. et al. The default mode network and self-referential processes in depression. Proc. Natl. Acad. Sci. U. S. A. 106(6), 1942–1947 (2009).
Spreng, R. N. et al. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage 53(1), 303–317 (2010).
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3(3), 201–215 (2002).
Kim, S. M. et al. Brain functional connectivity in patients with somatic symptom disorder. Psychosom. Med. 81(3), 313–318 (2019).
Kim, J. et al. Altered attentional control over the salience network in complex regional pain syndrome. Sci. Rep. 8(1), 7466 (2018).
Otti, A. et al. Frequency shifts in the anterior default mode network and the salience network in chronic pain disorder. BMC Psychiatry 13, 84 (2013).
Ou, J. et al. Characterizing and differentiating brain state dynamics via hidden Markov models. Brain Topogr. 28(5), 666–679 (2015).
Kang, J., Pae, C. & Park, H. Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex. PLoS ONE 14(9), e222161 (2019).
Liu, J., Malesevic, N., & Antfolk, C. (2023). Long term sustained attention alters dynamic functional connectivity patterns. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1-4). IEEE.
Irani, F. et al. Is asthma associated with cognitive impairments? A meta-analytic review. J. Clin. Exp. Neuropsychol. 39(10), 965–978 (2017).
Ji, R. et al. Neuroinflammation and central sensitization in chronic and widespread pain. Anesthesiology 129(2), 343–366 (2018).
Menon, V. & Uddin, L. Q. Saliency, switching, attention and control: A network model of insula function. Brain Struct. Funct. 214(5–6), 655–667 (2010).
Napadow, V. et al. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis Rheum. 62(8), 2545–2555 (2010).
Luettich, A. et al. Functional connectivity between interoceptive brain regions is associated with distinct health-related domains: A population-based neuroimaging study. Hum. Brain Mapp. 44(8), 3210–3221 (2023).
Jahn, P. et al. Intrinsic network activity reflects the ongoing experience of chronic pain. Sci. Rep. 11(1), 21870 (2021).
Park, S. Y. et al. Clinical factors affecting discrepant correlation between asthma control test score and pulmonary function. Allergy, Asthma Immunol. Res. 7(1), 83–87 (2015).
Steele, A. M. et al. Discrepancies between lung function and asthma control: Asthma perception and association with demographics and anxiety. Allergy Asthma Proc. 33(6), 500–507 (2012).
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This study was supported by grants from : National Natural Science Foundation of China (Grant No. 82205267).
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Chunyang Xu performed MRI data processing. Xiangyu Wei prepared the tables and figures and wrote the manuscript. Xiangyu Wei assisted in participant recruitment. Xiangyu Wei provided financial support for this study. All authors contributed to data interpretation and manuscript revision.
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This study was approved by the Ethics Committee of Shuguang Hospital, Shanghai University of Traditional Chinese Medicine (Approval No.: 2023-1325-92-01).
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Xu, C., Wei, X. Resting-state fMRI using hidden Markov models reveals abnormal dynamic brain functional states in asthma. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44794-w
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DOI: https://doi.org/10.1038/s41598-026-44794-w
