Impact of Variable Breathing on Functional Connectivity in rs-fMRI: An Observational Study
Poster No:
1895
Submission Type:
Abstract Submission
Authors:
Thalita do Nascimento1, Abdoljalil Addeh2,3,4,5, Karen Ardila2,3,4,5, Rebecca J Williams6, G. Bruce Pike4,7,5, M. Ethan MacDonald2,3,4,5
Institutions:
1Department of Informatics, Federal University of Paraná, Brazil, Curitiba, Brazil, 2Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada, 3Department of Electrical & Software Engineering, Schulich School of Engineering, Calgary, Canada, 4Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada, 5Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada, 6Brain-Behaviour Research Group, University of New England, Armidale, Australia, 7Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
First Author:
Thalita do Nascimento
Department of Informatics, Federal University of Paraná, Brazil
Curitiba, Brazil
Department of Informatics, Federal University of Paraná, Brazil
Curitiba, Brazil
Co-Author(s):
Abdoljalil Addeh
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering|Department of Radiology, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering|Department of Radiology, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
Karen Ardila
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering|Department of Radiology, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering|Department of Radiology, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
G. Bruce Pike
Department of Radiology, Cumming School of Medicine, University of Calgary|Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada
Department of Radiology, Cumming School of Medicine, University of Calgary|Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada
M. Ethan MacDonald
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering|Department of Radiology, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering|Department of Radiology, Cumming School of Medicine, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
Introduction:
Resting-state fMRI studies often instruct participants to maintain steady breathing; however, unavoidable respiratory variations remain a concern (Addeh et al., 2023; Addeh et al., 2024).
This study investigates how variable breathing patterns influence functional connectivity (FC) estimates. Specifically, we hypothesize that irregular breathing patterns will introduce greater FC variability, as quantified by root-mean-square deviation (RMSD) (Birn et al., 2013), compared to cases with stable breathing. By analyzing data from the Human Connectome Project in Young Adults (HCP-YA) (Van Essen et al., 2013), which includes respiratory and functional imaging data across multiple sessions, we aim to provide robust evidence for the influence of respiratory fluctuations on FC variability.
This study investigates how variable breathing patterns influence functional connectivity (FC) estimates. Specifically, we hypothesize that irregular breathing patterns will introduce greater FC variability, as quantified by root-mean-square deviation (RMSD) (Birn et al., 2013), compared to cases with stable breathing. By analyzing data from the Human Connectome Project in Young Adults (HCP-YA) (Van Essen et al., 2013), which includes respiratory and functional imaging data across multiple sessions, we aim to provide robust evidence for the influence of respiratory fluctuations on FC variability.
Methods:
This study used resting-state fMRI and respiratory data from HCP-YA, comprising healthy participants aged 22–35 years. Each subject underwent four fMRI sessions: two on Day 1 and two on Day 2.
Subjects were selected based on the availability of high-quality respiratory data and specific study criteria. Of 1200 subjects, 50 subjects were included:
• Group A (n = 25): Stable breathing across all sessions.
• Group B (n = 25): Irregular or deep breaths in at least one session.
fMRI preprocessing included distortion correction, motion correction, and registration to 2 mm MNI space. FC matrices were computed by correlating BOLD time series between 518 ROIs (Peng et al., 2023).
To quantify FC variability, we calculated Root-Mean-Square Deviation (RMSD) across sessions, and compared between groups using two-sample t-tests (p < 0.001). Additionally, an example of ROI-to-ROI correlation standard deviation (SD) was presented in the results to illustrate the impact of variable breathing on connectivity variability.
Subjects were selected based on the availability of high-quality respiratory data and specific study criteria. Of 1200 subjects, 50 subjects were included:
• Group A (n = 25): Stable breathing across all sessions.
• Group B (n = 25): Irregular or deep breaths in at least one session.
fMRI preprocessing included distortion correction, motion correction, and registration to 2 mm MNI space. FC matrices were computed by correlating BOLD time series between 518 ROIs (Peng et al., 2023).
To quantify FC variability, we calculated Root-Mean-Square Deviation (RMSD) across sessions, and compared between groups using two-sample t-tests (p < 0.001). Additionally, an example of ROI-to-ROI correlation standard deviation (SD) was presented in the results to illustrate the impact of variable breathing on connectivity variability.
Results:
Figure 1 shows respiratory patterns for two representative subjects. Group A maintained consistent breathing across sessions (Figure 1A), while Group B exhibited variable patterns, including deep breaths and breath-holding events (Figure 1B).
The RMSD analysis revealed significant differences between the two groups, as shown in the top panel of Figure 2. Group A, characterized by stable breathing patterns, exhibited low RMSD values across sessions, indicating consistent FC. On the other hand, Group B, with variable breathing patterns, demonstrated significantly higher RMSD values in both intrasession and intersession comparisons (p < 0.001). These findings reflect the pronounced impact of irregular breathing on FC variability.
To further illustrate the effect of variable breathing patterns, an example of ROI-to-ROI correlation SD is presented in the bottom panel of Figure 2. The subject from Group A demonstrates low correlation SD across sessions, consistent with stable connectivity. In contrast, the subject from Group B shows higher SD, particularly during sessions with variable breathing patterns. This increased variability in FC strength highlights the influence of deep or irregular breaths on the BOLD signal.
The RMSD analysis revealed significant differences between the two groups, as shown in the top panel of Figure 2. Group A, characterized by stable breathing patterns, exhibited low RMSD values across sessions, indicating consistent FC. On the other hand, Group B, with variable breathing patterns, demonstrated significantly higher RMSD values in both intrasession and intersession comparisons (p < 0.001). These findings reflect the pronounced impact of irregular breathing on FC variability.
To further illustrate the effect of variable breathing patterns, an example of ROI-to-ROI correlation SD is presented in the bottom panel of Figure 2. The subject from Group A demonstrates low correlation SD across sessions, consistent with stable connectivity. In contrast, the subject from Group B shows higher SD, particularly during sessions with variable breathing patterns. This increased variability in FC strength highlights the influence of deep or irregular breaths on the BOLD signal.
·Fig 1. Respiratory data from two HCP-YA subjects. (A) Group A: consistent breathing with stable rate and depth. (B) Group B: variable breathing with deep breaths and breath-holding
·Fig 2. FC variability. Top: Group A shows lower RMSD, while Group B has higher values (p < 0.001). Bottom: Group B shows greater ROI-to-ROI correlation SD, indicating higher FC variability
Conclusions:
This study highlights the impact of variable respiratory patterns on FC variability in resting-state fMRI, as quantified by RMSD. These findings emphasize the importance of accounting for respiratory fluctuations when analyzing FC data, particularly in studies with long scanning sessions or involving populations more prone to physiological variability, such as children or older adults.
Importantly, we did not apply any physiological confound correction in our analyses; the results presented here are based on direct observations of the uncorrected data. Future studies should incorporate established physiological confound correction methods, such as RETROspective Image CORrection (RetroICor), or advanced nuisance regression techniques, to better separate true neuronal contributions from respiratory-induced artifacts. A comparative analysis of corrected and uncorrected data would provide more robust insights into the extent of these effects.
Importantly, we did not apply any physiological confound correction in our analyses; the results presented here are based on direct observations of the uncorrected data. Future studies should incorporate established physiological confound correction methods, such as RETROspective Image CORrection (RetroICor), or advanced nuisance regression techniques, to better separate true neuronal contributions from respiratory-induced artifacts. A comparative analysis of corrected and uncorrected data would provide more robust insights into the extent of these effects.
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other 2
Keywords:
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Addeh, A., Vega, F., Medi, P. R., Williams, R. J., Pike, G. B., & MacDonald, M. E. (2023). Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population. NeuroImage, 269, 119904. https://doi.org/https://doi.org/10.1016/j.neuroimage.2023.119904
Addeh, A., Vega, F., Morshedi, A., Williams, R. J., Pike, G. B., & MacDonald, M. E. (2024). Machine learning‐based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters. Magnetic Resonance in Medicine, 1-15.
Birn, R. M., Molloy, E. K., Patriat, R., Parker, T., Meier, T. B., Kirk, G. R., Nair, V. A., Meyerand, M. E., & Prabhakaran, V. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage, 83, 550-558.
Peng, L., Luo, Z., Zeng, L.-L., Hou, C., Shen, H., Zhou, Z., & Hu, D. (2023). Parcellating the human brain using resting-state dynamic functional connectivity. Cerebral Cortex, 33(7), 3575-3590.
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & Consortium, W.-M. H. (2013). The WU-Minn human connectome project: an overview. NeuroImage, 80, 62-79.