Respiratory Rate and Head Motion Dynamics: A Frequency Analysis Across Life Stages Using HCP Dataset
Poster No:
2320
Submission Type:
Abstract Submission
Authors:
Abdoljalil Addeh1,2,3,4, Fernando Vega1,2,3,4, Karen Ardila1,2,3,4, Rebecca J Williams5, G. Bruce Pike4,6,3, M. Ethan MacDonald1,2,3,4
Institutions:
1Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, 2Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada, 3Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada, 4Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada, 5Faculty of Health, Charles Darwin University, Darwin, Australia, 6Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
First Author:
Abdoljalil Addeh
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|Calgary, Canada|Calgary, Canada|Calgary, Canada
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|Calgary, Canada|Calgary, Canada|Calgary, Canada
Co-Author(s):
Fernando Vega
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|Calgary, Canada|Calgary, Canada|Calgary, Canada
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|Calgary, Canada|Calgary, Canada|Calgary, Canada
Karen Ardila
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|Calgary, Canada|Calgary, Canada|Calgary, Canada
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|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
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|Calgary, Canada|Calgary, Canada|Calgary, Canada
Biomedical Engineering Graduate Program, University of Calgary|Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary|Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Alberta|Calgary, Canada|Calgary, Canada|Calgary, Canada
Introduction:
Recently, there has been a growing interest in developing effective solutions to correct the effects of confounds in fMRI data analysis. While these solutions have been effective for head motion (Maziero et al., 2020; Raimondo et al., 2023), correcting for physiological confounds remains a significant challenge. Interestingly, head motion is not merely a nuisance; when analyzed using retrospective image realignment algorithms, the resulting estimated head motion parameters may provide profound insights into a subject's respiration (Power et al., 2019).
Exploring the Human Connectome Project (HCP) dataset, our objective is to determine the interplay between respiratory rate and the frequency of head motion parameters across a range of age groups. Our primary hypothesis suggests a significant interaction between these variables. If validated, this could improve the correction fMRI data for respiration confounds.
Exploring the Human Connectome Project (HCP) dataset, our objective is to determine the interplay between respiratory rate and the frequency of head motion parameters across a range of age groups. Our primary hypothesis suggests a significant interaction between these variables. If validated, this could improve the correction fMRI data for respiration confounds.
Methods:
This study utilized 900 resting-state fMRI datasets and associated respiratory signals from three distinct fMRI datasets: the HCP in Development (HCP-D) spanning ages 5-21, HCP in Young Adults (HCP-YA) covering ages 21-35, and HCP in Aging (HCP-A) encompassing ages 36-100+, with each group contributing 300 samples to the study.
HCP-D and HCP-A imaging protocols, similar to each other, use an anterior → posterior and posterior → anterior phase encoding (PE) direction. In contrast, HCP-YA uses a left → right and right → left PE direction. fMRI images were corrected for head motion with FSL's MCFLIRT tool (Jenkinson et al., 2002).
To assess synchrony between respiratory patterns and head motions, we compared the primary frequency of head motion in the phase encoding (PE) direction with the corresponding respiratory signal's frequency. We employed a paired t-test and Mean Square Error (MSE) to quantify the similarity between these two frequencies.
HCP-D and HCP-A imaging protocols, similar to each other, use an anterior → posterior and posterior → anterior phase encoding (PE) direction. In contrast, HCP-YA uses a left → right and right → left PE direction. fMRI images were corrected for head motion with FSL's MCFLIRT tool (Jenkinson et al., 2002).
To assess synchrony between respiratory patterns and head motions, we compared the primary frequency of head motion in the phase encoding (PE) direction with the corresponding respiratory signal's frequency. We employed a paired t-test and Mean Square Error (MSE) to quantify the similarity between these two frequencies.
Results:
Fig 1 displays the power spectra derived from motion parameters. Notable oscillations around 0.3 Hz, especially in the PE direction, are evident. These oscillations can be attributed to both the physical movement of the head due to breathing and the pseudomotion artifact (Power et al., 2019). Fig 2 offers a visual representation of the relationship between respiratory and motion parameters in the PE direction, showcasing their synchronized variation. The primary respiratory frequencies identified from the HCP-D, HCP-YA, and HCP-A datasets were 0.324±0.083 Hz, 0.303±0.051 Hz, and 0.272±0.093 Hz, respectively. Meanwhile, the corresponding frequencies for head motion were 0.318±0.068 Hz, 0.293±0.063 Hz, and 0.263±0.071 Hz. These frequencies align with the standard breathing rates of each age group. The MSE differences across these age categories were 0.0015 Hz, 0.0012 Hz, and 0.0014 Hz (with percentage errors of 0.46%, 0.4%, and 0.51%, respectively). A paired t-test revealed no significant difference between the respiratory and head motion frequencies (p>0.01).
·Fig 1. Frequency domain representation of head motion parameters indicates that respiration causes actual and apparent head motion at a frequency of approximately 0.3 Hz, as denoted by the red arrows
·Fig 2. Illustration of respiratory influences on estimated head motion parameters. During periods of slow breathing, head motion parameters fluctuate at a lower frequency.
Conclusions:
The analysis emphasizes the close resemblance between the frequency of respiratory signals and head motion parameters, as shown by notably low MSE values between the two, and statistical analyses. This suggests that the head motion parameter, particularly in the PE direction, can act as a trustworthy measure of a person's respiratory rate. When reliable respiratory data is unavailable or compromised - a frequent issue in fMRI studies, especially among children and elderly participants (Addeh et al., 2023), as illustrated in Fig 1 - head motion parameters offer a viable substitute. Recent advancements have seen the emergence of machine learning (ML) techniques that harness fMRI data to reconstruct respiratory variations (Salas et al., 2021). By integrating both head motion parameters and fMRI data as inputs into these ML models, it is expected that the accuracy of the reconstructed respiratory variation signal can be enhanced, given the valuable insight into breathing rate offered by the head motion parameters.
Lifespan Development:
Lifespan Development Other 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
ADULTS
Aging
Development
FUNCTIONAL MRI
MRI
Other - Respiration
1|2Indicates the priority used for review
Provide references using author date format
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
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825-841. https://doi.org/https://doi.org/10.1006/nimg.2002.1132
Maziero, D., Rondinoni, C., Marins, T., Stenger, V. A., & Ernst, T. (2020). Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion. Neuroimage, 212, 116594. https://doi.org/https://doi.org/10.1016/j.neuroimage.2020.116594
Power, J. D., Lynch, C. J., Silver, B. M., Dubin, M. J., Martin, A., & Jones, R. M. (2019). Distinctions among real and apparent respiratory motions in human fMRI data. Neuroimage, 201, 116041. https://doi.org/10.1016/j.neuroimage.2019.116041
Raimondo, L., Priovoulos, N., Passarinho, C., Heij, J., Knapen, T., Dumoulin, S. O., Siero, J. C. W., & van der Zwaag, W. (2023). Robust high spatio-temporal line-scanning fMRI in humans at 7T using multi-echo readouts, denoising and prospective motion correction. Journal of Neuroscience Methods, 384, 109746. https://doi.org/https://doi.org/10.1016/j.jneumeth.2022.109746
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