Machine learning‐based estimation of respiratory fluctuations in a healthy adult population using resting state <scp>BOLD fMRI</scp> and head motion parameters
This study shows that the respiratory variations could be reconstructed from BOLD-fMRI data in the young adult population, and its accuracy could be improved using supportive data such as head motion parameters. The method also performed well on independent datasets with different experimental conditions.
Authors
- M. Ethan MacDonald (ORCID: https://orcid.org/0000-0001-5421-3536)
- Abdoljalil Addeh (ORCID: https://orcid.org/0000-0003-2727-4557)
- G. Bruce Pike (ORCID: https://orcid.org/0000-0001-8924-683X)
- Rebecca J. Williams (ORCID: https://orcid.org/0000-0002-8949-1197)
- Fernando Vega (ORCID: https://orcid.org/0000-0003-0013-8133)
- A Morshedi
Institutions
- University of Calgary (CA)
- Charles Darwin University (AU)
- Hotchkiss Brain Institute (CA)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2024-10-31
- DOI
- https://doi.org/10.1002/mrm.30330
- Citations
- 3
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.03
Funders
- University of Calgary
- Alberta Innovates
- Cumming School of Medicine, University of Calgary
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada