Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

Journal: Magnetic resonance in medicine
PMID: 39481033

Abstract

PURPOSE: External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters.

Authors

  • Abdoljalil Addeh
    Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran.
  • Fernando Vega
    Cancer Therapy, Siemens Healthineers, Forchheim, Germany.
  • Amin Morshedi
    Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.
  • Rebecca J Williams
    Department of Radiology, Cumming School of Medicine, University of Calgary, Canada; Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Canada; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Canada.
  • G Bruce Pike
    Departments of Radiology and Clinical Neurosciences, University of Calgary, Calgary, Canada.
  • M Ethan MacDonald
    Department of Radiology, University of Calgary, Calgary, AB, Canada.

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