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

Autor: Addeh A; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada., Vega F; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada., Morshedi A; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada., Williams RJ; Faculty of Health, Charles Darwin University, Casuarina, Australia., Pike GB; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.; Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada., MacDonald ME; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Jazyk: angličtina
Zdroj: Magnetic resonance in medicine [Magn Reson Med] 2024 Oct 31. Date of Electronic Publication: 2024 Oct 31.
DOI: 10.1002/mrm.30330
Abstrakt: 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.
Methods: In the proposed method, 1D convolutional neural networks (1D-CNNs) used BOLD signals and head motion parameters to reconstruct the RV waveform for the whole fMRI scan time. Resting-state fMRI data and associated respiratory records from the Human Connectome Project in Young Adults (HCP-YA) dataset are used to train and test the proposed method.
Results: Compared to using only BOLD-fMRI data for a CNN input, this approach yielded improvements of 14% in mean absolute error, 24% in mean square error, 14% in correlation, and 12% in dynamic time warping. When tested on independent datasets, the method demonstrated generalizability, even in data with different TRs and physiological conditions.
Conclusion: 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.
(© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
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