Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters
<|reference_start|>Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters: Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices.   Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform.   Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals.   Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation.   Impact: It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.<|reference_end|>
@article{addeh2024machine,
    title={Machine Learning-based Estimation of Respiratory Fluctuations in a
  Healthy Adult Population using BOLD fMRI and Head Motion Parameters},
    author={Abdoljalil Addeh, Fernando Vega, Rebecca J. Williams, G. Bruce Pike,
  and M. Ethan MacDonald},
    journal={arXiv preprint arXiv:2405.00219},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00219},
    primaryClass={cs.LG}
}