Using ECG-derived respiration for explaining BOLD-fMRI fluctuations during rest and respiratory modulations

Area of Science:

• Neuroimaging
• Physiological Monitoring
• Biomedical Engineering

Background:

• Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are powerful tools for neuroscience research.
• Recording physiological signals like respiration during fMRI is crucial but complicates setup and participant comfort.
• Electrocardiogram (ECG) is often recorded in EEG-fMRI studies, offering a potential source for respiration data.

Purpose of the Study:

• To investigate the feasibility of deriving respiratory signals from ECG recordings within an fMRI environment.
• To evaluate different methods for extracting ECG-derived respiration (EDR) signals.
• To assess the utility of EDR signals for physiological noise correction and cerebrovascular reactivity estimation in EEG-fMRI.

Main Methods:

• Acquired simultaneous EEG, fMRI, ECG, and respiratory data from 15 healthy subjects.
• Applied multiple algorithms to extract EDR signals from ECG data.
• Compared EDR signals with simultaneously recorded respiration data.
• Evaluated EDR-derived regressors for fMRI denoising and cerebrovascular reactivity estimation.

Main Results:

• Amplitude-based EDR methods showed reduced correlation with respiration, likely due to MRI-induced ECG distortion.
• Coherence analysis confirmed that EDR signals retained relevant spectral information.
• EDR-based regressors were comparable to those derived from measured respiration.
• A heart rate variability-based EDR method yielded the best overall performance for noise correction and reactivity estimation.

Conclusions:

• Meaningful respiratory information can be extracted from ECG signals within the MRI environment.
• EDR offers a practical alternative for respiration monitoring in EEG-fMRI studies when direct recording is challenging.
• This approach can improve physiological noise correction and data analysis in fMRI studies without additional hardware.