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

Recording physiological signals during fMRI is valuable for multiple purposes but often requires additional setup, increasing complexity and participant discomfort. This is particularly challenging in simultaneous EEG-fMRI studies, which typically already include electrocardiogram (ECG) recordings. Here, we aim to leverage the known modulation of ECG by respiration to obtain an ECG-derived respiration (EDR) signal without extra equipment. We acquired EEG-fMRI data from 15 healthy subjects during resting state and two respiratory challenges (slow-paced breathing and breath-holding), with simultaneous ECG and respiratory recordings. Multiple methods were used to extract EDR signals, and the results were evaluated by comparing them with recorded respiration and assessing the quality of physiological regressors for denoising and cerebrovascular reactivity estimation. Amplitude-based EDR methods showed lower correlations with respiration, likely due to ECG distortion in the MRI. Nevertheless, coherence analysis showed that EDR preserved the relevant spectral content. EDR-based regressors were similar to those obtained from measured respiration. Notably, a method based on heart rate variability performed best overall, yielding physiological noise correction and reactivity estimates comparable to those using recorded respiration. Our results demonstrate that meaningful respiratory information can be extracted from ECG within the MRI environment, benefiting EEG-fMRI studies when respiration cannot be reliably recorded.

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Publication Details

Journal
Scientific Reports
Published
2025-11-11
DOI
https://doi.org/10.1038/s41598-025-23131-7
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Gina Caetano, Amparo Ruiz‐Tagle, Patrícia Figueiredo, Inês Esteves et al.
Scientific Reports
Functional Brain Connectivity Studies
article

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

Gina Caetano, Amparo Ruiz‐Tagle, Patrícia Figueiredo, Inês Esteves, Ana R. Fouto
article en

Abstract

Recording physiological signals during fMRI is valuable for multiple purposes but often requires additional setup, increasing complexity and participant discomfort. This is particularly challenging in simultaneous EEG-fMRI studies, which typically already include electrocardiogram (ECG) recordings. Here, we aim to leverage the known modulation of ECG by respiration to obtain an ECG-derived respiration (EDR) signal without extra equipment. We acquired EEG-fMRI data from 15 healthy subjects during resting state and two respiratory challenges (slow-paced breathing and breath-holding), with simultaneous ECG and respiratory recordings. Multiple methods were used to extract EDR signals, and the results were evaluated by comparing them with recorded respiration and assessing the quality of physiological regressors for denoising and cerebrovascular reactivity estimation. Amplitude-based EDR methods showed lower correlations with respiration, likely due to ECG distortion in the MRI. Nevertheless, coherence analysis showed that EDR preserved the relevant spectral content. EDR-based regressors were similar to those obtained from measured respiration. Notably, a method based on heart rate variability performed best overall, yielding physiological noise correction and reactivity estimates comparable to those using recorded respiration. Our results demonstrate that meaningful respiratory information can be extracted from ECG within the MRI environment, benefiting EEG-fMRI studies when respiration cannot be reliably recorded.

Scientific ReportsVol. 15(1)
University of Lisbon (PT), Universidade Politécnica de Lisboa (PT), Universidade Europeia (PT), Algarve Biomedical Center (PT), University of Algarve (PT)
Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 66%
Functional Brain Connectivity Studies
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