ArticleNeuroImage2021
Reconstruction of respiratory variation signals from fMRI data.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Pupil size is linked to opposite signal fluctuations between cortical BOLD and ventricular CSF.NeuroImage · 2026Article
- A new fMRI quality metric using multi-echo information: Theory, validation and implications.bioRxiv : the preprint server for biology · 2026Article
- The Regulation of Vasomotor and Cardiorespiratory Pulsations Is Disrupted in Primary Central Nervous System Lymphoma: A Case-Control fMRI Study.Human brain mapping · 2026Article
- Improving respiratory and heart rate variation estimation from resting-state BOLD fMRI across the lifespan using a functionally informed, tissue-aware deep learning framework.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Temporal and spatial characterization of physiological noise in rs-fMRI at a high temporal resolution.Scientific reports · 2025Article
- Transformer-based deep learning model for predicting fNIRS short-channel signals.Neurophotonics · 2025Article
- Physiological Confounds in BOLD-fMRI and Their Correction.NMR in biomedicine · 2025Review
- Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.Magnetic resonance in medicine · 2025Article
- Reconstructing physiological signals from fMRI across the adult lifespan.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Vasomotor fluctuations are increased in primary central nervous system lymphoma: a case-control study with fast functional MRI.Brain communications · 2025Article
- Article
- Functional MRI signals exhibit stronger covariation with peripheral autonomic measures as vigilance decreases.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Detection of respiration-induced field modulations in fMRI: A concurrent and navigator-free approach.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI.NPJ digital medicine · 2023Article
- Comparing end-tidal CONeuroImage · 2023Article
- Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing.NeuroImage · 2023Article
- Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging.Frontiers in neuroimaging · 2023Article
- Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity.Brain structure & function · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.