Dataset Information

Reconstruction of respiratory variation signals from fMRI data.

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.

SUBMITTER: Salas JA

PROVIDER: S-EPMC7868104 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Reconstruction of respiratory variation signals from fMRI data.

Salas Jorge A JA Bayrak Roza G RG Huo Yuankai Y Chang Catie C

NeuroImage 20201028

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 phy ...[more]

PMID: 33129927

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Project description:One of the main obstacles in quantitative interpretation of functional magnetic resonance imaging (fMRI) signal is that this signal is influenced by non-neural factors such as vascular properties of the brain, which effectively increases signal variability. One approach to account for non-neural components is to identify and measure these confounding factors and to include them as covariates in data analysis or interpretation. Previously, several research groups have independently identified four potential physiologic modulators of fMRI signals, including baseline venous oxygenation (Yv ), cerebrovascular reactivity (CVR), resting state BOLD fluctuation amplitude (RSFA), and baseline cerebral blood flow (CBF). This study sought to directly compare the modulation effects of these indices in the same fMRI session. The physiologic parameters were measured with techniques comparable with those used in the previous studies except for CBF, which was determined globally with a velocity-based phase-contrast MRI (instead of arterial-spin-labeling MRI). Using an event-related, scene-categorization fMRI task, we showed that the fMRI signal amplitude was positively correlated with CVR (P < 0.0001) and RSFA (P = 0.002), while negatively correlated with baseline Yv (P < 0.0001). The fMRI-CBF correlation did not reach significance, although the (negative) sign of the correlation was consistent with the earlier study. Furthermore, among the physiologic modulators themselves, significant correlations were observed between baseline Yv and baseline CBF (P = 0.01), and between CVR and RSFA (P = 0.05), suggesting that some of the modulators may partly be of similar physiologic origins. These observations as well as findings in recent literature suggest that additional measurement of physiologic modulator(s) in an fMRI session may provide a practical approach to control for inter-subject variations and to improve the ability of fMRI in detecting disease or medication related differences. Hum Brain Mapp 34:2078-2088, 2013. © 2011 Wiley Periodicals, Inc.

Dataset Information

Reconstruction of respiratory variation signals from fMRI data.

Publications

Reconstruction of respiratory variation signals from fMRI data.

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