Dynamic brain-body coupling of breath-by-breath O2-CO2 exchange ratio with resting state cerebral hemodynamic fluctuations

Abstract

Background The origin of low frequency cerebral hemodynamic fluctuations (CHF) in the resting state remains unknown. Breath-by breath O 2-CO 2 exchange ratio (bER) has been reported to correlate with the cerebrovascular response to brief breath hold challenge at the frequency range of 0.008-0.03Hz in healthy adults. bER is defined as the ratio of the change in the partial pressure of oxygen (ΔPO 2) to that of carbon dioxide (ΔPCO 2) between end inspiration and end expiration. In this study, we aimed to investigate the contribution of respiratory gas exchange (RGE) metrics (bER, ΔPO 2 and ΔPCO 2) to low frequency CHF during spontaneous breathing. Methods Twenty-two healthy adults were included. We used transcranial Doppler sonography to evaluate CHF by measuring the changes in cerebral blood flow velocity (ΔCBFv) in bilateral middle cerebral arteries. The regional CHF were mapped with blood oxygenation level dependent (ΔBOLD) signal changes using functional magnetic resonance imaging. Temporal features and frequency characteristics of RGE metrics during spontaneous breathing were examined, and the simultaneous measurements of RGE metrics and CHF (ΔCBFv and ΔBOLD) were studied for their correlation. Results We found that the time courses of ΔPO 2 and ΔPCO 2 were interdependent but not redundant. The oscillations of RGE metrics were coherent with resting state CHF at the frequency range of 0.008-0.03Hz. Both bER and ΔPO 2 were superior to ΔPCO 2 in association with CHF while CHF could correlate more strongly with bER than with ΔPO 2 in some brain

Impaired Cerebrovascular Reactivity in Huntington’s Disease

“…Although we limited the mild hypercapnic condition to 4-8 mmHg above the subject's resting P ET CO 2 , it would be ideal to continuously measure and monitor the blood pressure changes while the subjects are under hypercapnic challenge which we did not incorporate in the current study. Variations stemming from gas delivery apparatus, design of the breathing circuit, or of the protocol, to the analysis of the imaging data in CVR MRI studies using externally administered CO 2 challenge have been reported previously (Fierstra et al, 2013;Liu et al, 2019;Chan et al, 2020a). These may be due in part to the level of the complexity of the set-up of the CO 2 challenge, varying tolerability in different subject or patient populations, variability in the physiological responses, in addition to differences in analytic approaches used to measure the response.…”

Applications of Advanced MRI to Disorders of Consciousness

“…First, though both fMRI and dMRI have advantages, a multimodal approach that combines the two, and synthesizes them with other conventional tools, may lend even greater insights into DoC. 27 51 52 68 84 85 Second, there is an opportunity to optimize how advanced MRI data are collected, as ongoing research explores techniques to improve resolution, 86 87 eliminate sources of physiologic noise, 88 89 90 91 92 93 and minimize the effects of confounders. 71 72 73 74 75 76 77 Third, the most effective way of analyzing and interpreting the data acquired with advanced MRI remains an active topic of investigation.…”

Correcting Cardiorespiratory Noise in Resting-state Functional MRI Data Acquired in Critically Ill Patients

“…We did not correct for spontaneous fluctuations below 0.125 Hz because many physiological fluctuations below 0.125 Hz are related to neuronal signaling, especially in critically ill patients. Fluctuations related to intracranial pressure (0.008-0.03Hz) (Lundberg, 1960), respiratory gas exchange (0.008-0.03Hz) (Chan et al, 2020;Lenfant, 1967), respiratory variation (~0.03Hz) (Birn et al, 2006;Chang et al, 2013), end-tidal carbon dioxide fluctuations (0-0.05Hz) (Wise et al, 2004), variation in arterial pressure (0.05-0.15Hz) (Mayer, 1876;Obrig et al, 2000), or heart rate variability (0.05-0.15Hz) (Chang et al, 2013) (Supplementary Figure 6) all occur at a similar frequency to spontaneous BOLD fluctuations. Among these fluctuations, respiratory gas exchange (Chan et al, 2020), respiratory variation (Birn et al, 2006), end-tidal carbon dioxide fluctuations (Wise et al, 2004), and heart rate variability (Chang et al, 2013) were previously found to be correlated with oscillations of the default mode network (DMN).…”

“…The peaks on the optical plethysmography time series serve as a surrogate of R peaks in electrocardiogram, while the peaks and troughs on the respiratory time series indicated end inspiration and end-expiration respectively (Supplementary Figure 1). Peaks and troughs on the time series of optical plethysmography and respiration were determined using a custom Matlab function (Chan et al, 2020) and corrected on the graphical user interface. The cardiac phase used in RETROICOR (Glover et al, 2000) advances linearly from 0 to 2π during each R-R interval and is reset to 0 for the next cycle.…”

“…In addition, cardiac pulsation and respiration signals have different frequencies and amplitude characteristics (Gray et al, 2009), and therefore may not be amenable to correction with a single bandpass filter. Notably, most studies that prospectively collected cardiorespiratory data enrolled healthy control subjects who had relatively stable hemodynamics, are able to tolerate long scan times, and were scanned in research settings with precise instrumentation (Chan et al, 2020;J. Lee et al, 2019).…”