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 O2-CO2 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 (ΔPO2) to that of carbon dioxide (ΔPCO2) between end inspiration and end expiration. In this study, we aimed to investigate the contribution of respiratory gas exchange (RGE) metrics (bER, ΔPO2 and ΔPCO2) 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 ΔPO2 and ΔPCO2 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 ΔPO2 were superior to ΔPCO2 in association with CHF while CHF could correlate more strongly with bER than with ΔPO2 in some brain regions. Brain regions with the strongest coupling between bER and ΔBOLD overlapped with many areas of default mode network including precuneus and posterior cingulate.

Conclusion: Although the physiological mechanisms underlying the strong correlation between bER and CHF are unclear, our findings suggest the contribution of bER to low frequency resting state CHF, providing a novel insight of brain-body interaction via CHF and oscillations of RGE metrics.

Fig 1. Correlation among breath-by-breath RGE metrics during spontaneous breathing.

(A) Correlations among breath-by breath RGE metrics (bER, ΔPO₂ and ΔPCO₂) in all subjects who participated in TCD sessions, and (B) those who participated in MRI sessions. Each gray circle represents the Pearson’s correlation coefficient from the correlation analysis of the time series of parameter pair shown on the x-axis for each subject. The thick middle horizontal line, the box and the vertical rod represent the mean, 95% confidence interval and standard deviation of the group data, respectively.

Fig 2. Correlation between the time series of ΔCBFv and RGE metrics during spontaneous breathing.

(A) Time series of ΔCBFv in right MCA of a representative subject at rest, without filtering and after applying low pass filter at 0.03Hz. (B) Time series of RGE metrics of bER, ΔPO₂, ΔPCO₂, P_ETO₂ and P_ETCO₂ acquired simultaneously with ΔCBFv of the same subject in (A). (C) Correlation between ΔCBFv without filtering and the RGE metrics (left), and that between ΔCBFv with low pass filtering at 0.03Hz and RGE metrics (right) of the same subject in (A). (D) For all 13 subjects who participated in TCD sessions, paired comparisons of Fisher's z scores between ΔCBFv in left MCA and bER, and those between ΔCBFv in left MCA and other RGE metrics besides bER (left), and the paired comparisons of Fisher's z scores between ΔCBFv in right MCA and bER, and those between ΔCBFv in right MCA and other RGE metrics besides bER (right). Each gray circle represents Fisher's z score from the correlation analysis of ΔCBFv and the parameter shown on the x-axis for each subject. The thick middle horizontal line, the box and the vertical rod represent the mean, 95% confidence interval and standard deviation of the group data, respectively.

Fig 3. Time-averaged coherence between time series of RGE metrics and CHF.

The mean time-averaged coherence in the frequency bandwidths from 0.008 to 0.25Hz (A) at the phase lag of 0±π/2, and (B) at the phase lag of π±π/2, were plotted (thick color lines). Color shaded areas represent standard error of the mean. Coherence between two time series at the phase lag of 0±π/2 indicates a positive correlation, while a negative correlation is represented by the coherence at the phase lag of π±π/2. Top panel shows the coherence of RGE metrics with ΔCBFv in LMCA and RMCA in TCD sessions (n = 13). The lower panel shows the coherence between RGE metrics and ΔBOLD in the inferior parietal lobule (IPL), posterior cingulate (PCC) and precuneus (PCun) within DMN in MRI sessions (n = 20).

Fig 4. Regional association between ΔBOLD and RGE metrics in the MRI sessions.

(A) Group maps showing the regional ΔBOLD per unit change of bER, ΔPO₂ and ΔPCO₂ (β_bER, β_ΔPO2 and β_ΔPCO2). (B) Group maps showing the regional percentage of voxels with significant ΔBOLD per unit change of bER, ΔPO₂ and ΔPCO₂ (voxel_βbER, voxelβ_ΔPO2 and voxelβ_ΔPCO2). (C) Paired comparisons of voxelβ maps. All the maps had been corrected for p_fdr<0.05.

Fig 5. Comparison of statistical parametric maps derived from regression and connectivity analyses.

(A) Group statistical parametric maps of the association between ΔBOLD and RGE metrics (bER, ΔPO₂ and ΔPCO₂). The white matter was excluded for the comparison with the group connectivity map. (B) Group connectivity map with the seed at left precuneus. ΔPCO₂ was demonstrated to be much weaker than bER and ΔPO₂ in its association with ΔBOLD in the brain regions of DMN. Comparing the top and bottom rows, similar regions of DMN were outlined with two independent methods of bER-CHF coupling and seed-based connectivity analysis. All the maps had been corrected for p_fdr<0.05.