Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Golestani, Ali-Mohammad; Wei, Luxi; Chen, Jean

doi:10.1016/j.neuroimage.2016.05.025

Cited by 81 publications

(98 citation statements)

References 109 publications

(139 reference statements)

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“…The voxel-wise and component-based analysis of RSFA maps reveal brain regions with high vascular reactivity (Yezhuvath et al, 2009;Kannurpatti et al, 2011;Di et al, 2012;Kalcher et al, 2013;Liu et al, 2013;Mueller et al, 2013), and accord with previous studies of average and age-effects on RSFA (Lipp et al, 2015;Tsvetanov et al, 2015b;Golestani et al, 2016;P. Liu et al, 2017).…”

Section: Spatial Distribution and Age Effects On Rsfasupporting

confidence: 89%

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The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Tsvetanov

Henson

Jones

et al. 2019

Preprint

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Accurate identification of brain function is necessary to understand neurocognitive ageing, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is therefore essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health and neurovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and neurovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and neurovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and neurovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age-differences in RSFA, after controlling for cardiovascular health. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and neurovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterise neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

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Section: Spatial Distribution and Age Effects On Rsfasupporting

confidence: 89%

“…The BOLD signal variability in a resting state ("task-free") is one such estimate and is also known as resting state fluctuation amplitudes (RSFA). It has been proposed as a safe, scalable and robust cerebrovascular reactivity mapping technique (Kannurpatti and Biswal, 2008;Jahanian et al, 2014;Golestani et al, 2016;P. Liu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Tsvetanov

Henson

Jones

et al. 2019

Preprint

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“…Recently, two studies reported the use of EtCO 2 time course recorded during the resting-state BOLD scan as a regressor to map CVR in healthy volunteers (Golestani et al, 2016; Lipp et al, 2015). Compared to these previous studies, the present study is the first to apply these methodologies in patients with cerebrovascular diseases.…”

Section: Discussionmentioning

confidence: 99%

“…An exploratory analysis was also performed to compare the statistical significance (indicated by Z score) of voxel-wise regression using the filtered reference BOLD signal as regressor to that using EtCO 2 time course as regressor following a previous study (Golestani et al, 2016). Z scores from all brain voxels were considered.…”

Section: Methodsmentioning

confidence: 99%

Cerebrovascular reactivity mapping without gas challenges

et al. 2017

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Cerebrovascular reactivity (CVR), the ability of cerebral vessels to dilate or constrict, has been shown to provide valuable information in the diagnosis and treatment evaluation of patients with various cerebrovascular conditions. CVR mapping is typically performed using hypercapnic gas inhalation as a vasoactive challenge while collecting BOLD images, but the inherent need of gas inhalation and the associated apparatus setup present a practical obstacle in applying it in routine clinical use. Therefore, we aimed to develop a new method to map CVR using resting-state BOLD data without the need of gas inhalation. This approach exploits the natural variation in respiration and measures its influence on BOLD MRI signal. In this work, we first identified a surrogate of the arterial CO2 fluctuation during spontaneous breathing from the global BOLD signal. Second, we tested the feasibility and reproducibility of the proposed approach to use the above-mentioned surrogate as a regressor to estimate voxel-wise CVR. Third, we validated the “resting-state CVR map” with a conventional CVR map obtained with hypercapnic gas inhalation in healthy volunteers. Finally, we tested the utility of this new approach in detecting abnormal CVR in a group of patients with Moyamoya disease, and again validated the results using the conventional gas inhalation method. Our results showed that global BOLD signal fluctuation in the frequency range of 0.02–0.04 Hz contains the most prominent contribution from natural variation in arterial CO2. The CVR map calculated using this signal as a regressor is reproducible across runs (ICC=0.91±0.06), and manifests a strong spatial correlation with results measured with a conventional hypercapnia-based method in healthy subjects (r=0.88, p<0.001). We also found that resting-state CVR was able to identify vasodilatory deficit in patients with steno-occlusive disease, the spatial pattern of which matches that obtained using the conventional gas method (r=0.71±0.18). These results suggest that CVR obtained with resting-state BOLD may be a useful alternative in detecting vascular deficits in clinical applications when gas challenge is not feasible.

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“…Taken together, our dynamic brain-body interaction provides evidence that fluctuations related to RGE metrics are associated with resting state CHF at low frequency. No matter whether the contribution of bER fluctuations to resting state CHF is considered as 'physiological noise' similar to respiratory variability (Birn et al, 2006) and heart rate variability (Chang et al, 2013) to be removed, or as 'useful signals' for the evaluation of cerebrovascular reactivity (CVR) to the changes of systemic/endogenous gases during spontaneous breathing (Golestani et al, 2016;Liu et al, 2017b), bER may offer a better choice of regressor than Δ PCO 2 .…”

Section: Discussionmentioning

confidence: 99%

Dynamic Brain-Body Coupling of Breath-by-Breath O₂-CO₂Exchange Ratio with Resting State Cerebral Hemodynamic Fluctuations

Chan

Evans

Song

et al. 2019

Preprint

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Running headline: Dynamic brain-body coupling between resting state hemodynamics and O 2 -CO 2 exchange ratio 2 ABSTRACT The origin of low frequency cerebral hemodynamic fluctuations (CHF) in resting state remains unknown. Here we studied the contribution of respiratory gas exchange (RGE) metrics to CHF during spontaneous breathing. RGE metrics include the breath-by-breath changes of partial pressure of oxygen (ΔPO 2 ) and carbon dioxide (ΔPCO 2 ) between end inspiration and end expiration, and their ratio breath-by-breath O 2 -CO 2 exchange ratio (bER). We used transcranial Doppler sonography to evaluate CHF changes during spontaneous breathing by measuring the cerebral blood flow velocity (CBFv) in the middle cerebral arteries. The regional CHF changes during spontaneous breathing were mapped with blood oxygenation level dependent (BOLD) signal changes using functional magnetic resonance imaging (fMRI) technique. We found that prominent oscillations with periods of 0.5 to 2 minutes characterized Δ PO 2 , Δ PCO 2 and bER.The oscillations of bER were coherent with CHF during spontaneous breathing at the frequency range of 0.008-0.03Hz which is consistent with the low frequency resting state CHF. CHF had strong correlation with bER, followed by Δ PO 2 and then by Δ PCO 2 . Brain regions with the strongest bER-CHF coupling overlapped with many areas of default mode network. Although the physiological mechanisms underlying the strong correlation between bER and CHF are not completely understood, our findings suggest the contribution of bER to low frequency resting state CHF. It also provides a novel insight of brain-body interaction via CHF and oscillations of RGE metrics. 3 KEYWORDS Resting State Blood oxygenation level dependent Functional magnetic resonance imaging Breath-by-breath O 2 -CO 2 exchange ratio Cerebral blood flow velocity Transcranial Doppler sonography 1 0

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Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Cited by 81 publications

References 109 publications

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Cerebrovascular reactivity mapping without gas challenges

Dynamic Brain-Body Coupling of Breath-by-Breath O₂-CO₂Exchange Ratio with Resting State Cerebral Hemodynamic Fluctuations

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Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Cited by 81 publications

References 109 publications

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Cerebrovascular reactivity mapping without gas challenges

Dynamic Brain-Body Coupling of Breath-by-Breath O2-CO2Exchange Ratio with Resting State Cerebral Hemodynamic Fluctuations

Contact Info

Product

Resources

About

Dynamic Brain-Body Coupling of Breath-by-Breath O₂-CO₂Exchange Ratio with Resting State Cerebral Hemodynamic Fluctuations