![]() The vascular response consists of both a blood flow and a blood volume response, but because blood flow has a supralinear dependence on vessel diameter (modeled using Poiseuille’s law), it is the blood flow response that dominates the BOLD response. It has been estimated that in healthy brains, the vascular response component is about twice the amplitude of the metabolic response ( Hoge et al., 1999a Uludağ et al., 2004). These biases can be vascular in nature, such as differences in baseline blood flow or reactivity, which are known to be prevalent in aging and clinical populations, or maybe due to decline in the availability of neuronal resources. While BOLD-based fMRI is widely used and has several applications in clinical fields ( Chen, 2018, 2019 Gauthier and Fan, 2019 Specht, 2019), its physiologically unspecific nature makes it vulnerable to a variety of biases, especially in clinical populations ( Ances et al., 2011 De Vis et al., 2015 Lajoie et al., 2017 Mazerolle et al., 2018 Chen, 2019). It is this last aspect that underlies the amplitude of the cerebrovascular reactivity (CVR) response measured by BOLD fMRI. Therefore, rather than being a direct marker of neuronal activity, the BOLD signal reflects the relative interplay between baseline oxidative metabolism, task-evoked metabolism, neurovascular coupling mechanisms, and the extent to which local vessels dilate in response to these neurovascular coupling chemical signals ( Gauthier and Fan, 2019). The signal measured during a task is due to the dilution of these two sources of dHb from a feedforward cascade of events leading to vasodilation in arterioles, bringing fully oxygenated, and therefore diamagnetic, blood to the area of activity ( Girouard and Iadecola, 2006 Iadecola, 2017). This lack of specificity stems from the fact that most deoxyhemoglobin (dHb) locally arises from baseline metabolism, with a more modest contribution from task-evoked neuronal activity. This signal is based on the paramagnetic properties of deoxyhemoglobin, providing a sensitive, but un-specific marker of neuronal activity. This review addresses all of these aspects in which CVR interacts with fMRI and the role of CVR in calibrated fMRI, provides an overview of the physiological biases and assumptions underlying hypercapnia-based CVR and calibrated fMRI, and provides a view into the future of non-invasive CVR measurement.įunctional MRI (fMRI) is predominantly performed using the blood-oxygenation level-dependent (BOLD) signal. Furthermore, due to the obvious challenges in estimating CVR using gas challenges, a rapidly growing field of study is the estimation of CVR without any form of challenge, including the use of resting-state fMRI for that purpose. Moreover, CVR has recently been shown to be a major source of vascular bias in computing resting-state functional connectivity, in much the same way that it is used to neutralize the vascular contribution in calibrated fMRI. In particular, CVR estimation is part of a family of techniques called calibrated BOLD fMRI, the purpose of which is to allow the mapping of cerebral oxidative metabolism (CMRO2) using a combination of BOLD and cerebral-blood flow (CBF) measurements. The estimation of CVR has unique applications in and associations with fMRI. This technique is finding an ever-increasing role in neuroscience and clinical research as well as treatment planning. Task and resting-state functional MRI (fMRI) is primarily based on the same blood-oxygenation level-dependent (BOLD) phenomenon that MRI-based cerebrovascular reactivity (CVR) mapping has most commonly relied upon. 4Montreal Heart Institute, Montreal, QC, Canada.3Department of Physics, Concordia University, Montreal, QC, Canada.2Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.1Baycrest Centre for Geriatric Care, Rotman Research Institute, Toronto, ON, Canada.
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