Cerebrovascular reactivity (CVR), an index of the brain’s vascular response, has been shown to provide useful information in diagnosing and treating patients with different brain pathologies, as well as mapping the neurovascular effects associated with normal aging. Commonly, CVR mapping is performed using hypercapnic (HC) gas inhalation during functional MRI (termed HC-CVR), but the cost and complexity associated with using the inhalation equipment, as well as the inability of some patients to tolerate the protocol, has presented a major hindrance to the wide-scale application of CVR across MRI centres and hospitals. To circumvent these practical challenges, recent studies have used functional MRI data easily collected during natural breathing to map CVR and have shown that many aspects of CVR can be reliably estimated when the subject is simply laying at rest (called resting-state CVR or RS-CVR). The overarching goal of the current proposal is to compare different established RS-CVR metrics, as well as explore new RS-CVR metrics that can also be used to assay HC-CVR. To provide a practical test of these different metrics, we assess their ability, via a machine learning model, to predict subject brain age using publicly available data sets. Together, this approach promises to provide new data and knowledge that will have practical benefit for both the academic community and health care industry. Indeed, due to the simplicity of this approach and its inexpensive implementation, RS-CVR has the potential to serve as a much more wide-spread clinical and research tool. This project will occur over an approximately 3-year time period.
