A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function

Available online 24 June 2021.Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO 2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemo- dynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural ac- tivity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting- state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO 2 pressure: a breath-hold task to induce hypercapnia (CO 2 increase) and a cued deep breathing task to induce hypocapnia (CO 2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO 2 by systematically shifting the CO 2 regressor in time to optimize the model fit. This optimization inher- ently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO 2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemody- namic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.This research was supported by the Eunice Kennedy Shriver Na- tional Institute of Child Health and Human Development of the Na- tional Institutes of Health under award number K12HD073945. The pediatric dataset and cerebral palsy dataset were collected with sup- port of National Institutes of Health award R03 HD094615–01A1. The authors would like to acknowledge Marie Wasielewski and Carson Ingo for their support in acquiring these data. K.Z. was supported by an NIH-funded training program (T32EB025766). S.M. was supported by the European Union’s Horizon 2020 research and innovation pro- gram (Marie Sk ł odowska-Curie grant agreement No. 713673), a fel- lowship from La Caixa Foundation (ID 100010434, fellowship code LCF/BQ/IN17/11620063) and C.C.G was supported by the Spanish Ministry of Economy and Competitiveness (Ramon y Cajal Fellowship, RYC-2017- 21845), the Basque Government (BERC 2018–2021 and PIBA_2019_104) and the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019–105520GB-100)