Lag-Optimized Blood Oxygenation Level Dependent Cerebrovascular Reactivity Estimates Derived From Breathing Task Data Have a Stronger Relationship With Baseline Cerebral Blood Flow

Abstract

Published: 15 June 2022Cerebrovascular reactivity (CVR), an important indicator of cerebrovascular health, is commonly studied with the Blood Oxygenation Level Dependent functional MRI (BOLD-fMRI) response to a vasoactive stimulus. Theoretical and empirical evidence suggests that baseline cerebral blood flow (CBF) modulates BOLD signal amplitude and may influence BOLD-CVR estimates. We address how acquisition and modeling choices affect the relationship between baseline cerebral blood flow (bCBF) and BOLD-CVR: whether BOLD-CVR is modeled with the inclusion of a breathing task, and whether BOLD-CVR amplitudes are optimized for hemodynamic lag effects. We assessed between-subject correlations of average GM values and within-subject spatial correlations across cortical regions. Our results suggest that a breathing task addition to a resting-state acquisition, alongside lag-optimization within BOLD-CVR modeling, can improve BOLD-CVR correlations with bCBF, both between- and within-subjects, likely because these CVR estimates are more physiologically accurate. We report positive correlations between bCBF and BOLD-CVR, both between- and within-subjects. The physiological explanation of this positive correlation is unclear; research with larger samples and tightly controlled vasoactive stimuli is needed. Insights into what drives variability in BOLD-CVR measurements and related measurements of cerebrovascular function are particularly relevant when interpreting results in populations with altered vascular and/or metabolic baselines or impaired cerebrovascular reserve.This work was supported by the Center for Translational Imaging at Northwestern University. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health [K12HD073945]. KZ was supported by an NIH-funded training program [T32EB025766]. SM was supported by the European Union’s Horizon 2020 research and innovation program [Marie Skłodowska-Curie grant agreement No. 713673] and a fellowship from La Caixa Foundation [ID 100010434, fellowship code LCF/BQ/IN17/11620063]. CC-G was supported by the Spanish Ministry of Economy and Competitiveness [Ramon y Cajal Fellowship, RYC2017-21845], the Basque Government [BERC 2018-2021 and PIBA_2019_104], and the Spanish Ministry of Science, Innovation and Universities [MICINN; PID2019- 105520GB-100]