Cerebrovascular reactivity (CVR) mapping using CO 2 -inhalation can provide important insight into vascular health. At present, blood-oxygenation-level-dependent (BOLD) MRI acquisition is the most commonly used CVR method due to its high sensitivity, high spatial resolution, and relatively straightforward processing. However, large variations in CVR across subjects and across different sessions of the same subject are often observed, which can cloud the ability of this promising measure in detecting diseases or monitoring treatment responses. The present work aims to identify the physiological components underlying the observed variability in CVR data. When studying the association between CVR value and the subject’s CO 2 levels in a total of N = 253 healthy participants, we found that CVR was lower in individuals with a higher basal end-tidal CO 2 , EtCO 2 (slope = −0.0036 ± 0.0008%/mmHg 2 , p < 0.001), or with a greater EtCO 2 change (ΔEtCO 2 ) with hypercapnic condition (slope = −0.0072 ± 0.0018%/mmHg 2 , p < 0.001). In a within-subject setting, when studying the CVR difference between two repeated scans (with repositioning) in relation to the corresponding differences in basal EtCO 2 and ΔEtCO 2 (n = 11), it was found that CVR values were lower if the basal EtCO 2 or ΔEtCO 2 during that particular scan session was greater. The present work suggests that basal physiological state and the level of hypercapnic stimulus intensity should be considered in application studies of CVR in order to reduce inter-subject and intra-subject variations in the data. Potential approaches to use these findings to reduce noise and augment sensitivity are proposed.

Cerebrovascular Reactivity (CVR) provides an assessment of the brain’s vascular reserve and has been postulated to be a sensitive marker in cerebrovascular diseases. MRI-based CVR measurement typically employs alterations in arterial carbon dioxide (CO2) level while continuously acquiring Blood-Oxygenation-Level-Dependent (BOLD) images. CO2-inhalation and resting-state methods are two commonly used approaches for CVR MRI. However, processing of CVR MRI data often requires special expertise and may become an obstacle in broad utilization of this promising technique. The aim of this work was to develop CVR-MRICloud, a cloud-based CVR processing pipeline, to enable automated processing of CVR MRI data. The CVR-MRICloud consists of several major steps including extraction of end-tidal CO2 (EtCO2) curve from raw CO2 recording, alignment of EtCO2 curve with BOLD time course, computation of CVR value on a whole-brain, regional, and voxel-wise basis. The pipeline also includes standard BOLD image processing steps such as motion correction, registration between functional and anatomic images, and transformation of the CVR images to canonical space. This paper describes these algorithms and demonstrates the performance of the CVR-MRICloud in lifespan healthy subjects and patients with clinical conditions such as stroke, brain tumor, and Moyamoya disease. CVR-MRICloud has potential to be used as a data processing tool for a variety of basic science and clinical applications.

No abstract

No abstract