Calibration-Free Estimation of Cerebrovascular Reactivity From Resting-State BOLD fMRI Using Reconstructed Respiratory Fluctuations

Purpose: Cerebrovascular reactivity (CVR) provides an important index of vascular health and is conventionally quantified using a hypercapnic gas or breath-hold challenge in conjunction with blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI). Such approaches require a dedicated extra scan and, for hypercapnia, specialized equipment for gas administration and external physiological recordings, limiting their applicability in large-scale neuroimaging studies and clinical populations. To address this limitation, we introduce a calibration-free and breath-hold-free framework for CVR estimation from resting-state BOLD-fMRI.

Methods: The method leverages a previously validated machine learning-based respiratory variation (RV) reconstruction approach to recover respiratory dynamics directly from BOLD-fMRI time series. The reconstructed RV is subsequently convolved with a respiratory response function and incorporated as a regressor in a voxel-wise general linear model (GLM), yielding regression coefficients that serve as CVR estimates.

Results: Validation was performed on a cohort of 83 healthy young adults with ground-truth CVR maps obtained from hypercapnic gas challenges. The proposed framework demonstrated spatial correspondence with measured CVR (mean correlation = 0.61), with the strongest performance observed in participants exhibiting greater respiratory variability (mean r = 0.72).

Conclusion: Collectively, these results establish a reliable, non-invasive, and calibration-free strategy for CVR mapping, enabling broader deployment in both research and clinical environments where gas-challenge protocols and physiological monitoring are impractical.