Calibration‐Free Estimation of Cerebrovascular Reactivity From Resting‐State
BOLD fMRI
Using Reconstructed Respiratory Fluctuations
Abdoljalil Addeh, Ethan Church, Muhammad Mahajna, Karen Ardila, Pattarawut Charatpangoon, Alexander D. Cohen, Yang Wang, Rebecca J. Williams, G. Bruce Pike, M. Ethan MacDonald ABSTRACT
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.