Cerebrovascular reactivity mapping using breath-hold BOLD-fMRI: Comparison of signal models combined with voxelwise lag optimization

Cerebrovascular reactivity (CVR) can be mapped noninvasively using blood oxygenation level dependent (BOLD) fMRI during a breath-hold (BH) task. Previous studies showed that the BH BOLD response is best modeled as the convolution of the partial pressure of end-tidal CO2 (PetCO2) with a canonical hemodynamic response function (HRF). However, previous model comparisons employed a global bulk time lag, which is now well accepted to provide only a rough approximation of the heterogeneous distribution of response latencies across the brain. Here, we investigate the best modeling approach for mapping CVR based on BH BOLD-fMRI data, when using a lagged general linear model approach for voxelwise lag optimization. In a group of 14 healthy participants, we compared two types of regressors (PetCO2 and Block), and three convolution models (no convolution; convolution with a single gamma HRF; and convolution with a double gamma HRF), as well as a range of HRF delays and dispersions (for models with convolution). Convolution with a single gamma HRF yielded the greatest CVR values in PetCO2 models, while a double gamma HRF performed better for block models. Although PetCO2-based regressors generally outperformed block-based regressors, as expected, the latter may be an appropriate alternative in cases of poor CO₂ recordings. Overall, our results support the use of specific modeling approaches for CVR mapping based on end-expiration BH BOLD-fMRI, including the voxelwise optimization of the lag.