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 combined with voxelwise lag optimization. In a group of fourteen healthy participants, we compared models considering: two types of regressors (PetCO2 and Block), three convolution models (no convolution; convolution with a single gamma HRF; and convolution with a double gamma HRF), and a variable HRF delay (3-11s). We found no significant improvements in model fit with other delays, and hence selected the canonical delay of 6s. Although the two regressor types yielded similar model fits, PetCO2 produced significantly greater CVR values than Block models. Interestingly, a single gamma HRF yielded the greatest CVR values in PetCO2 models, while block models benefited from convolution with a double gamma HRF. In conclusion, when modeling BH BOLD-fMRI signals with voxelwise lag optimization, PetCO2 regressors convolved with a single gamma HRF should be preferentially used. In case good quality PetCO2 recordings are unavailable, a block-based model convolved with the canonical HRF may be a good alternative. In conclusion, our manuscript reports the first systematic signal model comparison, providing evidence to support the use of specific modeling approaches for CVR mapping based on BH BOLD-fMRI.