Regional variations in vascular density correlate with resting-state and task-evoked blood oxygen level-dependent signal amplitude.

Functional magnetic resonance imaging (fMRI) has become one of the primary tools used for noninvasively measuring brain activity in humans. For the most part, the blood oxygen level-dependent (BOLD) contrast is used, which reflects the changes in hemodynamics associated with active brain tissue. The main advantage of the BOLD signal is that it is relatively easy to measure and thus is often used as a proxy for comparing brain function across population groups (i.e., control vs. patient). However, it is particularly weighted toward veins whose structural architecture is known to vary considerably across the brain. This makes it difficult to interpret whether differences in BOLD between cortical areas reflect true differences in neural activity or vascular structure. We therefore investigated how regional variations of vascular density (VAD) relate to the amplitude of resting-state and task-evoked BOLD signals. To address this issue, we first developed an automated method for segmenting veins in images acquired with susceptibility-weighted imaging, allowing us to visualize the venous vascular tree across the brain. In 19 healthy subjects, we then applied voxel-based morphometry (VBM) to T1-weighted images and computed regional measures of gray matter density (GMD). We found that, independent of spatial scale, regional variations in resting-state and task-evoked fMRI amplitudes were better correlated to VAD compared to GMD. Using a general linear model (GLM), it was observed that the bulk of regional variance in resting-state activity could be modeled by VAD. Cortical areas whose resting-state activity was most suppressed by VAD correction included Cuneus, Precuneus, Culmen, and BA 9, 10, and 47. Taken together, our results suggest that resting-state BOLD signals are significantly related to the underlying structure of the brain vascular system. Calibrating resting BOLD activity by venous structure may result in a more accurate interpretation of differences observed between cortical areas and/or individuals.