Resting-state functional MRI (RS-fMRI) is a widely used method for inferring connectivity between brain regions or nodes. As with task-based fMRI, the spatial specificity of the connectivity maps can be distorted by the strong biasing effect of the BOLD signal in macroscopic veins. In RS-fMRI this effect is exacerbated by the temporal coherences of physiological origin between large veins that are widely distributed in the brain. In gradient echo based EPI, used for the vast majority of RS-fMRI, macroscopic veins that carry BOLD-related changes exhibit a strong phase response. This allows for post-processing identification and removal of venous signals using a phase regressor technique. Here, we employ this approach to suppress macrovascular venous contributions in high-field whole-brain RS-fMRI data sets, resulting in significant changes to both the spatial localization of the networks and the correlations between the network nodes. These effects were observed at both the individual and group analysis level, suggesting that venous contamination is a confounding factor for RS-fMRI studies even at relatively low image resolutions. Suppression of the macrovascular signal using the phase regression approach may therefore help to better identify, delineate, and interpret the true structure of large-scale brain networks.