The study of functional MRI (fMRI) data is increasingly performed after mapping from volumetric voxels to surface vertices. Processing pipelines commonly used to achieve this mapping produce meshes with uneven vertex spacing, with closer neighbours in sulci compared to gyri. Consequently, correlations between the fMRI time series of neighbouring sulcal vertices are stronger than expected. However, the causes, extent, and impacts of this “gyral bias” are not completely understood or widely appreciated. We explain the origins of this bias, and using in-silico models of fMRI data, illustrate how it leads to spurious results and leakage of anatomical cortical folding information into fMRI time series. We show that many common analyses can be affected by this bias, including test-retest reliability, fingerprinting, functional parcellations, and regional homogeneity. The recently developed onavg template partly reduces the bias but has relatively high residual variability in vertex spacing when projected to participant-specific surfaces. Finally, we outline recommendations to avoid or remedy the gyral bias.