Multi-voxel pattern analysis (MVPA) provides a powerful framework for making statistical inferences on the information present in brain activity patterns as measured by functional magnetic resonance imaging (fMRI). Many recent studies suggest that MVPA performance benefits from taking into account the spatial voxel-to-voxel correlations in the measurement noise. However, estimating these noise correlations is challenging due to the limited data points and large voxel counts. To address this issue, it is common practice to shrink the empirical correlation estimate towards its identity matrix, which biases the estimate towards the incorrect assumption that voxels are independent. We therefore propose an anatomically-informed model of measurement noise in fMRI, which takes into account the distances of voxels in the measurement volume, their distance on the cortical sheet, and the depth at which they sample the cortex. Our model can predict the noise-correlation structure in new participants and datasets. It improves the noise correlation estimate when used as a shrinkage target, thereby also potentially improving statistical inferences in MVPA
