In densely sampled regions, there are more vertices in each searchlight and more searchlight centers in the region, leading to prolonged computations. By uniformly sampling the cortex, onavg reduces the computational time of various commonly used algorithms.

As a result of uniform sampling, the quality of spatial patterns is greatly improved, leading to increased performance of various MVPA algorithms.

We optimized the location of cortical vertices by penalizing a pair of vertices if they were too close on the anatomical surface. After the optimization, we obtained the onavg template, where vertices are evenly distributed on the anatomical surface.

Traditional templates choose their vertices based on the inflated spherical surface. Vertices are mostly uniform on the spherical surface. However, on the actual anatomical surface, vertices are often sparsely distributed on gyri and densely distributed on sulci.