DICCCOL predictor (v0.1) is a toolbox to predict 358 DICCCOL landmarks on a new brain given b0, brain surface data and DTI derived fiber data (vtk format). DICCCOL is the abbreviation of Dense Individualized and Common Connectivity-based Cortical landmarks (http://dicccol.cs.uga.edu) and developed by CAID (caid.cs.uga.edu). Each DICCCOL landmark is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. DICCCOL aims to provide large-scale cortical landmarks with finer granularity, better functional homogeneity, more accurate functional localization, and automatically-established cross-subjects correspondence.