Distance networks for morphological profiling and characterization of DICCCOL landmarks

In recent works, 358 cortical landmarks named Dense Individualized Common Connectivity based Cortical Landmarks (DICCCOLs) were identified. Instead of whole-brain parcellation into sub-units, it identified the common brain regions that preserve consistent structural connectivity profile based diffusion tensor imaging (DTI). However, since the DICCCOL system was developed based on connectivity patterns only, morphological and geometric features were not used. Thus, in this paper, we constructed distance networks based on both geodesic distance and Euclidean distance to morphologically profile and characterize DICCCOL landmarks. Based on the distance network derived from 10 templates subjects with DICCCOL, we evaluated the anatomic consistency of each DICCCOL, identified reliable/unreliable DICCCOLs, and modeled the distance network of DICCCOLs. Our results suggested that the most relative consistent connections are long distance connections. Also, both of the distance measurements gave consistent observations and worked well in identifying anatomical consistent and inconsistent DICCCOLs. In the future, distance networks can be potentially applied as a complementary metric to improve the prediction accuracy of DICCCOLs or other ROIs defined on cortical surface.