Riemannian diffusion kernel-smoothed continuous structural connectivity on cortical surface.

ABSTRACT: Atlas-free continuous structural connectivity has garnered increasing attention due to the limitations of atlas-based approaches, including the arbitrary selection of brain atlases and potential information loss. Typically, continuous structural connectivity is represented by a probability density function, with kernel density estimation as a common estimation method. However, constructing an appropriate kernel function on the cortical surface poses significant challenges. Current methods often inflate the cortical surface into a sphere and apply the spherical heat kernel, introducing distortions to density estimation. In this study, we propose a novel approach using the Riemannian diffusion kernel derived from the Laplace-Beltrami operator on the cortical surface to smooth streamline endp

SUBMITTER: Wang L 

PROVIDER: S-EPMC12511801 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature