Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are noninvasive neuroimaging technologies providing functional mapping of stimulus activated voxels and detailed connectivity structures in the brain, which are traditionally based on simplified linear models. Despite the unique functional and structural representations achievable by fMRI and DTI, respectively, both representations still need validations of the assumptions embedded in the analysis of the data. Recent research efforts emphasize either a data driven or a hybrid approach to fMRI data analysis for more robust characterization of the data. Here we propose a methodology for finding relatively quantitative axonal connectivity pathways among distinct functional regions in the brain using appropriate image analysis techniques with the ultimate goal of generating a multidimensional structure-function correlation map. To achieve this goal, in our preliminary studies we have used independent component analysis (ICA) on fMRI data to locate the terminal seed points on axonal pathways segmented from fractional anisotropic (FA) DTI slices. The co-registered fMRI, and FA-DTI data with the corresponding anatomical image were color coded for visualization. A robust segmentation of axonal pathways was obtained by using a nonparametric estimation of Gaussian mixture model based on the transformation and analysis of the D(R) (distortion-rate) curve.