Cortical Surfaces Integration with Tractography for Structural Connectivity Analysis.

Abstract

Background: Mapping diffusion MRI tractography streamlines to the cortical surface facilitates the integration of white matter features onto gray matter, especially for connectivity analysis. Method: In this work, we present methods that combine cortical surface meshes with tractography reconstruction to improve endpoint precision and coverage. This cortical mapping also enables the study of structural measures from tractography along the cortex and subcortical structures. In addition to structural connectivity analysis, novel adaptive and dynamic surface seeding methods are proposed. These improvements are made by incorporating cortical maps such as endpoint density. Results: The proposed dynamic surface seeding increases the cortical coverage and reduces endpoint location biases. Our results suggest that the use of cortical and subcortical meshes together with a proper seeding strategy can reduce the variability in structural connectivity analysis. Conclusion: The proposed adaptive and dynamic seeding utilize cortical maps to better distribute tractography interconnections, thus increasing cortical coverage and reducing endpoint bias. This also facilitates the analysis of white matter & diffusion MRI features along the cortex, combined with cortical measures or functional activation. Impact statement This research presents an overview of surface mapping methods for tractography to reduce structural connectivity variability. The proposed adaptive and dynamic seeding utilize cortical maps to better distribute tractography interconnections, thus increasing cortical coverage and reducing end-point bias. This also facilitates the analysis of white matter and diffusion magnetic resonance imaging features along the cortex, combined with cortical measures or functional activation.