Modelling white matter in gyral blades as a continuous vector field

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Biophysics

Background:

• Diffusion tractography is crucial for mapping brain structural connectivity.
• Existing methods suffer from "gyral biases" near the white matter-cortical grey matter boundary, limiting accuracy.
• These biases result in inaccurate streamline distributions compared to anatomical data.

Purpose of the Study:

• To develop and validate a novel algorithm to mitigate tractography "gyral biases".
• To improve the anatomical accuracy of structural connectivity mapping.
• To enhance the agreement between structural and functional brain connectomes.

Main Methods:

• Proposed an algorithm modeling fiber density and orientation using a divergence-free vector field.
• Applied the algorithm to in vivo diffusion MRI data from the Human Connectome Project.
• Compared tractography results with functional connectomes derived from resting-state fMRI.

Main Results:

• The algorithm successfully reduced "gyral biases" in diffusion tractography.
• Improved cross-modal agreement between structural and functional connectivity maps.
• Minor changes in the overall structural connectome post-parcellation, with enhanced interhemispheric connectivity.

Conclusions:

• The proposed algorithm offers a more anatomically accurate approach to diffusion tractography.
• This method enhances the reliability of structural connectivity estimation.
• Improved structural connectomes show better correspondence with functional brain organization.