A subject-specific reversible folding model reveals geometry-driven white-matter organization.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Developmental Neuroscience

Background:

• Current tractography methods using diffusion data and seed maps lack anatomical accuracy.
• White matter fiber development is closely linked to cortical folding patterns during brain development.

Purpose of the Study:

• To develop a subject-specific cortical folding simulation framework to reconstruct white matter fiber organization.
• To investigate the mechanistic link between cortical geometry and fiber organization using a novel computational model.

Main Methods:

• Introduced a quasi-static, constraint-based model to simulate cortical folding trajectories from structural T1-weighted MRI.
• Reversed folding to a fetal-like configuration and then refolded to map volumetric deformation onto fiber organization.
• Validated the model with longitudinal fetal MRI and applied it to adult data.

Main Results:

• Simulated cortical folds demonstrated biologically plausible developmental paths.
• The model accurately reproduced diffusion-derived white matter orientation patterns and regional correspondence in adults.
• Generated realistic short-range U-fibers and long-range association/commissural pathways without diffusion input or machine learning.

Conclusions:

• Cortical geometry alone provides a powerful, anatomically grounded source for subject-specific white matter fiber orientation.
• This geometry-informed approach offers a new paradigm for tractography, reducing reliance on diffusion imaging.
• The framework opens new avenues for understanding brain development and improving tractography accuracy.