A subject-specific reversible folding model reveals geometry-driven white-matter organization
Osman, B.; Vink, R.; Jalba, A. C.; Schilling, K. G.; Chamberland, M.
Show abstract
Tractography currently relies almost exclusively on diffusion data and seed maps, which alone remain insufficient for anatomically accurate fiber reconstruction. During brain development, white-matter fibers elongate alongside cortical folding, suggesting a close mechanistic link between cortical geometry and fiber organization. To investigate this link, we introduce a subject-specific cortical folding simulation framework that reconstructs an individuals folding trajectory using only a structural T1-weighted image. The quasi-static, constraint-based model reverses folding to generate an unfolded, fetal-like configuration and then refolds to map the resulting volumetric deformation onto fiber organization. Vali-dation with longitudinal fetal MRI shows that simulated folds follow biologically plausible developmental paths. Applied to adult data, deformation of simple radial fibers reproduces diffusion-derived orientation patterns across the white matter and achieves high regional correspondence. The deformation model also generates characteristic short-range U-fibers as well as long-range association and commissural pathways, all without any diffusion in-put or machine learning. This approach provides a new, anatomically grounded source of subject-specific fiber orientation derived solely from cortical geometry, opening avenues for geometry-informed tractography.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- XTRACT - Standardised protocols for automated tractography in the human and macaque brain 96%
- An atlas of white matter anatomy, its variability, and reproducibility based on Constrained Spherical Deconvolution of diffusion MRI 96%
- Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data 96%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.