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Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures
Preprint   Open access

Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures

Julien Laval, Robin Guiavarch, Antoine Dufournet, Racim Menasria, Barthélémy Drabczuk, Cristobal Mendoza, Saeb Tounsi, Chikh Abdelghani Baroud, Merieme Bourenane, Vanessa Troiani, …
arXiv (Cornell University)
22 Jul 2026
DOI: https://doi.org/10.48550/arxiv.2609.05438
url
https://doi.org/10.48550/arXiv.2609.05438View
Preprint (Author's original) Open arXiv.org - Non-exclusive license to distribute

Abstract

Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Quantitative Biology - Quantitative Methods
The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable thereafter, making them promising early neurodevelopmental markers. Yet it is unclear whether representations given by current neuroimaging foundation models capture cortical folding variability. Here, we introduce Champollion, a self-supervised learning framework that learns interpretable local representations of cortical folding from structural MRI. Optimized on representative folding-related tasks, Champollion accurately captures known folding patterns across cortical regions and external datasets. In a comprehensive benchmark, it consistently outperforms neuroimaging and general-purpose foundation models. Furthermore, Champollion reveals richer genetic associations than conventional morphometric descriptors and identifies localized folding signatures associated with incomplete hippocampal inversion, prematurity, and maternal smoking. These results establish cortical folding as a rich and largely untapped source of neurodevelopmental information and illustrate how pre-processing and architectural inductive biases can recover biologically meaningful signals overlooked by current generalist foundation models.

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