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

2026-07-22

AI Summary

研究通过自监督学习框架Champollion从结构MRI中学习皮质折叠的局部表示，以揭示神经发育特征，并在多项任务上优于现有模型。

Abstract

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.

Problem

• cortical folding
• neuroimaging
• representation learning
• neurodevelopmental signatures

Innovation

• self-supervised learning
• cortical folding
• structural MRI
• neurodevelopmental markers
• genetic associations