Towards a Foundation Model for Cortical Folding

The brain surface is composed of humps called gyri, separated by grooves called sulci. Although the main folds are common to all individuals, their shape varies, making them unique to each individual. Cortical folding may contain biomarkers that have yet to be deciphered. While conventional geometric approaches fail to fully characterize the high inter-individual variability, recent efforts in large-scale MRI data collection allow us to leverage the statistical power of deep neural networks. Here, we introduce Champollion V0, a self-supervised learning (SSL) algorithm to sort sulcal variability based on 21,070 subjects from the UKBioBank dataset. We revisit from scratch an existing model and optimize its ability to retrieve hand-labeled patterns defined by the neuroscientific community. Under linear evaluation on the latent space, Champollion V0 significantly improves the detection of three different kinds of folding patterns: the presence of a parallel sulcus (AUC increases from 73% to 84%), the presence of specific interruptions (AUC increases from 50% to 79%) and the detection of a specific folding shape (R² increases on each of the six main geometric features), respectively in the cingulate, the orbital and the central region. These hand-labeled patterns were found to be correlated to neurodevelopmental pathologies. Champollion V0 could enable the automatic labeling of larger datasets for future studies. The code can be found on Github.