  The human cerebral cortex has many bumps and grooves called gyri and sulci.
Even though there is a high inter-individual consistency for the main cortical
folds, this is not the case when we examine the exact shapes and details of the
folding patterns. Because of this complexity, characterizing the cortical
folding variability and relating them to subjects' behavioral characteristics
or pathologies is still an open scientific problem. Classical approaches
include labeling a few specific patterns, either manually or
semi-automatically, based on geometric distances, but the recent availability
of MRI image datasets of tens of thousands of subjects makes modern
deep-learning techniques particularly attractive. Here, we build a
self-supervised deep-learning model to detect folding patterns in the cingulate
region. We train a contrastive self-supervised model (SimCLR) on both Human
Connectome Project (1101 subjects) and UKBioBank (21070 subjects) datasets with
topological-based augmentations on the cortical skeletons, which are
topological objects that capture the shape of the folds. We explore several
backbone architectures (convolutional network, DenseNet, and PointNet) for the
SimCLR. For evaluation and testing, we perform a linear classification task on
a database manually labeled for the presence of the "double-parallel" folding
pattern in the cingulate region, which is related to schizophrenia
characteristics. The best model, giving a test AUC of 0.76, is a convolutional
network with 6 layers, a 10-dimensional latent space, a linear projection head,
and using the branch-clipping augmentation. This is the first time that a
self-supervised deep learning model has been applied to cortical skeletons on
such a large dataset and quantitatively evaluated. We can now envisage the next
step: applying it to other brain regions to detect other biomarkers.
