  Like fingerprints, cortical folding patterns are unique to each brain even
though they follow a general species-specific organization. Some folding
patterns have been linked with neurodevelopmental disorders. However, due to
the high inter-individual variability, the identification of rare folding
patterns that could become biomarkers remains a very complex task. This paper
proposes a novel unsupervised deep learning approach to identify rare folding
patterns and assess the degree of deviations that can be detected. To this end,
we preprocess the brain MR images to focus the learning on the folding
morphology and train a beta-VAE to model the inter-individual variability of
the folding. We compare the detection power of the latent space and of the
reconstruction errors, using synthetic benchmarks and one actual rare
configuration related to the central sulcus. Finally, we assess the
generalization of our method on a developmental anomaly located in another
region. Our results suggest that this method enables encoding relevant folding
characteristics that can be enlightened and better interpreted based on the
generative power of the beta-VAE. The latent space and the reconstruction
errors bring complementary information and enable the identification of rare
patterns of different nature. This method generalizes well to a different
region on another dataset. Code is available at
https://github.com/neurospin-projects/2022_lguillon_rare_folding_detection.
