Unsupervised Representation Learning of Cingulate Cortical Folding Patterns

Joël Chavas, Louise Guillon, Marco Pascucci, Benoit Dufumier, Denis Rivière, Jean-Francois Mangin

Keywords: beta-VAE, SimCLR, contrastive learning, folding pattern

TL;DR: Contrastive learning and beta-VAE permit to characterize cortical folding patterns

Abstract: The human cerebral cortex is folded, making sulci and gyri over the whole cortical surface. Folding presents a very high inter-subject variability, and some neurodevelopmental disorders are correlated to local folding structures, named folding patterns. However, it is tough to characterize these patterns manually or semi-automatically using geometric distances. Here, we propose a new methodology to represent and group individuals having similar folding patterns. We focus on the cingulate region, known to have a clinical interest, using so-called skeletons (3D representation of folding patterns). We compare two models, beta-VAE and SimCLR, in an unsupervised setting to learn a relevant representation of these patterns. Specifically, we leverage the data augmentations used in SimCLR to propose a novel kind based on folding topology. Best clustering with Affinity Propagation has a silhouette score of 0.42. Comparison of cluster averages reveals new pattern structures, and test with the other half of the dataset demonstrates that the representation is stable. This structured representation shows that unsupervised learning can increase the number of detected patterns. We will gain further insights into folding patterns by using new priors in the unsupervised algorithms and integrating other brain data modalities. Code and experiments are available at github.com/neurospin-projects/2021_jchavas_lguillon_deepcingulate.

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Paper Type: both

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Application: Other

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