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 identify typical 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, β − V AE and SimCLR, in an unsupervised setting to learn a relevant representation of these patterns. We add a decoder to SimCLR to be able to analyse latent space. Specifically, we leverage the data augmentations used in SimCLR to propose a novel kind of augmentations based on folding topology. We then apply a clustering on the latent space. Cluster folding averages, interpolation in the latent space and reconstructions reveal new pattern structures. This structured representation shows that unsupervised learning can help in the discovery of still unknown 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.
Although the main structures of cortical folding are present in each human brain, the folding pattern is unique to each individual. Because of this large normal variability, the identification of abnormal patterns associated to developmental disorders is a complex open challenge. In this paper, we tackle this problem as an anomaly detection task and explore the potential of deep generative models using benchmarks made up of synthetic anomalies. To focus learning on the folding geometry, brain MRI are preprocessed first to deal only with a skeleton-based negative cast of the cortex. A variational auto-encoder is trained to get a representation of the regional variability of the folding pattern of the general population. Then several synthetic benchmark datasets of abnormalities are designed. The latent space expressivity is assessed through classification experiments between control's and abnormal's latent codes. Finally, the properties encoded in the latent space are analyzed through perturbation of specific latent dimensions and observation of the resulting modification of the reconstructed images. The results have shown that the latent representation is rich enough to distinguish subtle differences like asymmetries between the right and left hemispheres.
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