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.

存在规则是一种表达性知识表示语言，主要开发用于查询数据。在文献中，它们通常被认为是一种正常形式，可以简化技术发展。例如，一个共同的假设是规则头是原子，即仅限于单个原子。这样的假设被认为是在不丧失一般性的情况下进行的，只要在保留累积的过程中可以将所有规则集归一化。但是，一个重要的问题是确保推理可决定性的属性是否也保留。我们对这些程序对Chase（非）终止和FO-剥夺性的不同追逐变体的影响提供了系统的研究。这也导致我们研究与追逐独立利益有关的开放问题。