Identification of Rare Cortical Folding Patterns using Unsupervised Deep Learning

TL;DR

This paper introduces an unsupervised deep learning method using beta-VAE to identify rare cortical folding patterns in brain MRIs, aiding in biomarker discovery for neurodevelopmental disorders.

Contribution

It presents a novel approach leveraging beta-VAE to model inter-individual variability and detect rare brain folding patterns, with demonstrated generalization across regions and datasets.

Findings

The method effectively encodes folding characteristics for rare pattern detection.

Latent space and reconstruction errors provide complementary detection signals.

The approach generalizes well to different brain regions and datasets.

Abstract

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.