Self-supervised contrastive learning unveils cortical folding pattern linked to prematurity

TL;DR

This study uses self-supervised contrastive learning to identify cortical folding patterns linked to prematurity, revealing reduced variability and specific structural features in preterm infants.

Contribution

It introduces a novel application of self-supervised contrastive learning to analyze cortical folding patterns related to prematurity.

Findings

Lower variability in preterm embeddings

Identification of a missing knob pattern in extremely preterm infants

Potential marker for prematurity-related brain development

Abstract

Brain folding patterns have been reported to carry clinically relevant information. The brain folds mainly during the last trimester of pregnancy, and the process might be durably disturbed by preterm birth. Yet little is known about preterm-specific patterns. In this work, we train a self-supervised model (SimCLR) on the UKBioBank cohort (21070 adults) to represent the right superior temporal sulcus (STS) region and apply it to sulci images of 374 babies from the dHCP database, containing preterms and full-terms, and acquired at 40 weeks post-menstrual age. We find a lower variability in the preterm embeddings, supported by the identification of a knob pattern, missing in the extremely preterm population.