Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps

TL;DR

This paper introduces a novel contrastive learning method with adaptive neighborhoods for brain age prediction using 3D stiffness maps, incorporating mechanical properties to improve accuracy in neuroimaging analysis.

Contribution

It presents the first self-supervised learning approach utilizing brain stiffness maps and a dynamic contrastive loss to enhance brain age prediction accuracy.

Findings

Outperforms existing state-of-the-art methods

Demonstrates robustness across diverse datasets

Highlights the importance of mechanical properties in brain aging

Abstract

In the field of neuroimaging, accurate brain age prediction is pivotal for uncovering the complexities of brain aging and pinpointing early indicators of neurodegenerative conditions. Recent advancements in self-supervised learning, particularly in contrastive learning, have demonstrated greater robustness when dealing with complex datasets. However, current approaches often fall short in generalizing across non-uniformly distributed data, prevalent in medical imaging scenarios. To bridge this gap, we introduce a novel contrastive loss that adapts dynamically during the training process, focusing on the localized neighborhoods of samples. Moreover, we expand beyond traditional structural features by incorporating brain stiffness - a mechanical property previously underexplored yet promising due to its sensitivity to age-related changes. This work presents the first application of self-supervised learning to brain mechanical properties, using compiled stiffness maps from various clinical studies to predict brain age. Our approach, featuring dynamic localized loss, consistently outperforms existing state-of-the-art methods, demonstrating superior performance and paving the way for new directions in brain aging research.