  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 laying the way for new directions in brain aging
research.
