Inter-Regional Structural Self-Supervised Learning for Brain MRI: Individual and Population Formulations

Brain structure is characterized not only by the properties of individual anatomical regions but also by the relationships among them. These inter-regional relationships constitute a fundamental aspect of brain organization. However, existing self-supervised learning (SSL) methods for structural brain MRI predominantly rely on voxel reconstruction or instance-level consistency, without explicitly modeling such inter-regional structural organization. We introduce Inter-Regional Structural Self-Supervised Learning (IRS-SSL), a framework that establishes inter-regional structural organization as its primary self-supervisory principle and inductive bias. Rather than incorporating inter-regional relationships as an auxiliary constraint on a conventional SSL objective, IRS-SSL derives its primary supervisory signal directly from these relationships. We instantiate this principle through two formulations. The individual formulation, IRS-SSL-Ind, characterizes pairwise similarities between regional structural profiles within each subject. The population formulation, IRS-SSL-Pop, captures coordinated variation between brain regions across subjects. The two formulations encode distinct relational information: IRS-SSL-Ind describes how regions relate within a particular brain, whereas IRS-SSL-Pop captures structural regularities shared across individuals. We systematically evaluate both formulations across diverse downstream neuroimaging tasks and compare them with state-of-the-art SSL frameworks. Both formulations demonstrate the value of inter-regional structural organization as a source of self-supervision. Notably, IRS-SSL-Ind yields strong and consistent downstream performance. These findings establish inter-regional structural organization as an effective inductive bias for SSL and provide new insights into the relative value of individual- and population-derived relationships for learning representations from structural brain MRI.