Cross-Dataset Linkage of Brain MRI using Image Similarity Measures

Head magnetic resonance imaging (MRI) data are routinely collected and shared for research under strict regulatory frameworks that require the removal of direct identifiers prior to data release. However, even after skull stripping, brain parenchyma may retain participant-specific features that enable linkage of scans acquired from the same individual across datasets, posing a potential privacy risk when combined with auxiliary information. Current regulatory approaches typically assess such risks using qualitative notions of reasonableness. Although prior work has suggested that brain MRI can support subject linkage, existing demonstrations have relied on training-based or computationally intensive methods. Here, we show that reliable linkage of skull-stripped T1-weighted brain MRI is possible using standard preprocessing pipelines followed by direct image similarity computations. Using this simple approach, we achieve near-perfect matching accuracy across datasets acquired at different time points, with varying scanner types, spatial resolutions, and acquisition protocols, and even in the presence of cognitive decline. These experiments simulate realistic scenarios of cross-database matching in large-scale neuroimaging repositories. Our findings highlight a previously underappreciated re-identification risk in shared brain MRI data and provide empirical evidence relevant to the development of informed, forward-looking data-sharing policies in neuroimaging research.