BrainNorm: Learning the Healthy Brain Representations from Structural MRI

We introduce **BrainNorm**, a normative foundation model trained and evaluated on  66,000 T1-weighted structural MRI (T1w sMRI) scans. Trained exclusively on healthy subjects, BrainNorm learns a _Semantic Atlas Latent_ (SAL) space in which anatomically grounded parcel representations are jointly organized by anatomical identity and age. Through contrastive alignment with parcel-specific, age-conditioned healthy reference templates, SAL provides an explicit geometry of healthy anatomical variation that supports age-consistent template matching and represents _localized deviations_ directly in the latent space. Across multiple downstream cohorts, BrainNorm is evaluated on various tasks spanning age estimation, brain-age gap estimation, parcel identification, and single- & multi-disease classification. The resulting deviation patterns in SAL space enable zero-shot disease prediction from parcel-wise abnormalities and exhibit regional patterns that closely align with findings across neurodegenerative diseases reported in the clinical literature. Fine-tuning on healthy-only subjects from downstream cohorts further improves performance across various tasks. Across all classification tasks, linear probing on BrainNorm’s frozen embeddings outperforms nine baselines fine-tuned with end-to-end downstream supervision. Together, these results demonstrate that learning an anatomically structured, age-conditioned geometry of healthy brain variation yields transferable representations for detecting and characterizing departures from expected brain structure.