BrainNorm: A Foundation Model that knows Normal via Semantic Atlas Pretraining

2026-08-18

AI Summary

BrainNorm通过在健康人群的T1加权结构MRI上进行语义图谱预训练，学习正常脑老化模式，并用于疾病预测和分类。

Abstract

We introduce BrainNorm, a normative foundation model, trained and tested on ~66,000 T1-weighted structural MRI (T1w sMRI) scans. By leveraging language-image style contrastive pretraining on healthy cohorts across ages, BrainNorm learns a Semantic Atlas Latent space (SAL), where each scan is represented as a set of atlas-parcel embeddings. This yields parcel-specific healthy aging template trajectories that support age-consistent template matching and localized deviation scoring relative to a subject's chronological age. Across 6 downstream cohorts, BrainNorm demonstrates generalization evaluated across 25 task-setting combinations spanning age estimation, brain-age gap estimation, parcel identification, and single- & multi-disease classification tasks under direct inference, zero-shot, few-shot & full-data linear-probe settings. The resulting deviation patterns in SAL space enable zero-shot tasks for disease prediction using parcel-wise abnormalities. Fine-tuning on healthy-only cohorts of downstream datasets further improves the performance of various tasks. Across all classification tasks, linear probing on BrainNorm's frozen embeddings outperforms 9 baselines finetuned under end-to-end supervision. Furthermore, the localized deviations identified by BrainNorm across various neurodegenerative disorders closely align with established neurodegeneration pathology in clinical literature.

Problem

• BrainNorm
• Semantic Atlas Latent space
• healthy aging template
• deviation scoring
• neurodegenerative disorders

Innovation

• Semantic Atlas Latent space
• contrastive pretraining
• age-consistent template matching
• localized deviation scoring
• zero-shot tasks