BrainNorm：一种通过语义图谱预训练了解正常状态的基础模型

中文摘要 AI 辅助

我们推出了BrainNorm，这是一种规范基础模型，在约66000例T1加权结构磁共振成像（T1w sMRI）扫描数据上进行训练和测试。通过对跨年龄段的健康队列采用语言-图像风格的对比预训练，BrainNorm学习到了语义图谱潜在空间（SAL），其中每一次扫描都表示为一组脑区分区嵌入。这产生了特定脑区分区的健康衰老模板轨迹，支持与受试者实际年龄一致的模板匹配和局部偏差评分。在6个下游队列中，BrainNorm在25种任务设置组合上展示了泛化能力，这些组合涵盖年龄估计、脑-年龄差距估计、脑区分区识别，以及直接推理、零样本、少样本和全数据线性探测设置下的单疾病与多疾病分类任务。SAL空间中产生的偏差模式支持利用脑区分区异常进行疾病预测的零样本任务。对下游数据集的仅健康队列进行微调，进一步提升了各类任务的性能。在所有分类任务中，对BrainNorm的冻结嵌入进行线性探测，其表现优于9种在端到端监督下微调的基线模型。此外，BrainNorm在各类神经退行性疾病中识别出的局部偏差，与临床文献中已确立的神经退行病变高度吻合。

英文摘要

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.

发表机构

• Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）

机构由 AI 辅助整理，请以论文原文为准。