Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data. Here we show that a single self-supervised representation can generalize across heterogeneous brain MRI endpoints. We trained BrainDINO, a self-distilled foundation model, on approximately 6.6 million unlabeled axial slices from 20 datasets encompassing broad variation in population, disease, and acquisition setting. Using a frozen encoder with lightweight task heads, BrainDINO supported transfer across tumor segmentation, neurodegenerative and neurodevelopmental conditions classification, brain age estimation, post-stroke temporal prediction, molecular status prediction, MRI sequence classification, and survival modeling. Across tasks and supervision regimes, BrainDINO consistently equaled or exceeded natural-image and MRI-specific self-supervised baselines, with particularly strong advantages under label scarcity. Representation analyses further showed anatomically organized and pathology-sensitive feature structure in the absence of task-specific supervision. Our findings indicate that large-scale slice-wise self-supervised learning can yield a unified brain MRI representation that supports diverse neuroimaging tasks without volumetric pretraining or full-network fine-tuning, establishing a scalable foundation for robust and data-efficient brain imaging analysis. Code is available at https://github.com/mclwu22/BrainDINO

翻译：脑部MRI支撑着广泛的神经科学与临床应用，然而大多数基于学习的方法仍局限于特定任务且需要大量标注数据。本研究证明，单一的自监督表征可泛化至异质性脑MRI终点任务。我们训练了BrainDINO——一种自蒸馏基础模型，该模型基于来自20个数据集的约660万张无标注轴向切片进行预训练，这些数据集涵盖人群、疾病及采集设置的广泛变异。通过冻结编码器配合轻量级任务头，BrainDINO支持跨肿瘤分割、神经退行性与神经发育性疾病分类、脑龄估计、卒中后时间预测、分子状态预测、MRI序列分类及生存建模等迁移任务。在不同任务与监督范式下，BrainDINO始终达到或超越自然图像及MRI特异性自监督基线模型，尤其在标签稀缺场景中表现出显著优势。表征分析进一步表明，在缺乏任务特定监督的条件下，该模型依然形成了具有解剖组织性和病理敏感性的特征结构。我们的研究结果表明，大规模切片级自监督学习无需体积预训练或全网络微调，即可生成支持多样化神经影像任务的统一脑MRI表征，为稳健且数据高效的脑影像分析建立了可扩展基础。代码开源于 https://github.com/mclwu22/BrainDINO