Functional MRI (fMRI) is crucial for studying brain function and diagnosing neurological disorders. However, existing analysis methods suffer from reproducibility and transferability challenges due to complex preprocessing pipelines and task-specific model designs. In this work, we introduce NeuroSTORM (Neuroimaging Foundation Model with Spatial-Temporal Optimized Representation Modeling) that learns generalizable representations directly from 4D fMRI volumes and enables efficient transfer to diverse downstream applications. Specifically, NeuroSTORM is pre-trained on 28.65 million fMRI frames from over 50,000 subjects, spanning multiple centers and ages 5 to 100. It combines an efficient spatiotemporal modeling design and lightweight task adaptation to enable scalable pre-training and fast transfer to downstream applications. Here we show that NeuroSTORM consistently outperforms existing methods across five downstream tasks, including demographic prediction, phenotype prediction, disease diagnosis, re-identification, and state classification. On two multi-hospital clinical cohorts with 17 diagnoses, NeuroSTORM achieves the best diagnosis performance while remaining predictive of psychological and cognitive phenotypes. These results suggest that NeuroSTORM could become a standardized foundation model for reproducible and transferable fMRI analysis.

翻译：功能性磁共振成像（fMRI）对于研究大脑功能和诊断神经系统疾病至关重要。然而，现有分析方法因复杂的预处理流程和特定任务模型设计而面临可重复性与可迁移性挑战。本文提出NeuroSTORM（基于时空优化表征建模的神经影像基础模型），该模型可直接从4D fMRI体素数据中学习通用表征，并高效迁移至多种下游应用。具体而言，NeuroSTORM在涵盖5至100岁年龄跨度、来自多个中心的50,000余名被试的2,865万帧fMRI数据上完成预训练。通过融合高效时空建模设计与轻量化任务适配策略，该模型实现了可扩展的预训练与快速下游迁移。实验表明，NeuroSTORM在人口学预测、表型预测、疾病诊断、重识别及状态分类等五项下游任务中均持续优于现有方法。在两个包含17种诊断的多医院临床队列中，NeuroSTORM在取得最佳诊断性能的同时，仍能有效预测心理与认知表型。这些结果表明，NeuroSTORM或将成为实现可重复、可迁移fMRI分析的标准化基础模型。