Whole-brain 4D fMRI generation is valuable for modeling functional brain dynamics, yet existing fMRI foundation models mainly target representation learning and downstream prediction rather than conditional predictive generation. We introduce BrainWorld, a structural-prior-conditioned generative model for whole-brain 4D fMRI dynamics. BrainWorld uses sMRI as subject-level anatomical context to guide future fMRI generation, integrating structural information into the denoising process rather than treating it as a parallel modality. Evaluated on 22 datasets spanning diverse cohorts and brain states, BrainWorld generates stable 4D fMRI trajectories up to 400 frames, improves downstream performance through generated-example augmentation, and learns transferable multimodal representations that outperform baselines. Together, these results establish BrainWorld as a condition-aware generative framework for long-horizon brain dynamics modeling and multimodal representation learning.

翻译：全脑4D fMRI生成对于模拟大脑功能动态具有重要意义，然而现有fMRI基础模型主要针对表征学习和下游预测任务，而非条件性预测生成。我们提出BrainWorld——一种结构先验条件化生成模型，用于全脑4D fMRI动态建模。BrainWorld利用sMRI作为个体级解剖背景来引导未来fMRI生成，将结构信息整合至去噪过程中，而非将其视为并行模态。在跨越不同队列与脑状态的22个数据集上的评估表明，BrainWorld可生成长达400帧的稳定4D fMRI轨迹，通过生成样本增强提升下游任务性能，并学习到超越基线方法的可迁移多模态表征。综合而言，这些结果确立了BrainWorld作为面向长时域脑动态建模与多模态表征学习的条件感知生成框架。