用解剖结构引导生成全脑4D fMRI动态，实现长时序预测。
BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
- 以sMRI为先验条件，融入去噪过程指导fMRI生成。
- 在22个数据集上生成长达400帧的稳定动态轨迹。
- 适合研究脑功能建模与多模态表示学习的科研人员。
全脑4D fMRI生成对建模脑功能动态具有重要价值，但现有基础模型主要聚焦表征学习和下游预测，而非条件生成。本文提出BrainWorld，一种基于结构先验的全脑4D fMRI生成模型。该模型利用sMRI作为受试者级解剖上下文，指导未来fMRI生成，将结构信息整合到去噪过程中，而非将其视为并行模态。在覆盖多样人群与脑状态的22个数据集上评估，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，并学习出优于基线的可迁移多模态表征。结果表明，BrainWorld是长时序脑动态建模与多模态表示学习的条件感知生成框架。
原文摘要 · Abstract (English)
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.
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