NeuroWorld：用于刺激条件下人脑动力学的潜在脑世界模型
NeuroWorld: A Latent Brain World Model for Stimulus-Conditioned Human Brain Dynamics
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中文总结 AI 辅助
研究针对现有脑编码模型的时间约束缺陷，提出首个脑世界模型NeuroWorld，通过两阶段方法在三个fMRI基准上实现了更优的脑活动多步预测性能，为脑活动因果预测提供了新框架。
中文摘要 AI 辅助
在自然体验过程中预测人脑活动，需要建模内源性神经状态在连续感官驱动下的因果演化。现有脑编码模型将其视为刺激到响应的回归，无严格时间约束，允许未来刺激泄露到当前预测中。我们提出NeuroWorld，据我们所知是首个脑世界模型，将自然脑功能动力学预测转化为学习到的潜在脑状态空间中的刺激条件演化，在两个阶段将内源性状态（通过fMRI测量）与外源性多模态刺激分离。潜在动力学学习（LDL）通过下一个潜在预测联合学习转换充分表示和因果动力学，无需重建观测到的fMRI信号。潜在展开解码（LRD）冻结LDL，从观测到的fMRI前缀自回归地将潜在状态向前展开，并将其解码为受试者特异性全脑响应。在三个涵盖30名受试者的自然电影fMRI基准测试中，包括我们新收集的新加坡多模态成像与自然数据集（SG-MIND；20名受试者，8519对刺激-响应片段，140.7人时观看时长），NeuroWorld在严格因果刺激访问下实现了最先进的多步展开性能，对长程自回归漂移具有更强的鲁棒性，支持扩展脑状态轨迹的可靠模拟。大量可解释性分析表征了所学动力学的功能组织，确立了潜在空间世界建模作为人脑活动因果预测的原则性框架。
英文摘要
Forecasting human brain activity during naturalistic experience requires modeling how endogenous neural states evolve causally under continuous sensory drive. Existing brain encoding models instead frame this as stimulus-to-response regression without strict temporal constraints, allowing future stimuli to leak into current predictions. We introduce NeuroWorld, to our knowledge the first brain world model, which casts naturalistic brain functional dynamics prediction as stimulus-conditioned evolution in a learned latent brain-state space, separating endogenous states (measured via fMRI) from exogenous multimodal stimuli across two stages. Latent Dynamics Learning (LDL) jointly learns a transition-sufficient representation and causal dynamics through next-latent prediction, without reconstructing the observed fMRI signal. Latent Rollout Decoding (LRD) freezes LDL, autoregressively rolls latent states forward from an observed fMRI prefix, and decodes them into subject-specific whole-brain responses. Across three naturalistic movie-fMRI benchmarks spanning 30 participants, including our newly collected Singapore Multimodal Imaging & Naturalistic Dataset (SG-MIND; 20 participants, 8,519 paired stimulus-response clips, 140.7 person-hours of viewing), NeuroWorld achieves state-of-the-art multi-step rollout performance under strictly causal stimulus access, with greater robustness to long-horizon autoregressive drift, supporting reliable simulation of extended brain-state trajectories. Extensive interpretability analyses characterize the functional organization of the learned dynamics, establishing latent-space world modeling as a principled framework for causal forecasting of human brain activity.
