面向高分辨率大脑动态的时空神经帧建模
Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic
将EEG作为条件信号、以零空间中间帧重建处理不规则采样，把fMRI重建为皮层顶点级连续序列；这套「条件生成+不规则采样补全」思路可迁移到其他跨模态医学时序重建与视觉解码任务。
原文依据 we incorporate a null-space intermediate-frame reconstruction, enabling measurement-consistent completion of arbitrary intermediate frames
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论捕捉动态时空神经活动对于理解大规模大脑机制至关重要。功能磁共振成像（fMRI）提供高分辨率皮层表征，为刻画细粒度大脑活动模式奠定了坚实基础。fMRI 的高采集成本限制了其大规模应用，因此高质量 fMRI 重建成为一项关键任务。脑电图（EEG）提供毫秒级时序线索，可与 fMRI 形成互补。利用这种互补性，我们提出一个以 EEG 为条件的框架，在皮层顶点级别将动态 fMRI 重建为具有高空间保真度和强时序连贯性的连续神经序列。为应对真实 fMRI 采集中常见的采样不规则问题，我们引入零空间中间帧重建，从而实现对任意中间帧的测量一致补全，并提升序列连续性与实际适用性。在 CineBrain 数据集上的实验表明，该方法在全脑及功能特定区域均取得更优的体素级重建质量与稳健的时序一致性。重建的 fMRI 还保留了关键功能信息，可支撑下游视觉解码任务。本工作为从 EEG 估计高分辨率 fMRI 动态提供了新途径，并推动多模态神经影像走向更具动态性的大脑活动建模。
英文原文摘要 1467 字符
Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (fMRI) provides high-resolution cortical representations that form a strong basis for characterizing fine-grained brain activity patterns. The high acquisition cost of fMRI limits large-scale applications, therefore making high-quality fMRI reconstruction a crucial task. Electroencephalography (EEG) offers millisecond-level temporal cues that complement fMRI. Leveraging this complementarity, we present an EEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and strong temporal coherence at the cortical-vertex level. To address sampling irregularities common in real fMRI acquisitions, we incorporate a null-space intermediate-frame reconstruction, enabling measurement-consistent completion of arbitrary intermediate frames and improving sequence continuity and practical applicability. Experiments on the CineBrain dataset demonstrate superior voxel-wise reconstruction quality and robust temporal consistency across whole-brain and functionally specific regions. The reconstructed fMRI also preserves essential functional information, supporting downstream visual decoding tasks. This work provides a new pathway for estimating high-resolution fMRI dynamics from EEG and advances multimodal neuroimaging toward more dynamic brain activity modeling.