时空神经帧建模用于高分辨率脑动态研究
Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic
AI总结：
本文提出一种基于EEG条件的框架，用于高保真重建动态fMRI，通过引入空域中间帧重建解决采样不规则问题，提升时空连续性与应用性。
摘要：
捕捉动态时空神经活动对于理解大规模脑机制至关重要。功能性磁共振成像（fMRI）提供高分辨率皮层表示，是表征精细脑活动模式的基础。然而，fMRI的高获取成本限制了大规模应用，因此高质fMRI重建成为关键任务。EEG提供毫秒级时间线索，可补充fMRI。本文提出EEG条件框架，用于重建动态fMRI为连续神经序列，具有高空间保真度和强时间一致性。为解决真实fMRI采集中的采样不规则问题，我们引入空域中间帧重建，使任意中间帧测量一致完成，提升序列连续性和实用性。在CineBrain数据集上的实验显示，重建的fMRI在全脑和功能特定区域具有优越的体素重建质量和稳健的时间一致性。重建的fMRI也保留了必要的功能信息，支持下游视觉解码任务。本文为从EEG估计高分辨率fMRI动态提供了新路径，推动多模态神经成像向更动态的脑活动建模发展。
英文摘要：
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.
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