FAST-Brain：一种流对齐的时空代理脑模型
FAST-Brain: A Flow-Aligned Spatio-Temporal Surrogate Brain Model
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中文总结 AI 辅助
FAST-Brain提出流对齐时空代理模型，结合图卷积与Transformer，直接预测BOLD信号，在低维子空间假设下误差随内在维度缩放，实验证明其在恢复功能连接等方面达到最先进性能。
中文摘要 AI 辅助
对静息态功能磁共振成像（rs-fMRI）数据进行建模对于理解全脑神经活动至关重要。然而，传统方法难以捕捉长时间范围内的复杂时间动态，难以考虑大脑的解剖空间结构，也难以对位于低维内在子空间上的高维环境信号进行建模。我们提出了FAST-Brain，一种统一的流对齐时空代理脑模型，以解决这三个挑战。其核心是一个流对齐生成框架，直接预测干净的血液氧合水平依赖（BOLD）信号，并配有一个图卷积网络来捕捉空间结构约束，以及一个Transformer来建模长距离时间依赖。理论上，我们表明在低维子空间假设下，我们模型的近似误差随内在维度而非环境维度缩放，这证明了我们对BOLD信号直接建模的合理性。在合成数据集和人类连接组计划数据集上的大量实验表明，FAST-Brain在恢复功能连接、有效连接和隐式低维信号子空间方面达到了最先进的性能。
英文摘要
Modeling resting-state functional magnetic resonance imaging (rs-fMRI) data is crucial for understanding brain-wide neural activity. However, traditional methods struggle to capture complex temporal dynamics over long horizons, to account for the brain's anatomical spatial structure, and to model high-dimensional ambient signals that lie on a low-dimensional intrinsic subspace. We propose FAST-Brain, a unified flow-aligned spatio-temporal surrogate brain model that addresses all three challenges. At its core is a flow-aligned generative framework that directly predicts the clean blood-oxygen-level-dependent (BOLD) signal, paired with a graph convolutional network that captures spatial structural constraints and a Transformer that models long-range temporal dependencies. Theoretically, we show that under a low-dimensional subspace assumption, the approximation error of our model scales with the intrinsic dimension rather than the ambient dimension, which justifies our direct modeling of the BOLD signal. Extensive experiments on synthetic and Human Connectome Project datasets demonstrate that FAST-Brain achieves state-of-the-art performance in recovering functional connectivity, effective connectivity, and the implicit low-dimensional signal subspace.
