Designing a comprehensive four-dimensional resting-state functional magnetic resonance imaging (4D Rs-fMRI) based default mode network (DMN) modeling methodology to reveal the spatio-temporal patterns of individual DMN, is crucial for understanding the cognitive mechanisms of the brain and the pathogenesis of psychiatric disorders. However, there are still two limitations of existing approaches for DMN modeling. The approaches either (1) simply split the spatio-temporal components and ignore the overall character of the spatio-temporal patterns or (2) are biased in the process of feature extraction for DMN modeling, and their spatio-temporal accuracy is thus not warranted. To this end, we propose a novel Spatio-Temporal Brain Attention Skip Network (STBAS-Net) to model the personalized spatio-temporal patterns of the DMN. STBAS-Net consists of spatial and temporal components, where the multi-head attention skip connection block in the spatial component achieves detailed feature extraction and enhancement in the shallow stage. Under the guidance of spatial information, we technically fuse multiple spatio-temporal information in the temporal component, which dexterously exploits the overall spatio-temporal features and achieves mutual constraints of spatio-temporal patterns to characterize the spatio-temporal patterns of the DMN. We verify the proposed STBAS-Net on a publicly released 4D Rs-fMRI dataset and an EMCI dataset. The experimental results show that compared with existing advanced methods, the proposed network can more accurately model the personalized spatio-temporal patterns of the human brain DMN and successfully identify abnormal spatio-temporal patterns in EMCI patients. This study provides a potential tool for revealing the spatio-temporal patterns of the human brain DMN and is expected to provide an effective methodological framework for future exploration of abnormal brain spatio-temporal patterns and modeling of other functional brain networks.

设计基于默认模式网络（DMN）的全面四维静息态功能磁共振成像（4D Rs-fMRI）建模方法来揭示个体DMN的时空模式，对于理解大脑的认知机制和精神疾病的发病机制。然而，现有的 DMN 建模方法仍然存在两个局限性。这些方法要么（1）简单地分割时空分量而忽略时空模式的整体特征，要么（2）在 DMN 建模的特征提取过程中存在偏差，因此无法保证其时空准确性。为此，我们提出了一种新颖的时空大脑注意力跳跃网络（STBAS-Net）来建模 DMN 的个性化时空模式。STBAS-Net由空间和时间组件组成，其中空间组件中的多头注意力跳跃连接块实现了浅层阶段的详细特征提取和增强。在空间信息的指导下，我们在时间分量中技术性地融合了多种时空信息，巧妙地利用整体时空特征，实现时空模式的相互约束，来表征DMN的时空模式。我们在公开发布的 4D Rs-fMRI 数据集和 EMCI 数据集上验证了所提出的 STBAS-Net。实验结果表明，与现有的先进方法相比，所提出的网络可以更准确地模拟人脑DMN的个性化时空模式，并成功识别EMCI患者的异常时空模式。这项研究为揭示人脑DMN的时空模式提供了一个潜在的工具，并有望为未来探索异常大脑时空模式和其他功能脑网络的建模提供有效的方法框架。