Estimating the direction of functional connectivity (FC) can help further elucidate complex brain function. However, the estimation of directed FC at the voxel level in fMRI data, and evaluating its performance, has yet to be done. We therefore developed a novel directed seed-based connectivity analysis (SCA) method based on normalized pairwise Granger causality that provides greater detail and accuracy over ROI-based methods. We evaluated its performance against 145 cortical retrograde tracer injections in male and female marmosets that were used as ground truth cellular connectivity on a voxel-by-voxel basis. The receiver operating characteristic (ROC) curve was calculated for each injection, and we achieved area under the ROC curve of 0.95 for undirected and 0.942 for directed SCA in the case of high cell count threshold. This indicates that SCA can reliably estimate the strong cellular connections between voxels in fMRI data. We then used our directed SCA method to analyze the human default mode network (DMN) and found that dlPFC (dorsolateral prefrontal cortex) and temporal lobe were separated from other DMN regions, forming part of the language-network that works together with the core DMN regions. We also found that the cerebellum (Crus I-II) was strongly targeted by the posterior parietal cortices and dlPFC, but reciprocal connections were not observed. Thus, the cerebellum may not be a part of, but instead a target of, the DMN and language-network. Summarily, our novel directed SCA method, visualized with a new functional flat mapping technique, opens a new paradigm for whole-brain functional analysis.

估计功能连接 （FC） 的方向有助于进一步阐明复杂的大脑功能。然而，在 fMRI 数据中体素水平上估计定向 FC 并评估其性能尚未完成。因此，我们开发了一种基于归一化成对 Granger 因果关系的新型基于定向种子的连接分析 （SCA） 方法，该方法比基于 ROI 的方法提供了更多的细节和准确性。我们评估了它对雄性和雌性狨猴的 145 次皮质逆行示踪剂注射的性能，这些示踪剂在体素的基础上用作地面真实细胞连接。计算每次注射的受试者工作特征 （ROC） 曲线，在细胞计数阈值高的情况下，我们实现了非定向 SCA 的 ROC 曲线下面积为 0.95，定向 SCA 的 ROC 曲线下面积为 0.942。这表明 SCA 可以可靠地估计 fMRI 数据中体素之间的强细胞连接。然后，我们使用定向 SCA 方法分析人类默认模式网络 （DMN），发现 dlPFC（背外侧前额叶皮层）和颞叶与其他 DMN 区域分离，形成与核心 DMN 区域一起工作的语言网络的一部分。我们还发现小脑 （Crus I-II） 强烈靶向后顶叶皮层和 dlPFC，但没有观察到相互连接。因此，小脑可能不是 DMN 和语言网络的一部分，而是 DMN 和语言网络的目标。总之，我们新颖的定向 SCA 方法，使用新的功能平面映射技术进行可视化，为全脑功能分析开辟了新的范式。