The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.

额叶皮质（FPC）在各种高阶功能中起着关键作用，并且在非人类灵长类动物中高度发达。缺少的一项重要信息是与以前的示踪剂结果相比，猕猴FPC的细微分割的详细解剖连接。这对于理解大脑皮层的功能结构很重要。在这里，结合交叉验证和主成分分析，我们形成了一个基于谱学的分割方案，该方案应用了机器学习算法，通过高分辨率扩散磁共振成像将猕猴FPC（2个雄性和6个雌性）划分为8个子区域。 9.4T布鲁克系统，然后揭示了它们的次区域联系。此外，我们对获得的地块应用改进的层次聚类，以探查子区域的模块结构，发现包含内侧FPC扩展的背外侧FPC主要连接到默认模式网络的区域。腹侧FPC主要参与社交互动网络，而背侧FPC主要参与元认知网络。这些结果增强了我们对猕猴大脑的解剖结构和电路的理解，并有助于FPC相关的临床研究。