He Bin, Cao Long, Xia Xiaoluan, Zhang Baogui, Zhang Dan, You Bo, Fan Lingzhong, Jiang Tianzi
School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Neurosci Bull. 2020 Dec;36(12):1454-1473. doi: 10.1007/s12264-020-00589-1. Epub 2020 Oct 27.
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.
额极皮质（FPC）在各种高级功能中发挥关键作用，并且在非人类灵长类动物中高度发达。一个至关重要的缺失信息是，相较于先前示踪剂研究结果，猕猴FPC更精细分区的详细解剖连接情况。这对于理解大脑皮质的功能结构很重要。在此，我们结合交叉验证和主成分分析，形成了一种基于纤维束成像的分区方案，该方案应用机器学习算法，使用9.4T布鲁克系统的高分辨率扩散磁共振成像，将猕猴FPC（2只雄性和6只雌性）划分为八个子区域，然后揭示它们的区域间连接。此外，我们对获得的脑区应用改进的层次聚类来探究这些子区域的模块化结构，发现包含向内侧FPC延伸部分的背外侧FPC主要与默认模式网络的区域相连。腹侧FPC主要参与社交互动网络，而背侧FPC参与元认知网络。这些结果增强了我们对猕猴大脑解剖结构和神经回路的理解，并有助于与FPC相关的临床研究。
