Face processing supports our ability to recognize friend from foe, form tribes and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions, but how these regions interact is poorly understood. Here we integrate anatomical and functional connectivity measurements with behavioural assays to create a global model of the face connectome. We dissect key features, such as the network topology and fibre composition. We propose a neurocognitive model with three core streams; face processing along these streams occurs in a parallel and reciprocal manner. Although long-range fibre paths are important, the face network is dominated by short-range fibres. Finally, we provide evidence that the well-known right lateralization of face processing arises from imbalanced intra- and interhemispheric connections. In summary, the face network relies on dynamic communication across highly structured fibre tracts, enabling coherent face processing that underpins behaviour and cognition.

面部处理支持我们识别朋友和敌人、形成部落以及理解面部肌肉组织变化的情感影响的能力。这项技能依赖于大脑区域的分布式网络，但人们对这些区域如何相互作用知之甚少。在这里，我们将解剖学和功能连接测量与行为分析相结合，创建面部连接组的全局模型。我们剖析了网络拓扑和光纤组成等关键特征。我们提出了一个具有三个核心流的神经认知模型；沿着这些流的面部处理以并行和交互的方式发生。尽管长距离光纤路径很重要，但面网以短距离光纤为主。最后，我们提供的证据表明，众所周知的面部处理的右侧化是由不平衡的半球内和半球间连接引起的。总之，面部网络依赖于高度结构化的纤维束之间的动态通信，从而实现支持行为和认知的连贯面部处理。