Understanding the rat neurochemical connectome is fundamental for exploring neuronal information processing. By using advanced data mining, supervised machine learning, and network analysis, this study integrates over 5 decades of neuroanatomical investigations into a multiscale, multilayer neurochemical connectome of the rat brain. This neurochemical connectivity database (ChemNetDB) is supported by comprehensive systematically-determined receptor distribution maps. The rat connectome has an onion-type structural organization and shares a number of structural features with mesoscale connectomes of mouse and macaque. Furthermore, we demonstrate that extremal values of graph theoretical measures (e.g., degree and betweenness) are associated with evolutionary-conserved deep brain structures such as amygdala, bed nucleus of the stria terminalis, dorsal raphe, and lateral hypothalamus, which regulate primitive, yet fundamental functions, such as circadian rhythms, reward, aggression, anxiety, and fear. The ChemNetDB is a freely available resource for systems analysis of motor, sensory, emotional, and cognitive information processing.

了解大鼠神经化学连接体是探索神经信息处理的基础。通过使用高级数据挖掘，有监督的机器学习和网络分析，该研究将超过5年的神经解剖学研究整合到了大鼠大脑的多尺度，多层神经化学连接体中。该神经化学连通性数据库（ChemNetDB）得到全面系统确定的受体分布图的支持。大鼠连接体具有洋葱型的结构组织，并且与小鼠和猕猴的中尺度连接体具有许多结构特征。此外，我们证明了图理论量度（例如，度数和中间度）的极值与进化保守的深层大脑结构（如杏仁核，纹状体床核，背缝和下丘脑外侧，调节原始但基本的功能，例如昼夜节律，奖赏，攻击，焦虑和恐惧。ChemNetDB是可免费获得的资源，用于运动，感觉，情感和认知信息处理的系统分析。