The way in which the central nervous system (CNS) governs animal movement is complex and difficult to solve solely by the analyses of muscle movement patterns. We tackle this problem by observing the activity of a large population of neurons in the CNS of larval Drosophila. We focused on two major behaviors of the larvae – forward and backward locomotion – and analyzed the neuronal activity related to these behaviors during the fictive locomotion that occurs spontaneously in the isolated CNS. We expressed a genetically-encoded calcium indicator, GCaMP and a nuclear marker in all neurons and then used digitally scanned light-sheet microscopy to record (at a fast frame rate) neural activities in the entire ventral nerve cord (VNC). We developed image processing tools that automatically detected the cell position based on the nuclear staining and allocate the activity signals to each detected cell. We also applied a machine learning-based method that we recently developed to assign motor status in each time frame. Our experimental procedures and computational pipeline enabled systematic identification of neurons that showed characteristic motor activities in larval Drosophila. We found cells whose activity was biased toward forward locomotion and others biased toward backward locomotion. In particular, we identified neurons near the boundary of the subesophageal zone (SEZ) and thoracic neuromeres, which were strongly active during an early phase of backward but not forward fictive locomotion.

中枢神经系统（CNS）控制动物运动的方式是复杂的，仅通过分析肌肉运动模式很难解决。我们通过观察果蝇幼虫中枢神经系统中大量神经元的活动来解决这个问题。我们关注幼虫的两种主要行为-向前和向后运动-并分析了在孤立的CNS中自然发生的虚拟运动期间与这些行为相关的神经元活动。我们在所有神经元中表达了一种基因编码的钙指示剂，GCaMP和一个核标记，然后使用数字扫描光片显微镜记录（以快速帧速率）整个腹侧神经索（VNC）的神经活动。我们开发了图像处理工具，可以根据核染色自动检测细胞位置，并将活动信号分配给每个检测到的细胞。我们还应用了最近开发的基于机器学习的方法，以在每个时间范围内分配电机状态。果蝇。我们发现其活动偏向向前运动而其他偏向向后运动的细胞。尤其是，我们确定了食管下区域（SEZ）和胸神经绒毛边界附近的神经元，它们在向后的虚构运动而不是向前的虚构运动的早期阶段都非常活跃。