Automated light-based motor mapping of multiple limb movements in mice using deep neural networks

Abstract

Recent developments in optogenetics allow for quick and minimally-invasive methods of studying functional brain organization in animal models. DeepLabCut (DLC), a toolbox for markerless pose estimation, offers the ability to track user-defined features in 3-dimensions with human level accuracy. We demonstrate a hybrid method utilizing DLC and optogenetic motor mapping to localize the movements of multiple modalities to the mouse cortex. We outline a pipeline to map and characterize multiple motor representations in anesthetized and awake mice. Furthermore, we identify behaviorally-relevant motor movements of multiple limbs reside in overlapping cortical representations of the respective limbs.