Abstract
Facial paralysis is characterized by an injury to the facial nerve, causing the loss of the functions of the structures that it innervates, as well as changes in the motor cortex. Current models have some limitations for the study of facial paralysis, such as movement restriction, the absence of studying awake animals in behavioral contexts, and the lack of a model that fully evaluates facial movements. The development of an algorithm capable of automatically inferring facial paralysis and overcoming the existing limitations is proposed in this work. In C57/BL6J mice, we produced both irreversible and reversible facial paralysis. Video recordings were made of the faces of paralyzed mice to develop an algorithm for detecting facial paralysis applied to mice, which allows us to predict the presence of reversible and irreversible facial paralysis automatically. At the same time, the algorithm was used to track facial movement during gustatory stimulation and extracellular electrophysiological recordings in the anterolateral motor cortex (ALM). In the basal state, mice can make facial expressions, whereas the algorithm can detect this movement. Simultaneously, such movement is correlated with the activation in the ALM. In the presence of facial paralysis, the algorithm cannot detect movement. Furthermore, it predicts that the condition exists, and the neuronal activity in the cortex is affected with respect to the evolution of facial paralysis. This way, we conclude that the facial paralysis algorithm applied to mice allows for inferring the presence of experimental facial paralysis and its neuronal correlates for further studies.