Integrated Behavior Quantification System for the Measurement of Movement Kinematics and Neural Activity in the String Pulling Task

Abstract

There are many behaviors used in the study of motor disorders. Simple ones like the rotarod test are easy to implement and give a single measure of behavior. Others, like the center-out task, skilled reaching task, vermicelli handling test, and string pulling task are more complex. These often require manual segmentation of movement to identify motor patterns. We have developed an integrated hardware and software system capable of automatically controlling and quantifying behavior in the string pulling motor task. This system also integrates with neural recording systems to allow for the syncing of behavioral states to neural responses. Our system also streamlines the training of this behavior by allowing the automated shaping of animals. Key elements of the hardware included 3D-printed parts, a high-speed camera, a rotary encoder, an Arduino microcontroller, and custom circuits. Open-source deep-learning software (DeepLabCut) was used to record paw and head movements and custom software used this information to further categorize the phases and segments of each pull. Using this system, we were able to identify 97 neurons that were significantly responsive to right paw motion, 83 neurons that were significantly responsive to left paw motion, and others that were responsive to other components of the string pulling task. This system has applications in the study of motor control, motivation, and motor disorders such as Parkinson's Disease, Huntington's Disease, and strokes.