Automated Two-Chamber Operon ID/ED Task for Mice

Automation at the service of the study of executive functions in preclinical models

Cognitive flexibility involves the capability to switch between different perspectives and implement novel strategies upon changed circumstances. The Wisconsin Card Sorting Test (in humans) and the Attentional Set-Shifting Task (ASST, in rodents) evaluate individual capability to acquire a reward-associated rule and subsequently disregard it in favour of a new one. Both tasks entail consecutive stages wherein subjects discriminate between: two stimuli of a given category (simple discrimination, SD); the stimuli of SD confounded by an irrelevant stimulus of a different category (compound discrimination, CD); different stimuli belonging to the SD category (intradimensional shift, IDS); and two stimuli of the confounding category (extradimensional shift, EDS). The ASST is labour intensive, not sufficiently standardised, and prone to experimental error. Here, we tested the validity of a new, commercially available, automated version of ASST (OPERON) in two independent experiments conducted in: different mouse strains (C57BL/6 and CD1 mice) to confirm their differential cognitive capabilities (Experiment 1); and an experimental model of chronic stress (administration of corticosterone in the drinking water; Experiment 2). In both experiments, OPERON confirmed the findings obtained through the manual version. Just as in Experiment 1 both versions captured the deficit of C57BL/6 mice on the reversal of the CD (CDR), so also in Experiment 2 they provided analogous evidence that corticosterone treated mice have a remarkable impairment in the IDS. Thus, OPERON capitalises upon automated phenotyping to overcome the limitation of the manual version of the ASST while providing comparable results.

DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning

Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of laboratory animals, allowing animal behavior to be analyzed digitally. In vivo optical imaging and electrophysiological recording in freely behaving animals are now widely used to understand neural functions in circuits. However, it is always a challenge to accurately track the movement of an animal under certain complex conditions due to uneven environment illumination, variations in animal models, and interference from recording devices and experimenters. To overcome these challenges, we have developed a strategy to track the movement of an animal by combining a deep learning technique, the You Only Look Once (YOLO) algorithm, with a background subtraction algorithm, a method we label DeepBhvTracking. In our method, we first train the detector using manually labeled images and a pretrained deep-learning neural network combined with YOLO, then generate bounding boxes of the targets using the trained detector, and finally track the center of the targets by calculating their centroid in the bounding box using background subtraction. Using DeepBhvTracking, the movement of animals can be tracked accurately in complex environments and can be used in different behavior paradigms and for different animal models. Therefore, DeepBhvTracking can be broadly used in studies of neuroscience, medicine, and machine learning algorithms.