What is my rat doing? Behavior understanding of laboratory animals

Analixity: An open source, low-cost analysis system for the elevated plus maze test, based on computer vision techniques

Manual analysis of behavioral tests in rodents involves inspection of video recordings by a researcher that assesses rodent movements to quantify parameters related with a behavior of interest. The assessment of the researcher during the quantification of such parameters can introduce variability among experimental conditions or among sessions of analysis. Here, we introduce Analixity, a video processing software for the elevated plus maze test (EPM), in which quantification of behavioral parameters is automatic, reducing the time spent in analysis and solving the variability problem. Analixity is an adaptable multiplatform open-source system. Analixity generates an Excel file with the quantified behavioral variables, such as time spent in open and closed arms and in the center zone, number of entries to each zone and total distance traveled during the test. For validation, we compared results obtained by Analixity with results obtained by manual analysis. We did not find statistically significant differences. In addition, we compared the results obtained by Analixity with results obtained by the commercial software ANY-maze. We did not find statistically significant differences in the quantification of parameters such as time spent in open arms, time spent in closed arms, time spent in center zone, number of closed arms, open arms entries, and anxiety index. We concluded that Analixity is an open-source software as reliable and effective as a commercial software.

Pharmaco-electroencephalographic responses in the rat differ between active and inactive locomotor states

Quantitative electroencephalography from freely moving rats is commonly used as a translational tool for predicting drug‐effects in humans. We hypothesized that drug‐effects may be expressed differently depending on whether the rat is in active locomotion or sitting still during recording sessions, and proposed automatic state‐detection as a viable tool for estimating drug‐effects free of hypo‐/hyperlocomotion‐induced effects. We aimed at developing a fully automatic and validated method for detecting two behavioural states: active and inactive, in one‐second intervals and to use the method for evaluating ketamine, DOI, d‐cycloserine, d‐amphetamine, and diazepam effects specifically within each state. The developed state‐detector attained high precision with more than 90% of the detected time correctly classified, and multiple differences between the two detected states were discovered. Ketamine‐induced delta activity was found specifically related to locomotion. Ketamine and DOI suppressed theta and beta oscillations exclusively during inactivity. Characteristic gamma and high‐frequency oscillations (HFO) enhancements of the NMDAR and 5HT_2A modulators, speculated associated with locomotion, were profound and often largest during the inactive state. State‐specific analyses, theoretically eliminating biases from altered occurrence of locomotion, revealed only few effects of d‐amphetamine and diazepam. Overall, drug‐effects were most abundant in the inactive state. In conclusion, this new validated and automatic locomotion state‐detection method enables fast and reliable state‐specific analysis facilitating discovery of state‐dependent drug‐effects and control for altered occurrence of locomotion. This may ultimately lead to better cross‐species translation of electrophysiological effects of pharmacological modulations.

An automated behavior analysis system for freely moving rodents using depth image

A rodent behavior analysis system is presented, capable of automated tracking, pose estimation, and recognition of nine behaviors in freely moving animals. The system tracks three key points on the rodent body (nose, center of body, and base of tail) to estimate its pose and head rotation angle in real time. A support vector machine (SVM)-based model, including label optimization steps, is trained to classify on a frame-by-frame basis: resting, walking, bending, grooming, sniffing, rearing supported, rearing unsupported, micro-movements, and "other" behaviors. Compared to conventional red-green-blue (RGB) camera-based methods, the proposed system operates on 3D depth images provided by the Kinect infrared (IR) camera, enabling stable performance regardless of lighting conditions and animal color contrast with the background. This is particularly beneficial for monitoring nocturnal animals' behavior. 3D features are designed to be extracted directly from the depth stream and combined with contour-based 2D features to further improve recognition accuracies. The system is validated on three freely behaving rats for 168 min in total. The behavior recognition model achieved a cross-validation accuracy of 86.8% on the rat used for training and accuracies of 82.1 and 83% on the other two "testing" rats. The automated head angle estimation aided by behavior recognition resulted in 0.76 correlation with human expert annotation. Graphical abstract Top view of a rat freely behaving in a standard homecage, captured by Kinect-v2 sensors. The depth image is used for constructing a 3D topography of the animal for pose estimation, behavior recognition, and head angle calculation. Results of the processed data are displayed on the user interface in various forms.