Variation and Variability in Drosophila Grooming Behavior

Behavioral differences can be observed between species or populations (variation) or between individuals in a genetically similar population (variability). Here, we investigate genetic differences as a possible source of variation and variability in Drosophila grooming. Grooming confers survival and social benefits. Grooming features of five Drosophila species exposed to a dust irritant were analyzed. Aspects of grooming behavior, such as anterior to posterior progression, were conserved between and within species. However, significant differences in activity levels, proportion of time spent in different cleaning movements, and grooming syntax were identified between species. All species tested showed individual variability in the order and duration of action sequences. Genetic diversity was not found to correlate with grooming variability within a species: melanogaster flies bred to increase or decrease genetic heterogeneity exhibited similar variability in grooming syntax. Individual flies observed on consecutive days also showed grooming sequence variability. Standardization of sensory input using optogenetics reduced but did not eliminate this variability. In aggregate, these data suggest that sequence variability may be a conserved feature of grooming behavior itself. These results also demonstrate that large genetic differences result in distinguishable grooming phenotypes (variation), but that genetic heterogeneity within a population does not necessarily correspond to an increase in the range of grooming behavior (variability).

Environmental Enrichment Ameliorates Psychological Dependence Symptoms and Voluntary Morphine Consumption in Morphine Withdrawn Rats Under Methadone Maintenance Treatment

Introduction: Previous studies have shown that physical and psychological dependence and the vulnerability to relapse are still present during MMT. Thus, this study examined whether Enriched Environment (EE) would attenuate anxiety, depressive, and obsessive-compulsive-like behaviors, as well as voluntary morphine consumption following Methadone Maintenance Treatment (MMT) in morphine withdrawn rats. Methods: The rats were injected bi-daily doses (10 mg/kg, 12-h interval) of morphine for 14 days. Then, the rats were reared in a Standard Environment (SE) or EE for 30 more days during morphine withdrawal, simultaneous with receiving MMT. The rats were tested for anxiety (the Elevated Plus Maze [EPM]) and depression (Sucrose Preference Test [SPT]), Obsessive-Compulsive Disorder (OCD) as grooming behavior, and voluntary morphine consumption using a Two-Bottle Choice (TBC) paradigm. Results: The findings revealed that EE experience in morphine withdrawn rats under MMT significantly increased the EPM open-arm time and higher sucrose preference than SE rats. Also, we found that the EE decreased the self-grooming behavior and morphine preference ratio in morphine withdrawn rats receiving MMT compared to the SE group. Conclusion: We conclude that exposure to EE decreased methadone-induced anxiety, depressive and OCD-like behaviors, and voluntary morphine consumption in morphine withdrawn rats under MMT. Thus, the EE seems to be one of the strategies for reducing MMT-induced behavioral dysfunction and the risk of relapse induced by morphine withdrawal.

Key Points Tracking and Grooming Behavior Recognition of Bactrocera minax (Diptera: Trypetidae) via DeepLabCut

Statistical analysis of Bactrocera grooming behavior is important for pest control and human health. Based on DeepLabCut, this study proposes a noninvasive and effective method to track the key points of Bactrocera minax and to detect and analyze its grooming behavior. The results are analyzed and calculated automatically by a computer program. Traditional movement tracking methods are invasive; for instance, the use of artificial pheromone may affect the behavior of Bactrocera minax, thus directly affecting the accuracy and reliability of experimental results. Traditional research studies mainly rely on manual work for behavior analysis and statistics. Researchers need to play the video frame by frame and record the time interval of each grooming behavior manually, which is time-consuming, laborious, and inaccurate. So the advantages of automated analysis are obvious. Using the method proposed in this paper, the image data of 94538 frames from 5 adult Bactrocera were analyzed and 14 key points were tracked. The overall tracking accuracy was as high as 96.7%. In the behavior analysis and statistics, the average accuracy rate of the five grooming behavior was all above 96%, and the accuracy rate of the remaining two grooming behavior was over 87%. The experimental results show that the automatic noninvasive method designed in this paper can track many key points of Bactrocera minax with high accuracy and ensure the accuracy of insect behavior recognition and analysis, which greatly reduces the manual observation time and provides a new method for key points tracking and behavior recognition of related insects.

Action detection using a neural network elucidates the genetics of mouse grooming behavior

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms.