Automated Tracking of Drosophila Specimens

An automatic system for recognizing fly courtship patterns via an image processing method

Fruit fly courtship behaviors composed of a series of actions have always been an important model for behavioral research. While most related studies have focused only on total courtship behaviors, specific courtship elements have often been underestimated. Identifying these courtship element details is extremely labor intensive and would largely benefit from an automatic recognition system. To address this issue, in this study, we established a vision-based fly courtship behavior recognition system. The system based on the proposed image processing methods can precisely distinguish body parts such as the head, thorax, and abdomen and automatically recognize specific courtship elements, including orientation, singing, attempted copulation, copulation and tapping, which was not detectable in previous studies. This system, which has high identity tracking accuracy (99.99%) and high behavioral element recognition rates (> 97.35%), can ensure correct identification even when flies completely overlap. Using this newly developed system, we investigated the total courtship time, and proportion, and transition of courtship elements in flies across different ages and found that male flies adjusted their courtship strategy in response to their physical condition. We also identified differences in courtship patterns between males with and without successful copulation. Our study therefore demonstrated how image processing methods can be applied to automatically recognize complex animal behaviors. The newly developed system will largely help us investigate the details of fly courtship in future research.

Automating behavioral analysis in neuroscience: Development of an open-source python software for more consistent and reliable results

BACKGROUND

The application of automated analyses in neuroscience has become a practical approach. With automation, the algorithms and tools employed perform fast and accurate data analysis. It minimizes the inherent errors of manual analysis performed by a human experimenter. It also reduces the time required to analyze a large amount of data and the need for human and financial resources.

METHODS

In this work, we describe a protocol for the automated analysis of the Morris Water Maze (MWM) and the Open Field (OF) test using the OpenCV library in Python. This simple protocol tracks mice navigation with high accuracy.

RESULTS

In the MWM, both automated and manual analysis revealed similar results regarding the time the mice stayed in the target quadrant (p = 0.109). In the OF test, both automated and manual analysis revealed similar results regarding the time the mice stayed in the center (p = 0.520) and in the border (p = 0.503) of the field.

CONCLUSIONS

The automated analysis protocol has several advantages over manual analysis. It saves time, reduces human errors, can be customized, and provides more consistent information about animal behavior during tests. We conclude that the automated protocol described here is reliable and provides consistent behavioral analysis in mice. This automated protocol could lead to deeper insight into behavioral neuroscience.

Analysis of Visual Collision Avoidance in Xenopus Tadpoles

In teaching, the best exam questions are those that seem simple at first but can lead to deep and nuanced conversations. Similarly, to probe brain development, we should look for behaviors that are easy to evoke and quantify, but that are demanding, malleable, and inherently variable. Visual collision avoidance is an example of such a behavior; it is ecologically relevant, robust, and easy to record, but also nuanced and shaped by the sensory history of the animal. Here we describe how to set up a visual avoidance assay and how to use it to test sensory processing and sensorimotor transformations in the vertebrate brain.

A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila

Isolation profoundly influences social behavior in all animals. In humans, isolation has serious effects on health. Drosophila melanogaster is a powerful model to study small-scale, temporally-transient social behavior. However, longer-term analysis of large groups of flies is hampered by the lack of effective and reliable tools. We built a new imaging arena and improved the existing tracking algorithm to reliably follow a large number of flies simultaneously. Next, based on the automatic classification of touch and graph-based social network analysis, we designed an algorithm to quantify changes in the social network in response to prior social isolation. We observed that isolation significantly and swiftly enhanced individual and local social network parameters depicting near-neighbor relationships. We explored the genome-wide molecular correlates of these behavioral changes and found that whereas behavior changed throughout the six days of isolation, gene expression alterations occurred largely on day one. These changes occurred mostly in metabolic genes, and we verified the metabolic changes by showing an increase of lipid content in isolated flies. In summary, we describe a highly reliable tracking and analysis pipeline for large groups of flies that we use to unravel the behavioral, molecular and physiological impact of isolation on social network dynamics in Drosophila.

Social isolation severely affects the behavior and physiology of social animals, including humans. The fruit fly is a powerful model for studying the mechanisms of development, health and disease and is also used to study social behaviors such as mating and aggression. However, these studies are limited to examining few individuals for shorts amounts of time, due to the lack of effective computational tools for the analysis of large groups over prolonged time. To overcome this hurdle, we built a new behavioral arena and developed new software that accurately tracks many flies simultaneously over long time periods. The arena is cheap and easy to build and the software works with low resolution videos. Using these improved tools, we studied social isolation in groups of male flies. We found that isolation caused flies to form stronger interactions with neighboring flies in their social network. These behavioral changes were preceded by transient changes in the expression of metabolism genes and eventually resulted in isolated flies accumulating fat, as has been previously observed in studies in mice and humans. Our study opens the door for the use of fruit flies in future studies of social isolation.