Speaker
Description
Understanding the underlying mechanisms of Autism Spectrum Disorder (ASD) requires detailed analysis of behaviour in model organisms. Here, we present a complete image analysis pipeline designed to analyze ASD-like behaviours in Drosophila, providing insights into the associated mechanisms.
Our pipeline begins with the acquisition of high-resolution, large-format videos capturing Drosophila behaviour. We employ advanced tracking algorithms to accurately trace the movement of individual flies, followed by rigorous post-processing steps to clean and refine the tracking data. From this, we extract precise trajectories and positional information for each fly.
Subsequently, we calculate key behavioural metrics such as locomotion patterns and social distances. Further analysis explores specific behaviours pertinent to ASD research, such as repetitive behaviour (grooming) and comorbidities, including aggression. By quantifying these behavioural outputs, our pipeline offers robust tools for recording and analyzing in depth a spectrum of different ASD-like behaviours.
This comprehensive approach not only facilitates high-throughput analysis but also enhances the reliability and depth of behavioural studies in Drosophila, enabling researchers to draw more nuanced conclusions about the underlying mechanisms in health and disease.
| Authors | Arianna Ravera*, Kyriaki Foka, Amelien Goossens, Tilmann Achsel, Claudia Bagni |
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| Keywords | Image Analysis, Drosophila Melanogaster, Behavioural Analysis, Artificial Intelligence |
