Yu, S.-y. | Search bioRxiv

bioRxiv2023

The modified elevated gap interaction test: A novel paradigm to assess social preference.

Social deficits play a role in numerous psychiatric, neurological and neurodevelopmental disorders. Relating complex behavior, such as social interaction, to brain activity remains one of the biggest goals and challenges in neuroscience. Availability of standardized behavioral tests that assess social preference is however, limited. Here, we present a novel behavioral set-up and paradigm that we developed to measure social behavior, the modified elevated gap interaction test (MEGIT). In this test, animals are placed on one of two elevated platforms separated by a gap, in which they can engage in whisker-interaction with either a conspecific or an object. This allows quantification of social preference in real interaction rather than just proximity and forms an ideal setup for social behavior-related neuronal recordings. We provide a detailed description of the paradigm and its highly reliable, deep-learning based analysis, and show test results obtained from wild-type animals as well as genetic mouse models for disorders characterized by either hyposocial (autism spectrum disorder; ASD) or hypersocial (Williams Beuren syndrome; WBS) behavior. Wild-type animals show a clear preference for whisker interaction with another mouse rather than an inanimate object. This preference proved to be significantly smaller in mice carrying a mutation that can cause ASD in humans, whereas it is larger in WBS murine models. Differences in social preference became even more pronounced when corrected for hyperactive or repetitive behavior. The results indicate that MEGIT is a sensitive and reliable test for detecting and investigating social phenotypes.

neuroscience↗

bioRxiv2020

OptiFlex: video-based animal pose estimation using deep learning enhanced by optical flow

Deep learning based animal pose estimation tools have greatly improved animal behaviour quantification. However, those tools all make predictions on individual video frames and do not account for variability of animal body shape in their model designs. Here, we introduce the first video-based animal pose estimation architecture, referred to as OptiFlex, which integrates a flexible base model to account for variability in animal body shape with an optical flow model to incorporate temporal context from nearby video frames. This approach can be combined with multi-view information, generating prediction enhancement using all four dimensions (3D space and time). To evaluate OptiFlex, we adopted datasets of four different lab animal species (mouse, fruit fly, zebrafish, and monkey) and proposed a more intuitive evaluation metric - percentage of correct key points (aPCK). Our evaluations show that OptiFlex provides the best prediction accuracy amongst current deep learning based tools, and that it can be readily applied to analyse a wide range of behaviours.

animal behavior and cognition↗