Computational classification of different wild-type zebrafish strains based on their variation in light-induced locomotor response. (1st February 2016)

• Record Type: Journal Article
• Authors: Gao, Yuan; Zhang, Gaonan; Jelfs, Beth; Carmer, Robert; Venkatraman, Prahatha; Ghadami, Mohammad; Brown, Skye A.; Pang, Chi Pui; Leung, Yuk Fai; Chan, Rosa H.M.; Zhang, Mingzhi
• Is Part Of: Computers in biology and medicine. Volume 69(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-02-01
• Subjects: Zebrafish -- Computational classification -- Light-induced locomotor response -- Visual motor response -- Support vector machines; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.11.012
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Zebrafish larvae display a rapid and characteristic swimming behaviour after abrupt light onset or offset. This light-induced locomotor response (LLR) has been widely used for behavioural research and drug screening. However, the locomotor responses have long been shown to be different between different wild-type (WT) strains. Thus, it is critical to define the differences in the WT LLR to facilitate accurate interpretation of behavioural data. In this investigation, we used support vector machine (SVM) models to classify LLR data collected from three WT strains: AB, TL and TLAB (a hybrid of AB and TL), during early embryogenesis, from 3 to 9 days post-fertilisation (dpf). We analysed both the complete dataset and a subset of the data during the first 30 after light change. This initial period of activity is substantially driven by vision, and is also known as the visual motor response (VMR). The analyses have resulted in three major conclusions: First, the LLR is different between the three WT strains, and at different developmental stages. Second, the distinguishable information in the VMR is comparable to, if not better than, the full dataset for classification purposes. Third, the distinguishable information of WT strains in the light-onset response differs from that in the light-offset response. While the classification accuracies were higher for the light-offset than light-onset response when using the complete LLR dataset, a reverse trend was observed when using a shorter VMR dataset. Together, our results indicate that one should use caution when extrapolating interpretations of LLR/VMR obtained from one WT strain to another.