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An automatic pattern detection method for behavioral analysis of zebrafish larvae

机译：斑马鱼幼虫行为分析的自动模式检测方法

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Zebrafish has becomes a popular biological model for studies in pain, stress and welfare. However, automated assessment of nociceptive thresholds in larval zebrafish remains a challenge for biomedical researchers. This paper presents a new automatic pattern detection method for behavioral analysis of zebrafish larvae. The proposed method divides each arena in the test-bed mesh into an inner and outer zone with the aim of detecting patterns of fish behavior in the outer zones (also called thigmotaxis or wall hugging) that is considered one of the most common behavioral patterns studied in anxiety models. Three distinct groups of fish larvae are used as test subjects in this study. These groups are exposed to electric stimulation using different voltage levels. Poststimulation behaviors of the subjects under test are recorded using an infrared sensitive camera and analyzed. The obtained results demonstrated a noticeable change in the larval behavior in terms of the number of detected patterns in the outer zones of the arena cells. These findings confirm the validity of the proposed pattern detection method as a new metric to assess nociceptive thresholds for behavioral analysis of larvae.
机译：斑马鱼已成为研究疼痛，压力和福利的流行生物学模型。然而，对幼虫斑马鱼的伤害感受性阈值进行自动评估仍然是生物医学研究人员面临的挑战。本文提出了一种用于斑马鱼幼虫行为分析的自动模式检测新方法。所提出的方法将测试床网格中的每个区域划分为内部区域和外部区域，目的是检测外部区域中鱼类行为的模式（也称为触线或壁抱），这被认为是研究的最常见的行为模式之一。在焦虑模型中。在本研究中，将三组不同的鱼幼虫用作测试对象。这些组使用不同的电压水平受到电刺激。使用红外敏感相机记录受测对象的刺激后行为并进行分析。获得的结果表明，根据在竞技场细胞外部区域中检测到的模式数量，幼虫行为发生了显着变化。这些发现证实了所提出的模式检测方法作为评估幼虫行为分析的伤害阈值的新指标的有效性。

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