An automatic pattern detection method for behavioral analysis of zebrafish larvae

Abstract

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.