| Abstract | The link between environmental chemical exposures and neurodevelopmental disorders such as autism and attention-deficit/hyperactivity disorder underscores the need to develop efficient developmental neurotoxicity (DNT) assays for chemical evaluation. The zebrafish Light-Dark Transition Test (LDTT) assesses changes in zebrafish larval behavioral responses to chemical exposure by recording their distance moved under alternating light and dark conditions. To gain confidence in classifying a chemical as having a DNT effect for the LDTT assay, it is important to determine the minimum sample size to obtain a robust behavioral response. We calculated statistical power under common models based on LDTT data collected from four laboratories using standard protocol parameters, where each 96-well plate contained 5–7 test concentrations and 12–16 vehicle control wells (1 larva/well). Power calculations were conducted to identify concentration effects using t-tests, analysis of variance (ANOVA), and repeated measures ANOVA (RMANOVA), with data from four endpoints: Total Distance, Movement Similarity, Distance Change, and Distance Shift. The tests showed the highest power for the Movement Similarity and Distance Change endpoints, which had the lowest intra- and inter-laboratory variability, resulting in a smaller necessary sample size to estimate dose effects. The use of these endpoints more than doubled the power of the statistical tests for the Total Distance endpoints using the same sample size and typically required between 8 and 32 samples to achieve 80 % power at a 20 % effect size. This work demonstrates that the LDTT can be improved for detecting DNT effects by careful consideration of endpoint selection, data transformation, and type of statistical test. |
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