Integrated behavioral z-scoring increases the sensitivity and reliability of behavioral phenotyping in mice: Relevance to emotionality and sex

Defining anxiety- and depressive-like states in mice (emotionality) is best characterized by the use of complementary tests, leading sometimes to puzzling discrepancies and lack of correlation between similar paradigms. To address this issue, we hypothesized that integrating measures along the same behavioral dimensions in different tests would reduce the intrinsic variability of single tests and provide a robust characterization of the underlying “emotionality” of individual mouse, similarly as mood and related syndromes are defined in humans through various related symptoms over time. We describe the use of simple mathematical and integrative tools to help phenotype animals across related behavioral tests (syndrome diagnosis) and experiments (meta-analysis). We applied z-normalization across complementary measures of emotionality in different behavioral tests after unpredictable chronic mild stress (UCMS) or prolonged corticosterone exposure – two approaches to induce anxious-/depressive-like states in mice. Combining z-normalized test values, lowered the variance of emotionality measurement, enhanced the reliability of behavioral phenotyping, and increased analytical opportunities. Comparing integrated emotionality scores across studies revealed a robust sexual dimorphism in the vulnerability to develop high emotionality, manifested as higher UCMS-induced emotionality z-scores, but lower corticosterone-induced scores in females compared to males. Interestingly, the distribution of individual z-scores revealed a pattern of increased baseline emotionality in female mice, reminiscent of what is observed in humans. Together, we show that the z-scoring method yields robust measures of emotionality across complementary tests for individual mice and experimental groups, hence facilitating the comparison across studies and refining the translational applicability of these models.