Personality traits can offer considerable insight into the biological basis of individual differences. However, existing approaches toward understanding personality across species rely on subjective criteria and limited sets of behavioral readouts, which result in noisy and often inconsistent outcomes. Here we introduce a mathematical framework for describing individual differences along dimensions with maximum consistency and discriminative power. We validate this framework in mice, using data from a system for high-throughput longitudinal monitoring of group-housed male mice that yields a variety of readouts from across the behavioral repertoire of individual animals. We demonstrate a set of stable traits that capture variability in behavior and gene expression in the brain, allowing for better-informed mechanistic investigations into the biology of individual differences.

中文翻译：

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身份域从整个行为库中捕获个体差异。

人格特质可以提供相当多的洞察个体差异的生物学基础。然而，现有的跨物种理解个性的方法依赖于主观标准和有限的行为读数集，这会导致嘈杂且经常不一致的结果。在这里，我们介绍了一个数学框架，用于描述具有最大一致性和辨别力的维度上的个体差异。我们在老鼠身上验证了这个框架，使用来自一个系统的数据，该系统用于对群养雄性老鼠进行高通量纵向监测，该系统产生来自个体动物行为库的各种读数。我们展示了一组稳定的特征，这些特征可以捕捉大脑中行为和基因表达的变异性，