德国马克斯·普朗克精神病学研究所Alon Chen研究组取得一项新进展。他们的研究利用身份域模型可从整个行为库中捕获个体差异。这一研究成果2019年11月4日在线发表在国际学术期刊《自然—神经科学》上。

研究人员报道了一个数学框架，用于描述维度上的个体差异，具有最大的一致性和判别力。 研究人员使用来自用于群居雄性小鼠的高通量纵向监测系统的数据验证了小鼠中的这一框架，该系统可从单个动物的行为方式中获得各种读数。研究人员展示了一组稳定的特征，这些特征可以捕获行为和基因在大脑中的表达的变异性，从而可以对个体差异的生物学进行更全面的机制研究。

据介绍，性格特质可以提供有关个体差异的生物学基础的重要见解。但是，现有的跨物种了解性格的方法依赖于主观标准和有限的行为测量结果，这会导致嘈杂且往往不一致的结果。

附：英文原文

Title: Identity domains capture individual differences from across the behavioral repertoire

Author: Oren Forkosh, Stoyo Karamihalev, Simone Roeh, Uri Alon, Sergey Anpilov, Chadi Touma, Markus Nussbaumer, Cornelia Flachskamm, Paul M. Kaplick, Yair Shemesh, Alon Chen

Issue&Volume: 2019-11-04

Abstract: 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.

DOI: 10.1038/s41593-019-0516-y

Source: https://www.nature.com/articles/s41593-019-0516-y