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Department of Animal Sciences
The Robert H. Smith Faculty
of Agricultural, Food & Environment

The Hebrew University of Jerusalem.

P.O. Box 12, Rehovot 76100, Israel
Phone: +972-(0)8-9489119;
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e-mail: yaellew@savion.huji.ac.il

Publications

2019
Forkosh, O. ; Karamihalev, S. ; Roeh, S. ; Alon, U. ; Anpilov, S. ; Touma, C. ; Nussbaumer, M. ; Flachskamm, C. ; Kaplick, P. M. ; Shemesh, Y. ; et al. Identity domains capture individual differences from across the behavioral repertoire. Nature Neuroscience 2019, 22, 2023-2028. Publisher's VersionAbstract
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. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.
2018
Forkosh, O. ; Karamihalev, S. ; Roeh, S. ; Engel, M. ; Alon, U. ; Anpilov, S. ; Nussbaumer, M. ; Flachskamm, C. ; Kaplick, P. ; Shemesh, Y. ; et al. Identity domains in complex behavior: Toward a biology of personality. bioRxiv 2018, 395111. Publisher's VersionAbstract
Personality traits 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, resulting in noisy and often inconsistent outcomes. Here, we introduce a mathematical framework for studying 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 mice that yields a variety of readouts from all across an individual’s behavioral repertoire. We describe 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.