Abstract
AbstractThe nuanced detection of rodent behavior in preclinical biomedical research is essential for understanding disease conditions, genetic phenotypes, and internal states. Recent advances in machine vision and artificial intelligence have popularized data-driven methods that segment complex animal behavior into clusters of behavioral motifs. However, despite the rapid progress, several challenges remain: Statistical power typically decreases due to multiple testing correction, poor transferability of clustering approaches across experiments limits practical applications, and individual differences in behavior are not considered. Here, we introduce “behavioral flow analysis” (BFA), which creates a single metric for all observed transitions between behavioral motifs. Then, we establish a “classifier-in-the-middle” approach to stabilize clusters and enable transferability of our analyses across datasets. Finally, we combine these approaches with dimensionality reduction techniques, enabling “behavioral flow fingerprinting” (BFF) for individual animal assessment. We validate our approaches across large behavioral datasets with a total of 443 open field recordings that we make publicly available, comparing various stress protocols with pharmacologic and brain-circuit interventions. Our analysis pipeline is compatible with a range of established clustering approaches, it increases statistical power compared to conventional techniques, and has strong reproducibility across experiments within and across laboratories. The efficient individual phenotyping allows us to classify stress-responsiveness and predict future behavior. This approach aligns with animal welfare regulations by reducing animal numbers, and enhancing information extracted from experimental animals
Publisher
Cold Spring Harbor Laboratory
Reference59 articles.
1. Skinner, B. F . The behavior of organisms: an experimental analysis. 457, (1938).
2. Darwin, C. & Prodger, P . The Expression of the Emotions in Man and Animals. (Oxford University Press, 1998).
3. Behavioral neuroscience, exploration, and K.C. Montgomery’s legacy;Brain Res. Rev.,2007
4. Big behavior: challenges and opportunities in a new era of deep behavior profiling;Neuropsychopharmacology,2021
5. Segalin, C. et al. The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice. Elife 10, (2021).