Analysis of behavioral flow resolves latent phenotypes
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Date
2024-12
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Journal Article
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yes
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Abstract
The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple testing, exhibit poor transferability across experiments and fail to exploit the rich behavioral profiles of individual animals. Here we introduce a pipeline to capture each animal's behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a large dataset of 771 behavior recordings of freely moving mice-including stress exposures, pharmacological and brain circuit interventions-to identify hidden treatment effects, reveal subtle variations on the level of individual animals and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, substantially enhances statistical power and enables predictions of an animal's future behavior.
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published
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21 (12)
Pages / Article No.
2376 - 2387
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Nature
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Organisational unit
09499 - Bohacek, Johannes / Bohacek, Johannes
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Funding
ETH-20 19-1 - A cross‐disciplinary, data‐driven approach to predict stress resilience from large‐scale behavioral, molecular and neural activity data (ETHZ)
172889 - Dissecting stress-induced molecular changes in circuits underlying anxiety (SNF)
204372 - A molecular roadmap to the acute stress response in health and disease (SNF)
172889 - Dissecting stress-induced molecular changes in circuits underlying anxiety (SNF)
204372 - A molecular roadmap to the acute stress response in health and disease (SNF)
Related publications and datasets
Is new version of: https://doi.org/10.3929/ethz-b-000651541