ArticleScientific reports2018
Active State Organization of Spontaneous Behavioral Patterns.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- eeeHive: a new HF RFID-based automated behavioral monitoring system enabling high spatiotemporal resolution tracking of group-housed animals.Frontiers in behavioral neuroscience · 2026Article
- Clinicopathologic Dissociation: Robust Lafora Body Accumulation in Malin KO Mice Without Observable Changes in Home-Cage Behavior.The Journal of comparative neurology · 2024Article
- Accurate locomotor activity profiles of group-housed mice derived from home cage monitoring data.Frontiers in neuroscience · 2024Article
- Data repurposing from digital home cage monitoring enlightens new perspectives on mouse motor behaviour and reduction principle.Scientific reports · 2023Article
- Home-cage behavior in the Stargazer mutant mouse.Scientific reports · 2022Article
- Phenotyping spontaneous locomotor activity in inbred and outbred mouse strains by using Digital Ventilated Cages.Lab animal · 2021Article
- Major oscillations in spontaneous home-cage activity in C57BL/6 mice housed under constant conditions.Scientific reports · 2021Article
- Measuring Behavior in the Home Cage: Study Design, Applications, Challenges, and Perspectives.Frontiers in behavioral neuroscience · 2021Article
- Germinal Matrix-Intraventricular Hemorrhage of the Preterm Newborn and Preclinical Models: Inflammatory Considerations.International journal of molecular sciences · 2020Review
- On the Digital Psychopharmacology of Valproic Acid in Mice.Frontiers in neuroscience · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We report the development and validation of a principled analytical approach to reveal the manner in which diverse mouse home cage behaviors are organized. We define and automate detection of two mutually-exclusive low-dimensional spatiotemporal units of behavior: "Active" and "Inactive" States. Analyses of these features using a large multimodal 16-strain behavioral dataset provide a series of novel insights into how feeding, drinking, and movement behaviors are coordinately expressed in Mus Musculus. Moreover, we find that patterns of Active State expression are exquisitely sensitive to strain, and classical supervised machine learning incorporating these features provides 99% cross-validated accuracy in genotyping animals using behavioral data alone. Altogether, these findings advance understanding of the organization of spontaneous behavior and provide a high-throughput phenotyping strategy with wide applicability to behavioral neuroscience and animal models of disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.