The DOI matches its Crossref record.
Mood and Anxiety Related Phenotypes in Mice: Characterization Using Behavioral Tests
Abstract
Affecting approximately twenty percent of the world population, mood and anxiety disorders have been the subject of ever-increasing research. This increased research parallels a remarkable growth in t
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Citations
Cited by
Can, A., Dao, D. T., Terrillion, C. E., Piantadosi, S. C., Bhat, S., & Gould, T. D. (2011) · Journal of Visualized Experiments · doi:10.3791/3769 · Cited by 526
Can, A., Dao, D. T., Arad, M., Terrillion, C. E., Piantadosi, S. C., & Gould, T. D. (2011) · Journal of Visualized Experiments · doi:10.3791/3638 · Cited by 521
Exploratory rearing: a context- and stress-sensitive behavior recorded in the open-field test
Sturman, O., Germain, P.-L., & Bohacek, J. (2018) · Stress · doi:10.1080/10253890.2018.1438405 · Cited by 484
Animal models of major depression: drawbacks and challenges
Planchez, B., Surget, A., & Belzung, C. (2019) · Journal of Neural Transmission · doi:10.1007/s00702-019-02084-y · Cited by 480
50 years of hurdles and hope in anxiolytic drug discovery
Griebel, G., & Holmes, A. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4075 · Cited by 414
Metadata
- Venue
- Neuromethods
- Publisher
- Humana Press
- Published
- 2009
- ISSN
- 0893-2336 (ISSN-L)
- 1940-6045
- OpenAlex
- W2495510751
- Cited by
- 349
- References
- 0
- FWCI
- 5.27
- Type
- book
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Mood and Anxiety Related Phenotypes in Mice: Characterization Using Behavioral Tests. https://app.cochat.ai/papers/10.1007/978-1-60761-303-9
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- Mood and Anxiety Related Phenotypes in Mice: Characterization Using Behavioral Tests. (2009). Neuromethods. https://doi.org/10.1007/978-1-60761-303-9
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
