The DOI matches its Crossref record; 139 of the 139 references it cites were found in a registry or an index.
Modeling anxiety using adult zebrafish: A conceptual review
Open accessAbstract
Zebrafish (Danio rerio) are rapidly emerging as a useful animal model in neurobehavioral research. Mounting evidence shows the suitability of zebrafish to model various aspects of anxiety-related states. Here, we evaluate established and novel approaches to uncover the molecular substrates, genetic pathways and neural circuits of anxiety using adult zebrafish. Experimental approaches to modeling anxiety in zebrafish include novelty-based paradigms, pharmacological and genetic manipulations, as well as innovative video-tracking, 3D-reconstructions and bioinformatics-based searchable databases and omics-based tools. Complementing traditional rodent models of anxiety, we provide a conceptual framework for the wider application of zebrafish and other aquatic models in anxiety research.
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Cites
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Stratified medicine for mental disorders
Schumann, G., Binder, E. B., Holte, A., de Kloet, E. R., Oedegaard, K. J., Robbins, T. W., Walker-Tilley, T. R., Bitter, I., Brown, V. J., Buitelaar, J., et al. (2014) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2013.09.010 · Cited by 186
Animal models for screening anxiolytic-like drugs: a perspective.
Bourin, M. (2015) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2015.17.3/mbourin · Cited by 163
Metadata
- Authors
- Stewart, A., Gaikwad, S., Kyzar, E., Green, J., Roth, A., & Kalueff, A. V.
- Venue
- Neuropharmacology
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 62
- Issue
- 1
- Pages
- 135-143
- ISSN
- 0028-3908 (ISSN-L)
- OpenAlex
- W2105356326
- Cited by
- 405
- References
- 139
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- green
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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.
