The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Animal models of mood disorders: recent developments
Abstract
PURPOSE OF REVIEW: The wide spectrum of disruptions that characterizes depression and bipolar illness highlights the difficulties researchers are posed with as they try to mimic these disorders in the laboratory. Nonetheless, numerous attempts have been made to create rodent models of mood disorders, or at least models of the symptoms of depression and bipolar illness. Despite many advances, however, there are no satisfactory animal models available. The need for improved animal models for identifying new antidepressants and providing insights into the neuropathology underlying the disease is critical. This review focuses on the attempts to improve current paradigms and also illustrates examples where current paradigms are used to uncover novel molecular targets of antidepressants. RECENT FINDINGS: Currently, there is a shift away from traditional animal models to a more focused research dealing with an endophenotype-style approach, genetic models and incorporation of new findings from human neuroimaging and genetic studies. SUMMARY: Endophenotype-based modelling of depression and bipolar illness is opening up more tractable avenues for understanding the neurobiological and genetic bases of these disorders. Further, advances in the clinical dissection of the psychiatric illnesses using molecular genetics, coupled with functional neuroimaging techniques, promises to yield better translational animal models and hence more fruitful therapeutic targets.
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Cites
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Deep Brain Stimulation for Treatment-Resistant Depression
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Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress
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Sustained hippocampal chromatin regulation in a mouse model of depression and antidepressant action
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Metadata
- Authors
- Cryan, J. F., & Slattery, D. A.
- Venue
- Current Opinion in Psychiatry
- Published
- 2007-01
- Volume
- 20
- Issue
- 1
- Pages
- 1-7
- ISSN
- 0951-7367 (ISSN-L)
- OpenAlex
- W2093219983
- Cited by
- 323
- References
- 65
- FWCI
- 10.57
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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