Rodent models for mania: practical approaches
The scarcity of good animal models for bipolar disorder (BPD) and especially for mania is repeatedly mentioned as one of the rate-limiting factors in the process of gaining a better understanding into its pathophysiology and of developing better treatments. Standard models of BPD have some value but...
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Veröffentlicht in: | Cell and tissue research 2013-10, Vol.354 (1), p.191-201 |
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Sprache: | eng |
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Zusammenfassung: | The scarcity of good animal models for bipolar disorder (BPD) and especially for mania is repeatedly mentioned as one of the rate-limiting factors in the process of gaining a better understanding into its pathophysiology and of developing better treatments. Standard models of BPD have some value but usually represent only one facet of the disease and have partial validity. A number of new approaches for modeling BPD and specifically mania have been suggested in the last few years and can be combined to improve models. These approaches include targeted mutation models representing reverse translation, the identification of advantageous strains for components of the disorder, a search for the most homologous species to address specific human pathology, and the exploration of individual differences of response including the separation between susceptible and resilient animals. Additionally, recent efforts have identified and developed new tests to distinguish between “normal” and “BPD-like” animals including the different utilization of known tests and novel tests such as the female-urine-sniffing test and behavior pattern monitor analysis. Additional tests relating to further domains of BPD are still needed. An ideal model for BPD that will encompass the entire disease and be useful for every demand will probably not become available until we have a full understanding of the pathophysiology of the disorder. However, the current advances in modeling should lead to better comprehension of the disorder and therefore to the gradual development of increasingly improved models. |
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ISSN: | 0302-766X 1432-0878 |
DOI: | 10.1007/s00441-013-1594-x |