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Qualitative Differences in Pup-Retrieval Strategies in a Maternal Separation Paradigm
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作者 Loudin Daoura Ingrid Nylander erika roman 《Journal of Behavioral and Brain Science》 2013年第8期603-616,共14页
The rodent maternal separation (MS) paradigm is frequently used to investigate the impact of early-life conditions in the offspring. One critical issue is whether the effects seen in the offspring are a result of mate... The rodent maternal separation (MS) paradigm is frequently used to investigate the impact of early-life conditions in the offspring. One critical issue is whether the effects seen in the offspring are a result of maternal contact deprivation and/or altered pup-directed maternal behavior. To address this question we used an innovative approach with a qualitative analysis of pup-retrieval strategies in a test situation related to risk for the pups. The dams were separated from their litters for 0 (MS0) or 360 (MS360) min, respectively. The pups were placed in a risk area in the multivariate concentric square field? test at two test occasions and the pup-retrieval strategies were recorded. No significant evident differences between MS0 and MS360 dams were found. However, there were clearly two different strategies, either removing the pups out of potential danger or into safety, and these strategies were represented in both MS groups. As compared to the MS0 dams, the MS360 dams did not change their strategies and left more pups in the risk area in both pup-retrieval tests. This implies different pup-retrieval strategies depending on early-life conditions. 展开更多
关键词 Pregnancy GESTATION VIRGIN Handling MATERNAL DEPRIVATION Multivariate Concentric Square Field BEHAVIOR Wistar Rats Stress MATERNAL BEHAVIOR Ultrasonic VOCALIZATION
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A Rank-Order Procedure Applied to an Ethoexperimental Behavior Model—The Multivariate Concentric Square Field<sup>TM </sup>(MCSF) Test
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作者 Bengt J. Meyerson Betty Jurek erika roman 《Journal of Behavioral and Brain Science》 2013年第4期350-361,共12页
Designing relevant animal models in order to investigate the neurobiological basis for human mental disorders is an important challenge. The need for new tests to be developed and traditional tests to be improved has ... Designing relevant animal models in order to investigate the neurobiological basis for human mental disorders is an important challenge. The need for new tests to be developed and traditional tests to be improved has recently been em-phasized. The authors propose a multivariate test approach, the multivariate concentric square fieldTM (MCSF) test. To measure and evaluate variation in the behavioral traits, we here put forward a statistical procedure of which the working title is “trend analysis”. Low doses of the benzodiazepine agonist diazepam (DZP;1.0, 1.5, or 2.0 mg/kg) were used for exploring the use of the trend analysis in combination with multivariate data analysis for assessment of MCSF per-formance in rats. The commonly used elevated plus maze (EPM) test was used for comparison. The trend analysis comparing vehicle and the DZP1.5 groups revealed significantly higher general activity and risk-taking behavior in the DZP1.5 rats relative to vehicle rats. This finding was supported by multivariate data analysis procedures. It is concluded that the trend analysis together with multivariate data analysis procedures offers possibilities to extract information and illustrates effects obtained in the MCSF test. Diazepam in doses that have no apparent increase in open arm activity in the EPM was effective to alter the behavior in the MCSF test. The MCSF test and the use of multivariate data analysis and the proposed trend analysis may be useful alternatives to behavioral test batteries and traditionally used tests for the understanding of mechanisms underlying various mental states. Finally, the impact of an ethological reasoning and multivariate measures enabling behavioral profiling of animals may be a useful complementary methodology when phenotyping animals in behavioral neuroscience. 展开更多
关键词 Trend ANALYSIS DIAZEPAM Elevated Plus MAZE MULTIVARIATE Data ANALYSIS
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