抑郁情绪模型大鼠旷场数据的聚合分析

Aggregate analysis of open field data in depression model rats

背景:多次测量正常大鼠的旷场数据,以加和方式进行数据聚合,可显著提高旷场指标的稳定性与一致性,此方法对造模后大鼠的旷场行为是否适用?目的:分析抑郁情绪模型大鼠旷场实验中活动量总得分的最佳聚合方式。方法:采用慢性温和刺激法复制抑郁情绪大鼠模型。在4周的造模过程中,每隔1周采集1次旷场行为学指标。采用两两聚合的分析方法(连续聚合,间隔聚合,立意聚合)处理数据,比对不同聚合手段对模型制备信度及相关性的影响。结果与结论:①一致性系数Kappa结果:间隔聚合与立意聚合的聚合方式都能得到较好的可信度(Kappa=0.611,P<0.05),连续聚合方式可信度一般(Kappa=0.222)。②组内相关系数ICC结果:只有立意聚合的可信度极好(ICC=0.835,P<0.01),间隔聚合方式呈较好可信度(ICC=0.792,P<0.05),连续聚合方式可信度差(ICC=0.196)。说明恰当的立意聚合可以大幅提升抑郁情绪大鼠旷场数据的可信度与稳定性。

BACKGROUND： Repeated measurements of open field data and proper data aggregation based on additivity can significantly improve the stability and consistency of the open field test in normal rats. Is this method applicable to model rats？ OBJECTIVE： To explore the best aggregate measurement of ambulation score in the open-field test of the depression model rats. METHODS： Depression models in rats were constructed using chronic mild stimulation method. The open-field behavior indicators were collected every two weeks during the model construction course of 4 weeks. Pairwise aggregation analysis （continuous aggregation, interval aggregation, judgmental aggregation） was used. Effects of different aggregation methods on reliability and correlation of model preparation were compared. RESULTS AND CONCLUSION： Consistency coefficient Kappa showed that good reliability and correlation were obtained using interval aggregation and judgmental aggregation （Kappa=0.611, P 〈 0.05）. The reliability gained using continuous aggregation was passable （Kappa=0.222）. Intreclass correlation coefficient ICC showed that an excellent reliability was gained using judgmental aggregation （ICC=0.835, P 〈 0.01）; the reliability gained using interval aggregation was good （ICC=0.792, P 〈 0.05）, and the reliability gained using continuous aggregation was poor （ICC=0.196）. It indicates that a proper judgment aggregation can significantly improve the reliability and correlation of the open-field data in depression model rats.