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Estimating Information Processing in a Memory System: The Utility of Meta-analytic Methods for Genetics

机译:估计存储系统中的信息处理:遗传学的元分析方法的实用性

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摘要

Genetic studies in Drosophila reveal that olfactory memory relies on a brain structure called the mushroom body. The mainstream view is that each of the three lobes of the mushroom body play specialized roles in short-term aversive olfactory memory, but a number of studies have made divergent conclusions based on their varying experimental findings. Like many fields, neurogenetics uses null hypothesis significance testing for data analysis. Critics of significance testing claim that this method promotes discrepancies by using arbitrary thresholds (α) to apply reject/accept dichotomies to continuous data, which is not reflective of the biological reality of quantitative phenotypes. We explored using estimation statistics, an alternative data analysis framework, to examine published fly short-term memory data. Systematic review was used to identify behavioral experiments examining the physiological basis of olfactory memory and meta-analytic approaches were applied to assess the role of lobular specialization. Multivariate meta-regression models revealed that short-term memory lobular specialization is not supported by the data; it identified the cellular extent of a transgenic driver as the major predictor of its effect on short-term memory. These findings demonstrate that effect sizes, meta-analysis, meta-regression, hierarchical models and estimation methods in general can be successfully harnessed to identify knowledge gaps, synthesize divergent results, accommodate heterogeneous experimental design and quantify genetic mechanisms.
机译:果蝇的遗传学研究表明,嗅觉记忆依赖于称为蘑菇体的大脑结构。主流观点认为,蘑菇体的三个裂片中的每个裂片在短期厌恶嗅觉记忆中都起着特殊的作用,但是许多研究基于其不同的实验发现得出了不同的结论。像许多领域一样,神经遗传学使用零假设重要性检验进行数据分析。重要性测试的批评者声称,该方法通过使用任意阈值(α)将拒绝/接受二分法应用于连续数据来加剧差异,这不能反映定量表型的生物学现实。我们探索了使用估计统计数据(一种替代的数据分析框架)来检查已发布的蝇类短期记忆数据。系统评价用于确定行为实验,以检查嗅觉记忆的生理基础,并采用荟萃分析方法评估小叶特化的作用。多变量元回归模型显示,数据不支持短期记忆小叶专业化;它确定了转基因驱动程序的细胞范围是其对短期记忆影响的主要预测因子。这些发现表明,一般可以成功地利用效应大小,荟萃分析,荟萃回归,层次模型和估计方法来识别知识差距,合成不同结果,适应异构实验设计并量化遗传机制。

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