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平均睡眠纺锤波在狗身上的出现比单一指标更能预测学习表现。

Averaging sleep spindle occurrence in dogs predicts learning performance better than single measures.

机构信息

Department of Ethology, ELTE Eötvös Loránd University, 1117, Budapest, Hungary.

MTA-ELTE Comparative Ethology Research Group, 1117, Budapest, Hungary.

出版信息

Sci Rep. 2020 Dec 31;10(1):22461. doi: 10.1038/s41598-020-80417-8.

Abstract

Although a positive link between sleep spindle occurrence and measures of post-sleep recall (learning success) is often reported for humans and replicated across species, the test-retest reliability of the effect is sometimes questioned. The largest to date study could not confirm the association, however methods for automatic spindle detection diverge in their estimates and vary between studies. Here we report that in dogs using the same detection method across different learning tasks is associated with observing a positive association between sleep spindle density (spindles/minute) and learning success. Our results suggest that reducing measurement error by averaging across measurements of density and learning can increase the visibility of this effect, implying that trait density (estimated through averaged occurrence) is a more reliable predictor of cognitive performance than estimates based on single measures.

摘要

虽然人类和跨物种的研究经常报告睡眠纺锤波的发生与睡眠后回忆(学习成功)的测量之间存在正相关,但该效应的测试-重测信度有时受到质疑。迄今为止最大的一项研究未能证实这种关联,但是自动纺锤波检测方法在其估计值上存在差异,并且在不同的研究中也存在差异。在这里,我们报告说,在使用相同检测方法的狗中,在不同的学习任务中观察到睡眠纺锤波密度(每分钟纺锤波数)与学习成功之间存在正相关。我们的结果表明,通过平均密度和学习的测量值来减少测量误差可以增加这种效应的可见度,这意味着特征密度(通过平均发生来估计)是认知表现的更可靠预测指标,而不是基于单次测量的估计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc6f/7775433/acfa445e75ad/41598_2020_80417_Fig1_HTML.jpg

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