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Big data approaches to identifying sex differences in long-term memory
Cognitive Neuroscience ( IF 2 ) Pub Date : 2020-12-24 , DOI: 10.1080/17588928.2020.1866520
Link Tejavibulya 1 , Dustin Scheinost 1, 2, 3, 4, 5
Affiliation  

ABSTRACT

Whether in neurotransmitters or large-scale circuits, sex differences have long been of interest in neuroscience. Spets and Slotnick conducted a meta-analysis of fMRI studies of long-term memory to identify sex differences in brain-behavior associations, demonstrating that sex differences are pervasive across many sub-types of long-term memory. Meta-analyses are a workhorse toward aggregating larger sample sizes to arrive at a more comprehensive understanding of such topics. However, more research is crucial to elucidate complex relationships in how fMRI signals translate to behavioral outcomes. We propose big data and open-science as a solution toward finding robust sex differences in brain-behavior associations.



中文翻译:

识别长期记忆中性别差异的大数据方法

摘要

无论是在神经递质还是大规模电路中,性别差异一直是神经科学领域的兴趣所在。Spets 和 Slotnick 对长期记忆的 fMRI 研究进行了荟萃分析,以确定大脑行为关联中的性别差异,证明性别差异在长期记忆的许多亚型中普遍存在。荟萃分析是聚合更大样本量以更全面地了解这些主题的主力。然而,更多的研究对于阐明 fMRI 信号如何转化为行为结果的复杂关系至关重要。我们提出大数据和开放科学作为寻找大脑行为关联中强大的性别差异的解决方案。

更新日期:2020-12-24
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