Big data approaches to identifying sex differences in long-term memory.

IF 2 4区 医学 Q3 NEUROSCIENCES
Cognitive Neuroscience Pub Date : 2021-07-01 Epub Date: 2020-12-24 DOI:10.1080/17588928.2020.1866520
Link Tejavibulya, Dustin Scheinost
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引用次数: 1

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.

用大数据方法识别长期记忆的性别差异。
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研究的热点。斯佩茨和斯洛特尼克对长期记忆的功能磁共振成像研究进行了荟萃分析,以确定大脑行为关联中的性别差异,结果表明性别差异普遍存在于许多长期记忆的子类型中。荟萃分析是汇总更大样本量以达到对此类主题更全面理解的主力。然而,更多的研究对于阐明功能磁共振成像信号如何转化为行为结果的复杂关系至关重要。我们建议使用大数据和开放科学来寻找大脑行为关联中强有力的性别差异。
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来源期刊
Cognitive Neuroscience
Cognitive Neuroscience NEUROSCIENCES-
CiteScore
3.60
自引率
0.00%
发文量
27
审稿时长
>12 weeks
期刊介绍: Cognitive Neuroscience publishes high quality discussion papers and empirical papers on any topic in the field of cognitive neuroscience including perception, attention, memory, language, action, social cognition, and executive function. The journal covers findings based on a variety of techniques such as fMRI, ERPs, MEG, TMS, and focal lesion studies. Contributions that employ or discuss multiple techniques to shed light on the spatial-temporal brain mechanisms underlying a cognitive process are encouraged.
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