利用人口多样性:寻找贸易工具。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Danilo Bzdok, Guy Wolf, Jakub Kopal
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引用次数: 0

摘要

在定量数据分析方面,大型神经科学数据集并非大型小型数据集。现在,神经科学领域出现了许多人群队列研究,这些研究对参与者进行了深度剖析,得出了数百个测量值,捕捉到了每个人在更广泛社会中的地位。事实上,目前的研究正在从规模较小、经过严格筛选、因而同质化的队列向规模更大、更具代表性、因而多样化的队列转变。队列构成的这一变化正在促使对现有的建模方法进行修正。人口分层的主要来源日益掩盖了神经科学家通常研究的微妙影响。我们认为,随着我们对来自更广泛多样性背景的个体进行采样,我们将需要一系列新的定量工具来实现多样性感知建模。我们在此盘点了候选分析框架。更好地纳入人口结构背后的驱动因素将使我们能够更好地理解大脑与行为之间的关系如何取决于人类亚群体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing population diversity: in search of tools of the trade.

Big neuroscience datasets are not big small datasets when it comes to quantitative data analysis. Neuroscience has now witnessed the advent of many population cohort studies that deep-profile participants, yielding hundreds of measures, capturing dimensions of each individual's position in the broader society. Indeed, there is a rebalancing from small, strictly selected, and thus homogenized cohorts toward always larger, more representative, and thus diverse cohorts. This shift in cohort composition is prompting the revision of incumbent modeling practices. Major sources of population stratification increasingly overshadow the subtle effects that neuroscientists are typically studying. In our opinion, as we sample individuals from always wider diversity backgrounds, we will require a new stack of quantitative tools to realize diversity-aware modeling. We here take inventory of candidate analytical frameworks. Better incorporating driving factors behind population structure will allow refining our understanding of how brain-behavior relationships depend on human subgroups.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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