MNI-Montreal Neurological Institute, Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada.
MILA-Quebec Artificial Intelligence Institute, Montreal H2S 3H1, Canada.
Gigascience. 2024 Jan 2;13. doi: 10.1093/gigascience/giae068.
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.
当涉及到定量数据分析时，大型神经科学数据集并不是小数据集。神经科学现在已经见证了许多群体队列研究的出现，这些研究深入分析参与者，产生数百种测量结果，捕捉每个个体在更广泛社会中的位置的各个维度。实际上，正在从规模小、严格选择的、因此同质化的队列向始终更大、更具代表性的、因此更多样化的队列进行重新平衡。这种队列组成的转变促使对现有建模实践进行修订。人口分层的主要来源越来越多地掩盖了神经科学家通常研究的微妙影响。在我们看来，随着我们从越来越广泛的多样性背景中抽样个体，我们将需要一套新的定量工具来实现对多样性的建模。在这里，我们列出了候选分析框架。更好地纳入人口结构背后的驱动因素将使我们能够更深入地了解脑-行为关系如何取决于人类亚组。
