Novel Approaches to Adjusting for Population Heterogeneity and Representation in Neuroimaging Studies

摘要 从大量基于人群的样本中收集的以神经成像信息为特征的大数据刺激了 人口神经科学研究的兴起。然而，神经科学研究的传统方法是 基于偏离目标人群的非代表性样本，如便利性和志愿者 样本。缺乏代表性可能会扭曲对大脑认知机制的关联研究。这 该提案是由研究团队在青少年大脑认知发展方面的合作工作推动的 这项研究提出了经验性神经成像研究中的这些常见问题，以解决统计学上的差距 调查和神经科学研究之间的方法论。该提案制定了新的战略，以适应 与复杂和非传统调查设计的相关性研究中的非代表性，并量化 抽样特征对统计和实质性推断的潜在影响。总体目标是 在成像和认知能力测量之间的关联研究中确定群体异质性 将多水平回归和fi后处理推广为基于非概率推理的稳健框架。 城市样本。具有计算可伸缩性和分步指导的软件交付将提供实用的 建议和工具，用于绘制关系图，并在进行人口统计时调整选择偏向- 费伦斯。这一跨学科项目将加强人口神经科学的有效性和普适性。 研究，深化对大脑和认知的新联想理解，促进政策干预。

Abstract Big data featuring neuroimaging information collected from large population-based samples have spurred the emergence of population neuroscience research. However, traditional methods for neuroscience research are based on nonrepresentative samples that deviate from the target population, such as convenience and volunteer samples. The lack of representativeness may distort association studies of brain-cognition mechanisms. This proposal is motivated by the research team's collaborative work on the Adolescent Brain Cognitive Development Study, which presents these common problems in empirical neuroimaging studies, to fill the gap in statistical methodology between survey and neuroscience research. The proposal develops new strategies to adjust for nonrepresentativeness in association studies with complex and nontraditional survey designs, and to quantify the potential impact of sampling features on statistical and substantive inferences. The overall objectives are to identify population heterogeneity in the association studies between imaging and cognitive ability measures and generalize multilevel regression and poststratification as a robust framework for inferences based on nonprobabil- ity samples. The software delivery with computational scalability and step-by-step guidelines will provide practical recommendations and tools to map the relationships and adjust for selection bias when making population in- ference. This interdisciplinary project will strengthen the validity and generalizability of population neuroscience research, deepen new association understandings of brain and cognition, and facilitate policy intervention.