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

神经影像研究中调整群体异质性和代表性的新方法

• 批准号: 10189007
• 负责人: Yajuan Si
• 金额: $ 18.08万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10189007
• 关键词: Address; Adolescent; Area; Behavioral Mechanisms; Big Data; Brain; Brain imaging; Calibration; Child; Cognition; Cognitive; Complex; Computer software; Data; Data Collection; Dependence; Development; Economics; Environmental Exposure; Foundations; Functional Magnetic Resonance Imaging; Gaussian model; Goals; Guidelines; Health Policy; Heterogeneity; Image; Individual; Individual Differences; Intelligence; Intervention; Investigation; Literature; Location; Maps; Measures; Methodology; Methods; Modeling; Modernization; Neurosciences; Neurosciences Research; Outcome; Policies; Population; Population Control; Population Heterogeneity; Population Research; Probability; Probability Samples; Procedures; Process; Public Health; Recommendation; Regression Analysis; Reproducibility; Research; Resources; Risk Factors; Sampling; Scientist; Selection Bias; Site; Social Sciences; Statistical Methods; Structure; Subgroup; Survey Methodology; Surveys; Systematic Bias; Target Populations; Time; Work; aged; base; brain behavior; cognitive ability; cognitive development; cost; data and analysis portal; data exploration; design; flexibility; high dimensionality; insight; instrument; neuroimaging; neuroimaging marker; neuromechanism; novel; novel strategies; population based; response; social; social disadvantage; software development; statistics; substance use; tool; volunteer

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.

摘要 从大量人群样本中收集的神经成像信息的大数据刺激了 人口神经科学研究的兴起。然而，神经科学研究的传统方法 基于偏离目标人群的非代表性样本，例如便利性和志愿者 样品缺乏代表性可能会扭曲脑认知机制的关联研究。这 该提案的动机是研究小组在青少年大脑认知发展方面的合作工作 这项研究提出了经验性神经影像学研究中的这些常见问题，以填补统计学上的差距。 研究方法与神经科学研究之间的关系。该提案制定了新的战略，以适应 在复杂和非传统调查设计的关联研究中的非代表性，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人群异质性， 广义多层次回归和后分层作为一个强大的框架推断的基础上nonprobabil- 样品。具有计算可扩展性和分步指南的软件交付将提供实用的 建议和工具，以映射关系，并调整选择偏见时，使人口- 协商。这个跨学科的项目将加强人口神经科学的有效性和普遍性 研究，加深对大脑和认知的新关联理解，并促进政策干预。