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

基本信息

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
项目类别：
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

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

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项目摘要

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.

抽象的来自大型基于人群样本的神经影像学信息的大数据刺激了人群神经科学研究的出现。但是，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，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。这提案是由研究团队在青少年大脑认知发展上的合作工作激发的在经验神经影像学研究中提出了这些常见问题的研究，以填补统计的差距调查与神经科学研究之间的方法。该提案制定了新的策略来调整与复杂和非传统调查设计的关联研究中的非陈述性，并量化采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指标和认知能力指标之间的关联研究中确定种群异质性将多级回归和延伸后概括为基于非验证的推论框架是样本。具有计算可扩展性和分步指南的软件交付将提供实用建议和工具绘制关系并调整选择偏见时，使人口融入 Ference。这个跨学科项目将增强人群神经科学的有效性和普遍性研究，加深对大脑和认知的新社会理解，并促进政策干预。