Data-driven approach for identifying subgroups using fMRI connectivity maps

使用功能磁共振成像连接图识别亚组的数据驱动方法

• 批准号: 8583968
• 负责人: Kathleen Gates
• 金额: $ 18.57万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8583968
• 关键词: Algorithms; Behavioral; Brain; Brain Mapping; Categories; Classification; Clinical; Clinical Research; Computer software; Data; Diagnosis; Diagnostic; Ensure; Functional Magnetic Resonance Imaging; Goals; Heterogeneity; Human; Individual; Manuals; Maps; Methods; Modeling; Population; Procedures; Process; Research; Research Personnel; Sampling; Subgroup; Task Performances; Techniques; Time; Work; base; indexing; interest; neuroimaging; novel; novel strategies; programs; public health relevance; simulation

DESCRIPTION (provided by applicant): Functional MRI (fMRI) researchers wishing to understand human brain processes increasingly estimate relations among regions of interest (ROIs) across time. Together, these estimates create a "connectivity map" of how brain processing occurs. One ubiquitous issue for most connectivity mapping methods is that they require homogeneity across individuals for reliable and valid results to be obtained. Researchers currently have no choice but to rely on homogeneity assumptions despite consistent evidence suggesting that brain processes vary substantially across human samples within control and clinical populations. Thus to examine differences between subgroups created according to demographic, behavioral or diagnostic indices, researchers must assume that all individuals within these subgroups are the same. There is a need in the field of neuroimaging for data-driven methods for identifying subgroups of individuals from their connectivity maps to accommodate within-subgroup heterogeneity. Data-driven subgroup classification could identify brain processes which relate to suboptimal task performance or specific diagnoses by subgrouping the entire sample in addition to helping researchers understand heterogeneity within subgroups. The present project aims to fill this demand by developing a novel approach for analyzing fMRI data which: 1) arrives at valid sample-level inferences that may be generalized to the population; 2) identifies subgroup classification for individuals; and 3) provides reliable parameter estimates at the individual level. After developing, validating, and implementing the new procedure, a program which builds from a successful novel algorithm developed by the present authors will be made freely available to the public.

描述（由申请人提供）：希望了解人脑过程的功能 MRI (fMRI) 研究人员越来越多地估计感兴趣区域 (ROI) 之间随时间变化的关系。这些估计共同创建了大脑处理如何发生的“连接图”。大多数连通性映射方法普遍存在的一个问题是，它们需要个体之间的同质性才能获得可靠且有效的结果。尽管一致的证据表明对照人群和临床人群中的人类样本的大脑过程存在很大差异，但研究人员目前别无选择，只能依赖同质性假设。因此，为了检查根据人口统计、行为或诊断指标创建的亚组之间的差异，研究人员必须假设这些亚组内的所有个体都是相同的。神经影像领域需要数据驱动的方法，用于从个体的连通性图中识别个体亚组，以适应亚组内的异质性。数据驱动的亚组分类除了帮助研究人员了解亚组内的异质性之外，还可以通过对整个样本进行亚组来识别与次优任务表现或特定诊断相关的大脑过程。本项目旨在通过开发一种分析功能磁共振成像数据的新方法来满足这一需求，该方法：1）得出可推广到人群的有效样本水平推论； 2) 确定个人的亚组分类； 3) 在个体层面提供可靠的参数估计。在开发、验证和实施新程序后，根据当前作者开发的成功新颖算法构建的程序将免费向公众开放。