Data-driven approach for identifying subgroups using fMRI connectivity maps

描述（由申请人提供）：希望了解人类大脑过程的功能性MRI（fMRI）研究人员越来越多地估计感兴趣区域（ROI）之间的时间关系。总之，这些估计创建了一个大脑处理如何发生的“连接图”。大多数连通性映射方法的一个普遍存在的问题是，它们需要跨个体的同质性以获得可靠和有效的结果。研究人员目前别无选择，只能依赖同质性假设，尽管一致的证据表明，在对照人群和临床人群中，人类样本的大脑过程存在很大差异。因此，为了检验根据人口统计学、行为或诊断指数建立的亚组之间的差异，研究人员必须假设这些亚组中的所有个体都是相同的。在神经成像领域中需要用于从个体的连接图识别个体的亚组以适应亚组内异质性的数据驱动的方法。数据驱动的亚组分类可以通过对整个样本进行分组来识别与次优任务表现或特定诊断相关的大脑过程，同时还可以帮助研究人员了解亚组内的异质性。本项目旨在通过开发一种分析fMRI数据的新方法来满足这一需求，该方法：1）达到有效的样本水平推断，可以推广到人口; 2）确定个体的亚组分类; 3）提供可靠的参数估计在个人水平。在开发、验证和实施新程序之后，由本作者开发的成功的新算法构建的程序将免费向公众提供。

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.