Semiparametric Inference for Psychiatric Neuroimaging

项目总结 识别大脑行为关联，以告知个体差异、疾病轨迹、 神经机制是精神病学神经成像的主要目标之一。大规模的多变量 神经成像数据的性质，由大脑结构和功能的空间细节图像组成， 与高维行为数据相结合，对实现这一目标构成了重大挑战。这个 神经影像研究中新出现的复制危机暴露了常用空间范围的局限性 用于分析成像数据的推理(SEI)方法。这些包括对空间的不切实际的假设 导致高度夸大错误率的成像数据的协方差函数。这个项目将开发一种新的 用于神经图像的健壮半参数推理框架，以满足对健壮方法的需求 在真实数据中，将这些方法集成到PBJ R包中，并开发图形用户界面 (图形用户界面)使神经成像科学家能够使用这些方法。我们将使用这些方法来研究如何 精神病患者多维症状与脑功能和结构的关系 范德比尔特大学精神病院(VUPH)收集的基因-表型项目(PGPP)和 研究公共通道Nathan Kline功能连通性的横断面和纵向变化 洛克兰研究所样本(NKI-RS)。我们将使用基于实际引导的方法来评估这些方法 神经成像模拟。在目标1中，我们将为SEI开发一个多维半参数过程，该过程 将利用计算效率高的参数和非参数引导进行推理。在《目标2》中我们将 将框架扩展到重复测量模型(包括纵向数据)，这将使科学家能够 对受试者水平的协变量测量与大脑结构或功能的关联进行稳健的建模。在AIM 3，为了解决在精神神经成像中替代假设检验的需求，我们将开发 可用于构建空间置信度的半参数覆盖概率漂移(COPE)集 半参数效果大小的间隔。这些方法将可用于神经成像 社区通过PBJ R包和图形用户界面，并在神经成像会议上传播。

PROJECT SUMMARY Identifying brain-behavior associations for the purpose of informing individual differences, illness trajectories, and neural mechanisms is one of the primary goals of psychiatric neuroimaging. The massively multivariate nature of neuroimaging data, which consists of spatially detailed images of brain structure and function, combined with high-dimensional behavioral data pose significant challenges to meeting this goal. The emerging replication crisis in neuroimaging research has exposed limitations of commonly used spatial extent inference (SEI) methods for analyzing imaging data. These include unrealistic assumptions about the spatial covariance function of the imaging data that lead to highly inflated error rates. This project will develop a new robust semiparametric inference framework for neuroimages to address the need for methods that are robust in real-world data, integrate these methods into the pbj R package, and develop a graphical user interface (GUI) to make the methods accessible to neuroimaging scientists. We will use the methods to study how multidimensional symptoms of psychosis are related to brain function and structure in the Psychiatric Genotype-Phenotype Project (PGPP) collected and Vanderbilt University Psychiatric Hospital (VUPH) and to study cross-sectional and longitudinal changes in functional connectivity in the public-access Nathan Kline Institute Rockland Sample (NKI-RS). We will evaluate the methods using realistic bootstrap-based neuroimaging simulations. In Aim 1 we will develop a multidimensional semiparametric procedure for SEI that will leverage computationally efficient parametric and nonparametric bootstraps for inference. In Aim 2 we will expand the framework to repeated measurement models (including longitudinal data), that will allow scientists to robustly model associations of subject-level covariate measurements and brain structure or function. In Aim 3, to address the need for alternatives to hypothesis testing in psychiatric neuroimaging, we will develop semiparametric Coverage Probability Excursion (CoPE) sets that can be used to construct spatial confidence intervals for semiparametric effect sizes. These methods will be made available to the neuroimaging community through the pbj R package and GUI, and disseminated at neuroimaging conferences.