Semiparametric Inference for Psychiatric Neuroimaging

精神病学神经影像的半参数推理

• 批准号: 10031157
• 负责人: Simon Neil Vandekar
• 金额: $ 44.61万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10031157
• 关键词: Address; American; Behavioral; Biological; Biological Markers; Brain; Brain imaging; Brain region; Cognitive; Communities; Computer software; Confidence Intervals; Cross-Sectional Studies; Data; Data Set; Dependence; Development; Diagnosis; Diagnostic; Dimensions; Foundations; Functional Magnetic Resonance Imaging; Gaussian model; Genotype; Goals; Hippocampus (Brain); Image; Image Analysis; Individual Differences; Institutes; Intercept; Joints; Lead; Location; Measurement; Measures; Mental disorders; Methods; Modeling; Motion; Nature; Outcome; Participant; Partner in relationship; Phenotype; Population; Probability; Procedures; Psychiatric Hospitals; Psychotic Disorders; Public Health; Reporting; Reproducibility; Research; Research Personnel; Sampling; Scientist; Series; Structure; Symptoms; Testing; Time; Universities; Work; base; brain behavior; experimental study; graphical user interface; high dimensionality; improved; interest; meetings; neuroimaging; neuromechanism; psychotic symptoms; response; semiparametric; simulation; symposium; vector

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.

项目总结