Semiparametric Inference for Psychiatric Neuroimaging

精神病学神经影像的半参数推理

• 批准号: 10652980
• 负责人: Simon Neil Vandekar
• 金额: $ 42.21万
• 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/10652980
• 关键词: Address; American; Behavioral; Binding; Biological; Brain; Brain imaging; Brain region; Cognitive; Communities; Computer software; Confidence Intervals; Coronavirus; Cross-Sectional Studies; Data; Data Set; Dependence; Development; Diagnosis; Diagnostic; Dimensions; Foundations; Functional Magnetic Resonance Imaging; Genotype; Goals; Hippocampus; Image; Image Analysis; Individual Differences; Intercept; Joints; Location; Measurement; Measures; Mental disorders; Methods; Modeling; Motion; Nature; Outcome; Parameter Estimation; Participant; Phenotype; Population; Probability; Procedures; Psychiatric Hospitals; Psychoses; Public Health; Reporting; Reproducibility; Research; Research Personnel; Sampling; Scientist; Series; Structure; Symptoms; Testing; Time; Universities; Work; biomarker identification; 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.

项目摘要 识别大脑行为关联，以告知个体差异，疾病轨迹， 和神经机制是精神病神经影像学的主要目标之一。大规模多元 神经成像数据的性质，包括大脑结构和功能的空间详细图像， 与高维行为数据相结合，对实现这一目标提出了重大挑战。的 神经影像学研究中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。其中包括对空间的不切实际的假设 成像数据的协方差函数，导致高度膨胀的错误率。该项目将开发一个新的 神经图像的鲁棒半参数推理框架，以满足对鲁棒方法的需求 在实际数据中，将这些方法集成到pbj R包中，并开发图形用户界面 (GUI)让神经成像科学家们也能使用这些方法。我们将使用的方法来研究如何 精神病的多维症状与精神病学中的脑功能和结构有关。 基因型-表型项目（PGPP）收集和范德比尔特大学精神病医院（VUPH），并 研究公共访问内森克莱恩功能连接的横截面和纵向变化 罗克兰研究所样本（NKI-RS）。我们将使用现实的基于引导程序的 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程序， 将利用计算上有效的参数和非参数引导进行推断。在目标2中， 将框架扩展到重复测量模型（包括纵向数据），这将使科学家 对受试者水平协变量测量值与大脑结构或功能的关联进行鲁棒建模。在Aim中 3、为了解决精神病神经影像学中假设检验替代方法的需求，我们将开发 可用于构建空间置信度的半参数覆盖概率偏移（科普）集 半参数效应量的区间。这些方法将用于神经成像 社区通过pbj R包和GUI，并在神经成像会议上传播。

项目成果

Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices.

非中心参数和效果大小指数的准确置信度和贝叶斯间隔估计。

• DOI: 10.1007/s11336-022-09899-x
• 发表时间: 2023-03
• 期刊: PSYCHOMETRIKA
• 影响因子: 3
• 作者: Kang, Kaidi;Jones, Megan T.;Armstrong, Kristan;Avery, Suzanne;McHugo, Maureen;Heckers, Stephan;Vandekar, Simon
• 通讯作者: Vandekar, Simon

Increased amplitude of hippocampal low frequency fluctuations in early psychosis: A two-year follow-up study.

• DOI: 10.1016/j.schres.2022.02.003
• 发表时间: 2022-03
• 期刊: Schizophrenia research
• 影响因子: 4.5
• 作者: McHugo M;Rogers BP;Avery SN;Armstrong K;Blackford JU;Vandekar SN;Roeske MJ;Woodward ND;Heckers S
• 通讯作者: Heckers S

mxnorm: An R Package to Normalize Multiplexed Imaging Data.

• DOI: 10.21105/joss.04180
• 发表时间: 2022-01-01
• 期刊: Journal of open source software
• 作者: Harris, Coleman;Wrobel, Julia;Vandekar, Simon
• 通讯作者: Vandekar, Simon

Erratum to: A Robust Effect Size Index.

勘误表：稳健效应大小指数。

• DOI: 10.1007/s11336-020-09732-3
• 发表时间: 2020
• 期刊: Psychometrika
• 影响因子: 3
• 作者: Vandekar,Simon;Tao,Ran;Blume,Jeffrey
• 通讯作者: Blume,Jeffrey

Incomplete hippocampal inversion in schizophrenia: prevalence, severity, and impact on hippocampal structure.

精神分裂症的不完全海马倒置：患病率、严重程度以及对海马结构的影响。

• DOI: 10.1038/s41380-020-01010-z
• 发表时间: 2021
• 期刊: Molecular psychiatry
• 影响因子: 11
• 作者: Roeske,MaxwellJ;McHugo,Maureen;Vandekar,Simon;Blackford,JenniferUrbano;Woodward,NeilD;Heckers,Stephan
• 通讯作者: Heckers,Stephan