Statistical Methods for Analyzing Complex, Multi-dimensional Data from Cross-sectional and Longitudinal Mental Health Studies

分析来自横断面和纵向心理健康研究的复杂、多维数据的统计方法

基本信息

批准号：
10396640
负责人：
Ying Guo
金额：
$ 61.41万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-16 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10396640
关键词：
Accident and Emergency department Address Amygdaloid structure Behavioral Symptoms Biological Biological Markers Child Sexual Abuse Clinical Clinical assessments Complex Data Demographic Factors Dependence Development Diagnosis Dimensions Disease Disease Management Environmental Risk Factor Face Fright Functional disorder Future Grant Heterogeneity Image Impact evaluation Individual Knowledge Maps Measurement Mental Health Mental disorders Methodology Methods Modeling National Institute of Mental Health Neurobiology Noise Outcome Pattern Phenotype Post-Traumatic Stress Disorders Procedures Psyche structure Psychiatry Psychophysiology Public Health Reproducibility Research Severities Signal Transduction Statistical Methods Stimulus Strategic Planning Structure Symptoms Trauma United States Validation analytical method base brain behavior burden of illness clinical predictors cohort diagnostic value feature extraction flexibility high dimensionality high risk improved individual variation insight large datasets learning strategy multidimensional data neural circuit neurobiological mechanism neuroimaging neuroimaging marker novel patient population post-trauma predict clinical outcome recruit relating to nervous system response statistical learning trauma exposure trauma symptom user friendly software vector

项目摘要

Project Summary To address the burden of mental illness, National institute of Mental Health encourages development of computational approaches that provide novel ways to understand relationships among complex, large datasets to further the understanding of the underlying pathophysiology of mental diseases. These datasets are multi- dimensional, including clinical assessments, behavioral symptoms, biological measurements such as neu- roimaging and psychophysiological data. The overall objective of this grant is to advance methodology for analyzing such data to more effectively extract relevant information that are predictive of disease, to improve the understanding of individual variability in clinical and neurobiological phenotypes, and to provide the capac- ity to handle both cross-sectional and longitudinal data. Our proposal will leverage two civilian trauma cohorts recruited through the Grady Trauma Project and the Grady Emergency Department Study, and an external validation cohort from the Hill Center study with a similar distribution of trauma exposure. We propose to develop statistically principled, computationally efﬁ- cient statistical learning methods for addressing key challenges in analyzing these large datasets. Challenges include multi-type outcomes, high dimensional data with sparse signals and high noise levels, spatial and tem- poral dependence of neuroimaging data, and heterogeneous effects across patient population. The scientiﬁc premise of this computational psychiatry research is that analytical methods integrating information from brain, behavior, and symptoms will provide much-needed data driven platforms for improving diagnosis and prediction of PTSD and other mental disorders. In this application, we propose: (1) to develop partial generalized tensor regression methods and partial tensor quantile regression methods that can simultaneously achieve accurate prediction of clinical outcomes and efﬁcient feature extraction from high dimensional neuroimaging biomarkers; (2) to develop tensor response quantile regression methods and global inference that can achieve comprehensive and robust understanding of the heterogeneity in high-dimensional neuroimaging phenotypes in terms of environmental factors such as trauma exposure; and (3) to develop and extend methods in Aims 1 and 2 for longitudinal multi-dimensional data that will enable prediction of future post-trauma symptom severity trajectories in terms of neuroimaging biomarkers and robustify the evaluation of the impact of psychophysiological factors on neuroimaging phe- notypes. The proposed methods will be applied to the two Grady studies to address scientiﬁc hypotheses relevant to PTSD research. We will use the Hill Center study as an independent validation cohort to evaluate the reproducibility and generalizability of the ﬁndings. User-friendly software will be developed. The proposed methodology is generally applicable to many other mental health studies with complex multi-dimensional data.

项目概要为了解决精神疾病的负担，国家精神卫生研究所鼓励发展计算方法提供了理解复杂、大型数据集之间关系的新方法进一步了解精神疾病的潜在病理生理学。维度，包括临床评估、行为症状、生物测量，例如神经此项资助的总体目标是推进成像和心理生理学数据的方法。分析此类数据以更有效地提取预测疾病的相关信息，以改善了解临床和神经生物学表型的个体差异，并提供能力能够处理横截面和纵向数据。我们的提案将利用通过格雷迪创伤项目招募的两个平民创伤群体格雷迪急诊室研究，以及来自希尔中心研究的外部验证队列我们建议开发理论上、计算上的效率。用于解决分析这些大型数据集的关键挑战的科学统计学习方法。包括多类型结果、具有稀疏信号和高噪声水平的高维数据、空间和温度神经影像数据的极性依赖性以及患者群体的异质效应。这项计算精神病学研究的前提是整合信息的分析方法来自大脑、行为和症状的数据将为改善症状提供急需的数据驱动平台 PTSD 和其他精神障碍的诊断和预测。在此应用中，我们建议：（1）开发部分广义张量回归方法和部分张量分位数回归方法，可同时实现临床结果的准确预测从高维神经影像生物标志物中有效提取特征（2）以开发张量响应；分位数回归方法和全局推理，可以实现全面而稳健的理解高维神经影像表型在环境因素方面的异质性，例如创伤暴露；(3) 开发和扩展目标 1 和 2 中的纵向多维方法这些数据将能够根据神经影像学预测未来的创伤后症状严重程度轨迹生物标志物并加强心理生理因素对神经影像学影响的评估所提出的方法将应用于两项格雷迪研究以解决科学假设。我们将使用希尔中心研究作为独立验证队列来评估。将开发用户友好的软件。该方法通常适用于许多其他具有复杂多维数据的心理健康研究。