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

• 批准号: 10611987
• 负责人: Ying Guo
• 金额: $ 61.41万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-16 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611987
• 关键词: Accident and Emergency department; Address; Amygdaloid structure; Behavior; Behavioral Symptoms; Biological; Biological Markers; Brain; 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; 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; neural circuit; neurobiological mechanism; neuroimaging; neuroimaging marker; novel; patient population; post-trauma; predict clinical outcome; recruit; resilience; response; statistical learning; trauma exposure; trauma symptom; user friendly software; vector

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.

项目摘要 为了解决精神疾病的伯宁，国家心理健康研究所鼓励发展 计算方法提供了了解复杂，大数据集之间关系的新颖方法 进一步理解心理疾病的潜在病理生理学。这些数据集是多的 维度，包括临床评估，行为症状，生物学测量，例如neu- 流浪和心理生理数据。这笔赠款的总体目的是推进方法论 分析此类数据以更有效地提取可预测疾病的相关信息，以改善 了解临床和神经生物学表型中个体变异性的理解，并提供了能力 它是处理横截面和纵向数据。 我们的提议将利用通过Grady创伤项目招募的两个平民创伤人群，并 Grady急诊科研究，以及Hill Center研究的外部验证队列 创伤暴露的相似分布。我们建议开发统计上的校长，计算有效的 针对分析这些大数据集的关键挑战的CIENT统计学习方法。挑战 包括多类结果，具有稀疏信号和高噪声水平的高维数据，空间和温度 神经影像数据的孔子依赖性以及患者人群之间的异质作用。科学 这项计算精神病学研究的前提是，分析方法整合信息 大脑，行为和症状将提供急需的数据驱动平台以改善 PTSD和其他精神障碍的诊断和预测。 在此应用程序中，我们建议：（1）开发部分广义张量回归方法和部分 张量的分位数回归方法可以轻松实现临床结果的准确预测 以及从高维神经影像生物标志物中提取有效的特征； （2）发展张量响应 分位数回归方法和全球推断，可以实现全面，强大的理解 高维神经影像型的异质性在环境因素方面 创伤暴露； （3）在AIM 1和2中开发和扩展方法，以进行纵向多维 可以在神经影像方面预测未来创伤后症状严重性轨迹的数据 生物标志物并鲁棒性评估心理生理因素对神经影像学的影响 Notypes。提出的方法将应用于两项Grady研究，以解决科学假设 与PTSD研究有关。我们将使用山丘中心研究作为独立验证队列来评估 发现的可重复性和概括性。将开发用户友好的软件。提议 方法学通常适用于许多其他具有复杂多维数据的心理健康研究。