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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.

项目总结