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

项目摘要 为了应对精神疾病的负担，国家精神卫生研究所鼓励发展 计算方法为理解复杂的大型数据集之间的关系提供了新的方法 以加深对精神疾病潜在病理生理学的理解。这些数据集是多个 维度，包括临床评估、行为症状、生物学测量，如Neu- 影像和心理生理学数据。这笔赠款的总体目标是推进 分析这些数据以更有效地提取可预测疾病的相关信息，以改进 了解个体在临床和神经生物学表型上的差异性，并为临床和神经生物学表型的个体差异提供能力。 用于同时处理横截面和纵向数据的ITE。 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队列和 Grady急诊部的研究，以及希尔中心研究的外部验证队列 相似的创伤暴露分布。我们建议开发统计原则性的、计算性的fi- 解决这些大型数据集分析中的关键挑战的有效统计学习方法。挑战 包括多种类型结果、具有稀疏信号和高噪声水平的高维数据、空间和时间 神经影像数据的局部依赖性，以及患者群体的异质性影响。The Science entific 这种计算精神病学研究的前提是整合信息的分析方法 从大脑、行为和症状出发，将为改进提供急需的数据驱动平台 诊断和预测创伤后应激障碍和其他精神障碍。 在这一应用中，我们提出：(1)发展部分广义张量回归方法和部分广义张量回归方法。 可同时实现临床结果准确预测的张量分位数回归方法 从高维神经影像生物标志物中提取有效的fi特征；(2)建立张量响应 分位数回归方法和全局推理，可以实现全面和稳健的理解 高维神经成像表型的异质性与环境因素的关系 创伤暴露；以及(3)开发和扩展目标1和目标2中的纵向多维方法 能够在神经成像方面预测未来创伤后症状严重程度轨迹的数据 心理生理因素对神经影像影响的生物标记物和生理学评价 无类型。建议的方法将被应用于两项格雷迪研究，以解决科学fic假说。 与创伤后应激障碍研究相关。我们将使用希尔中心的研究作为独立的验证队列来评估 fi编码的重复性和通用性。将开发用户友好的软件。建议数 方法论一般适用于其他许多具有复杂多维数据的心理健康研究。

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.