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

项目摘要 为了解决精神疾病的负担，国家精神卫生研究所鼓励发展 计算方法提供了新的方法来理解复杂的大型数据集之间的关系， 进一步了解精神疾病的潜在病理生理学。这些数据集是多- 维度，包括临床评估，行为症状，生物测量，如neu， 影像学和心理生理学数据。这笔赠款的总体目标是推进以下方面的方法： 分析这些数据以更有效地提取预测疾病的相关信息， 了解临床和神经生物学表型的个体差异，并提供卡帕克- 能够同时处理横截面和纵向数据。 我们的建议将利用通过格雷迪创伤项目招募的两个平民创伤小组， Grady急诊科研究和希尔中心研究的外部验证队列， 创伤暴露的相似分布。我们建议开发统计原则，计算效率- 这是一种非常有效的统计学习方法，用于解决分析这些大型数据集的关键挑战。挑战 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TEM， 神经影像学数据的时间依赖性和患者人群的异质性效应。科学 这种计算精神病学研究的前提是，分析方法整合信息 从大脑，行为和症状将提供急需的数据驱动的平台，以改善 PTSD和其他精神障碍的诊断和预测。 在这个应用中，我们提出：（1）发展偏广义张量回归方法和偏广义张量回归方法。 张量分位数回归方法，可以同时实现准确的预测临床结果 和有效的特征提取从高维神经影像生物标志物;（2）开发张量响应 分位数回归方法和全局推理，可以实现全面和强大的理解 在环境因素方面， 创伤暴露;（3）发展和扩展目标1和2中的方法， 这些数据将能够在神经成像方面预测未来创伤后症状严重程度的轨迹 生物标志物和鲁棒性的心理生理因素对神经影像学phe的影响的评价， 无名氏所提出的方法将应用于两个格雷迪研究，以解决科学假设 与PTSD研究有关。我们将使用希尔中心的研究作为一个独立的验证队列， 调查结果的可重复性和普遍性。将开发方便用户的软件。拟议 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。

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.