Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data

项目总结/摘要 最近的心理健康研究扩大了多模式大脑成像数据的深度，临床 评估和生理数据。此外，纵向研究对于 捕获疾病进展、治疗反应和复发的轨迹。这些丰富的数据集 为跨领域调查提供了前所未有的机会。然而，急需的统计数据 缺乏探索发现的方法。特别是， 先进的统计方法，用于几个重要目标：分解观察到的大脑连接 揭示潜在神经回路的措施是精神障碍的关键生物标志物，有效地提取 来自成像的低维神经特征以可靠地预测临床结果，例如治疗反应， 并分析纵向多维数据，包括神经成像，临床和行为评估， 研究大脑和行为变化之间的动态相互作用，由于治疗。 在这个竞争性的更新建议中，我们将建立在理论和计算框架的基础上， 在我们之前的奖项中建立了严格和计算效率高的统计方法， 实现上述目标。具体来说，我们计划开发1）稀疏和低秩伊卡（SLR- 伊卡）框架，用于可靠和简约的大脑连接性分解，以揭示 与精神障碍的特定临床症状相关的潜在神经回路; 2）ICA-神经 网络（ICA-NN）预测模型，有效地提取相关的低维线性和非线性 神经功能预测临床结果;（3）纵向多维数据分析工具， 研究由于不同治疗和疾病亚型引起的神经回路的异质性变化，以及 理清神经影像学表型变化与临床症状之间的关系。的 统计学方法将应用于NIH资助的一项重大抑郁症纵向研究 帮助发现特定抑郁症状（例如自杀想法）和差异化的神经回路 治疗反应，并最终帮助导致更有效的治疗个别MDD患者的基础上， 他/她自己的神经回路指纹和行为。我们计划用一个独立的 来自MDD R 01研究的验证队列。将向一般性研究提供方便用户的软件 社区.我们提出的方法发展将直接有利于心理健康研究， 创新的统计工具，有效地提取可靠的和高度相关的低维特征， 神经影像学，以加深对机制的理解，改善MDD和其他精神疾病的治疗 紊乱

Project Summary/Abstract Recent mental health studies have led to an expanded depth of multimodal brain imaging data, clinical assessments and physiological data. In addition, longitudinal studies have become increasingly important to capture the trajectory of disease progression, treatment response and relapse. This wealth of datasets provides an unprecedented opportunity for crosscutting investigations. However, much-needed statistical methods for exploring discoveries are lacking. In particular, there has been very limited development of advanced statistical methods for several important objectives: decompose observed brain connectivity measures to reveal underlying neural circuits which are key biomarkers for mental disorders, effectively extract low dimensional neural features from imaging to reliably predict clinical outcomes such as treatment response, and analyze longitudinal multidimensional data including neuroimaging, clinical and behavioral assessments to study the dynamic interplay between brain and behavior changes due to treatments. In this competing renewal proposal, we will build upon the theoretical and computational framework established in our previous award to develop rigorous and computationally efficient statistical methods to address the aforementioned objectives. Specifically, we plan to develop 1) a sparse and low rank ICA (SLR- ICA) framework for reliable and parsimonious decomposition of brain connectivity measures to reveal underlying neural circuits associated with specific clinical symptoms in mental disorders; 2) an ICA-Neural Network (ICA-NN) predictive model that effectively extracts relevant low dimensional linear and non-linear neural features to predict clinical outcomes; and (3) longitudinal multidimensional data analysis tools for investigating heterogeneous changes in neural circuits due to different treatments and disease subtypes, and disentangle the relationship between changes in neuroimaging phenotypes and clinical symptoms. The statistical methods will be applied to a major NIH funded longitudinal study of major depressive disorder (MDD) to help discover neural circuits underlying specific depressive symptoms (e.g. suicidal thoughts) and differential treatment response, and ultimately help lead to more effective treatment for individual MDD patients based on his/her own neural circuitry fingerprints and behavior. We plan to replicate the findings using an independent validation cohort from an R01 study of MDD. User-friendly software will be made available to general research communities. Our proposed method developments will directly benefit mental health research by providing innovative statistical tools to effectively extract reliable and highly relevant low dimensional features from neuroimaging to deepen mechanistic understanding and improve treatment of MDD and other mental disorders.