Scalable Bayesian methods for big imaging data analysis

抽象！ 这项建议将解决大成像数据统计分析中最及时、最重要的问题。我们的 该项目由规模最大的《青少年大脑认知发展(ABCD)研究》推动 一项关于美国大脑发育和儿童健康的长期研究，由国家科学基金会资助 卫生研究院(NIH)。该方案的创新之处在于：1)我们开发了一种新的贝叶斯图像-on-on-Bayes 具有新的稀疏光滑高斯过程先验的向量回归模型。它使您能够执行 脑活动的高分辨率图像与社会功能的高维向量的相关性分析 环境因素和临床变量。就我们所知，现有的方法都不能有效地 同时对高分辨率图像和协变量的高维向量进行联合分析 2)提出了一种新的贝叶斯图像标量神经网络模型。 稀疏、平滑和空间可变的系数。这种新模式有很大的潜力让它变得更好 与所有现有方法相比，对青少年开始使用物质的风险进行预测；以及更多 重要的是，它将识别与物质使用模式相关的重要成像生物标记物。这 将提供对物质使用开始的病理学的更好的理解；3)我们提出了贝叶斯 一种多模成像数据高维联合矢量图像调和分析模型 回归和带修正的标量图像回归。在潜在的结果框架下，！ 我们还将定义环境因素/电子健康记录对精神病理学的直接影响 因为它们的间接影响是通过大脑功能和/或结构的变化来调节的。4)我们 为所有提出的模型开发可扩展的后验计算算法。这些高效的计算 工具将使在临床和转化性研究中应用统计方法成为可能 申请。我们的方法可以解决关于青少年大脑认知发展的两个关键问题：1) 他们将确定重要的童年经历和社会环境因素，如运动、视频 游戏、社交媒体、不健康的睡眠模式和吸烟，这些都会影响大脑发育；2)了解 大脑发育对物质使用开始和模式的风险的推断，包括详细的数量， 频率、给药途径和共同使用模式。好了！ 好了！

ABSTRACT! This proposal will address the most timely and important issues in statistical analysis of big imaging data. Our project is motivated by "The Adolescent Brain Cognitive Development (ABCD) Study”, which is the largest long-term study of brain development and child health in the United States and is funded by the National Institutes of Health (NIH). Innovative aspects of this proposal are: 1) We develop a new Bayesian image-on- vector regression model with novel sparse and smooth Gaussian process priors. It enables to perform association analysis between high-resolution images of brain activity and high-dimensional vectors of social environmental factors and clinical variables. To the best of our knowledge no existing methods can efficiently and jointly analyze high-resolution images and high-dimensional vectors of covariates simultaneously under a systematic modeling framework; 2) We develop a new Bayesian scalar-on-image neural network model with sparse, smooth, and spatially-varying coefficients. This new model has great potential to make better predictions about the risk of an adolescent initiating substance use compared to all existing methods; and more importantly, it will identify important imaging biomarkers that are associated with substance use patterns. This will provide a better understanding of the pathology of substance use initiation; 3) We propose a Bayesian model for high-dimensional mediation analysis of multimodality imaging data by combining image-on-vector regression and scalar-on-image regression with modifications. Under the potential outcome framework, ! we will define the direct effects of environmental factors/electronic health records on psychopathology, as well as their indirect effects that are mediated through the changes in brain functions and/or structures. 4) We develop scalable posterior computation algorithms for all of the proposed models. These efficient computation tools will enable the possibility to apply the statistical methods in the clinical and translational research and applications. Our methods can address two key questions about adolescent brain cognitive development: 1) they will identify important childhood experiences and social environmental factors, such as sports, video games, social media, unhealthy sleep patterns, and smoking, that affect brain development; 2) understand the inferences of brain development on the risk of substance use initiation and patterns, including detailed quantity, frequency, route of administration, and co-use patterns. ! !