Project 4 Quantitative Methods for Brain Connectivity Network Estimation & Interference in Functional Magnetic Resonance Imaging

项目摘要 对内在神经活动的空间和时间模式的估计已经成为一种流行的方法， 深入了解大脑的功能组织。一种用于测量这些模式的方法 自发活动，称为功能连接，是功能磁共振成像（MRI） 而其他研究开发了实验任务，用于学习特定环境中的连接性。 大脑的各个区域。功能连接图已经被证明会随着年龄、训练、 意识和疾病状态。在某些假设下，这些功能连接图 显示出大脑不同区域之间的独立性偏差，通常包括空间上的 不协调的区域。功能连接图已被用于了解大脑的差异 通过基于体素的连接模式对体素进行聚类来确定疾病组之间的激活模式。我们 提出了一个总体框架，用于估计大脑连接图与感兴趣的预测因子之间的关联 在使用协方差回归控制混杂因素后-一种统计建模方法， 使用协方差结果的特殊结构来改进参数估计。统计 将在该模型中评估预测因子与结果图的关联的显著性， 校正其他变量的影响。协方差回归中连通图的估计 地图是成果的框架尚未开发。我们提出的框架将扩展模型 并在静态功能连接性分析的背景下纳入基于证据的现实假设 其中，我们假设在扫描会话期间和动态连接中连接性是恒定的 分析扫描会话期间连接性变化的时变模式。一个 这个建议的一个重要贡献是将模型扩展到高维设置， 基于功能磁共振成像的连接图通常很大。拟议的框架将用于了解 在功能性MRI研究中，在适应性学习任务期间大脑的功能组织， 视觉-运动连接在任务期间发生变化。

PROJECT SUMMARY Estimation of the spatial and temporal patterns of intrinsic neural activity has become a popular approach to gaining insight into the functional organization of the brain. One method for measuring these patterns of spontaneous activity, referred to as functional connectivity, is functional Magnetic Resonance Imaging (MRI) measured at rest while other studies developed experimental tasks for learning about connectivity in specific areas of the brain. Functional connectivity maps have been shown to change with age, training, levels of consciousness, and disease status. Under certain assumptions, these functional connectivity maps demonstrate deviations from independence between various areas of the brain, often including spatially incongruous areas. Functional connectivity maps have been utilized to learn about differences of brain activation patterns between disease groups via clustering voxels based on their connectivity patters. We propose a general framework for estimating associations of brain connectivity maps with predictors of interest after controlling for confounders using covariance regression - a statistical modeling approach that allows for using the special structure of covariance outcomes for improved parameter estimation. The statistical significance of the association of the predictors with the outcome maps will be assessed in this model while correcting for the effects of other variables. The estimation of connectivity maps in a covariance regression framework, where the map is the outcome is underdeveloped. Our proposed framework will extend the model and incorporate evidence based realistic assumptions in the context of static functional connectivity analysis where we assume that the connectivity is constant during the scanning session and in dynamic connectivity analyses where time-varying patterns of changes in connectivity during the scanning session is of interest. An important contribution of this proposal is the extension of the model to high dimensional settings as the connectivity maps based on fMRI are often large. The proposed framework will be used to learn about functional organization of the brain during an adaptation learning task in a functional MRI study focusing on visual-motor connectivity changes during the task.