CRCNS US-French Research Proposal: Advanced Spatiotemporal Statistical Models for Quantification and Estimation of Functional Connectivity: Q-FunC

对功能神经成像数据的研究提供了令人信服的证据，证明大脑是高度组织的网络。这促使了对计算和统计工具的需求，以根据成像数据可靠地构建网络，并随后发现个人或群体之间的模式和差异，例如在认知正常的受试者和其他表现出特定病理的受试者之间。定义这些网络的常见方法是计算在大脑中不同位置(体素)测量的信号之间的时间相关性，其中更强的相关性对应于网络连接。由于时间趋势以及来自生理和其他来源的噪声可能会污染测量的信号，量化大脑区域之间的功能连接这一看似简单的任务实际上需要仔细的统计建模和高效的计算工具，以便得出与大脑网络相关的可靠推断。该项目的主要目标是开发灵活的fMRI数据统计模型，该模型建立在传统的基于相关性的网络构建的基础上，以提供更健壮和完整的大脑连接图景。该项目将开发一个图形用户界面，用于计算和可视化连接属性。该项目将为学员提供广泛的国际合作经验。该项目由三个部分组成。在第一部分中，fMRI信号被建模为一个时空过程，其中同一大脑区域内的体素共享一个共同的随机结构。与传统方法相比，该模型不假定过程随时间的平稳性，也不执行来自同一区域中的体素的信号的初步平均。去掉平稳性假设增加了方法的稳健性，因为这样的性质在实验条件下不太可能成立。来自功能数据分析的方法允许使用所有体素数据进行估计。在第二部分中，给出了函数连通性的一个新的定义，它是与参数和模型无关的。对于任意两个大脑区域，这些区域内所有体素对的时间相关性分布构成了它们的连通性轮廓，称为相关性密度。分析分布数据的方法，包括探索性、聚类性和回归分析，可以用来从这种丰富的表示中提取信息。此外，网络分析仍然可以像在传统研究中一样，通过评估相关密度的特定分位数来执行，例如中位数。在项目的第三部分，将在真实数据集上通过可靠性和分类分数来验证网络建设。这些将包括已建立的数据库，如人类连接体项目，从麻醉的小动物收集的数据，以及意识障碍患者的大脑受损。法国国家研究机构(ANR)正在资助一个配套项目。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Studies of functional neuroimaging data have provided compelling evidence that brains function as highly organized networks. This has prompted the need for computational and statistical tools to reliably construct networks from imaging data, and subsequently to discover patterns and differences between individuals or groups, for instance between cognitively normal subjects and others exhibiting particular pathologies. The common approach to defining these networks is to compute temporal correlations between signals measured at distinct locations (voxels) in the brain, with stronger correlations corresponding to network connections. Due to temporal trends and noise from physiological and other sources that can contaminate the measured signals, the seemingly simple task of quantifying functional connectivity between brain regions in fact requires careful statistical modeling and efficient computational tools in order to draw reliable inferences related to brain networks. The primary aim of this project is to develop flexible statistical models of fMRI data that build on conventional correlation-based network construction to provide a more robust and complete picture of connectivity in the brain. The project will develop a graphical user interface for computing and visualizing connectivity properties. And the project will provide trainees with extensive international collaborative experience. The project consists of three parts. In part 1, fMRI signals are modeled as a spatio-temporal process, where voxels within the same brain region share a common stochastic structure. In contrast to conventional methods, the model does not assume stationarity of the process over time or perform a preliminary averaging of signals from voxels in the same region. The removal of the stationarity assumption adds robustness to the method since such a property is unlikely to hold in experimental conditions. Methods from functional data analysis allow for estimation using all voxel-wise data. In part 2, a novel definition of functional connectivity is given that is parameter- and model-free. For any two brain regions, the distribution of temporal correlations across all pairs of voxels within these regions constitutes their connectivity profile, and is termed the correlation density. Methods for analyzing distributional data, including exploratory, clustering, and regression analyses, can be used to extract information from this rich representation. Additionally, network analyses can still be performed as in conventional studies by evaluating specific quantiles of the correlation density, such as the median. In part 3 of the project, validation of network construction through reliability and classification scores will be carried out on real data sets. These will include established data banks such as the Human Connectome Project, data gathered from small animals that have been anesthetized, and lesioned brains of individuals with consciousness disorders.A companion project is being funded by the French National Research Agency (ANR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.