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

功能性神经成像数据的研究提供了令人信服的证据，证明大脑的功能是高度组织的网络。这促使了对计算和统计工具的需求，以可靠地从成像数据构建网络，并随后发现个体或群体之间的模式和差异，例如认知正常的受试者和其他表现出特定病理的受试者之间的模式和差异。定义这些网络的常用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，与网络连接相对应的相关性更强。由于时间趋势和来自生理和其他来源的噪声可能会污染测量信号，因此量化大脑区域之间功能连接的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。该项目的主要目的是开发灵活的fMRI数据统计模型，该模型建立在传统的基于相关性的网络结构上，以提供更可靠和完整的大脑连接图像。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，该模型不假设过程随时间的平稳性，也不对同一区域的体素信号进行初步平均。去除平稳性假设增加了方法的鲁棒性，因为这种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2部分中，给出了不需要参数和模型的功能连通性的新定义。对于任何两个大脑区域，这些区域内所有对体素的时间相关性分布构成了它们的连接概况，并被称为相关密度。分析分布数据的方法，包括探索性分析、聚类分析和回归分析，可用于从这种丰富的表示中提取信息。此外，网络分析仍然可以像传统研究一样，通过评估相关密度的特定分位数（如中位数）来进行。在项目的第三部分中，将通过可靠性和分类分数对真实数据集进行网络构建的验证。这些数据将包括已建立的数据库，如人类连接组项目，从被麻醉的小动物身上收集的数据，以及意识障碍患者受损的大脑。法国国家研究机构（ANR）正在资助一个伙伴项目。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.