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

CRCNS 美法研究提案：用于功能连通性量化和估计的高级时空统计模型：Q-FunC

• 批准号: 2011715
• 负责人: Alexander Petersen
• 金额: $ 38.89万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011715&HistoricalAwards=false
• 关键词: CRCNS; US; French; Research; Proposal

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.

对功能性神经成像数据的研究提供了令人信服的证据，证明大脑是高度组织化的网络。这促使需要计算和统计工具来从成像数据可靠地构建网络，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与表现出特定病理的其他受试者之间的模式和差异。 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。 由于来自生理和其他来源的时间趋势和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的看似简单的任务实际上需要仔细的统计建模和有效的计算工具，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构建基础上，以提供更强大和完整的大脑连接图。 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。该项目包括三个部分。 在第一部分中，fMRI信号被建模为一个时空过程，在同一个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，也不对来自相同区域中的体素的信号进行初步平均。 平稳性假设的去除增加了方法的鲁棒性，因为这样的属性不太可能在实验条件下保持。 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，给出了一个新的定义，是参数和模型的功能连接。 对于任何两个大脑区域，在这些区域内的所有体素对之间的时间相关性的分布构成了它们的连接性轮廓，并且被称为相关性密度。 用于分析分布数据的方法，包括探索性、聚类和回归分析，可以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。 在该项目的第3部分，将对真实的数据集进行通过可靠性和分类分数验证网络结构的工作。这些将包括建立的数据库，如人类连接组项目，从被麻醉的小动物收集的数据，一个由法国国家研究机构（ANR）资助的配套项目正在进行中。该奖项反映了NSF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进行评估，被认为值得支持的搜索.