Functional Magnetic Resonance Imaging of Brain States and Their Transitions

大脑状态及其转变的功能磁共振成像

• 批准号: RGPIN-2022-03024
• 负责人: Goodyear, Bradley
• 金额: $ 2.04万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764455
• 关键词: Functional; Magnetic; Resonance; Imaging; Brain

Establishing accurate models of human brain region communication is critical for understanding the underlying functions responsible for cognitive processes and generating behaviour. My research program is dedicated to the development of signal processing and data analysis strategies for resting-state functional magnetic resonance imaging (fMRI) studies of inter-regional brain communication at the network level. fMRI tells us about brain activity through its sensitivity to the magnetic properties of blood, which are dependent on blood oxygenation levels. Intrinsic fluctuations of resting-state fMRI signals are highly synchronous between brain regions of a network; the degree of synchrony is termed "connectivity". Connectivity, however, varies as brain regions interact between networks, leading to transitions between temporary brain states associated with cognitive processes. Thus, is it important to understand the dynamic nature of connectivity. Existing connectivity data analysis approaches lack the ability to make statistical inferences regarding the presence of or change in a functional connection at any point in time. This greatly hinders our ability to accurately characterize the complexity of human brain activity and wastes the potential benefits of recently-developed sub-second fMRI. During this proposed project, my lab will develop signal processing and data analysis approaches to accurately model the spatial and temporal dynamics of the human brain's functional networks, with statistical confidence. In Aim 1, we will develop data processing and analysis techniques for resting-state fMRI that use a two-level hierarchical observation model and a Bayesian (conditional) approach to estimate model parameters. Using this approach we can compute the conditional mean and covariance, which in turn permits the computation of a t-statistic at any point in time. In Aim 2, we will develop techniques to generate surrogate data to accurately estimate null distribution data, which is necessary for accurately computing statistical significance. This is non-trivial and is lacking from current dynamic connectivity analysis approaches. We will then validate our approaches and test their reproducibility using simulated datasets and human data from online functional connectome databases. In Aim 3, we will use our approaches in clustering and machine learning algorithms, to define distinct but temporary stationary brain states and the transitions between them. These aims will generate novel technical advances to significantly advance our knowledge of the functional connections of the human brain, and they will provide the future ability to investigate more complex brain systems. Our research program will also provide an excellent environment in which to train high quality personnel in imaging technology development, an area of accelerating growth in Canadian research and industry.

建立准确的人类大脑区域交流模型对于理解负责认知过程和产生行为的潜在功能至关重要。我的研究项目致力于开发信号处理和数据分析策略，用于静息状态功能磁共振成像（fMRI）在网络水平上研究脑区域间通信。功能磁共振成像通过其对血液磁性的敏感性来告诉我们大脑的活动，这取决于血液的氧合水平。静息状态fMRI信号的内在波动在网络的大脑区域之间是高度同步的；同步的程度被称为“连通性”。然而，连通性随着大脑区域在网络之间的相互作用而变化，导致与认知过程相关的临时大脑状态之间的转换。因此，理解连接性的动态特性是很重要的。现有的连接性数据分析方法缺乏在任何时间点对功能连接的存在或变化进行统计推断的能力。这极大地阻碍了我们准确描述人类大脑活动复杂性的能力，并浪费了最近开发的亚秒fMRI的潜在好处。在这个拟议的项目中，我的实验室将开发信号处理和数据分析方法，以统计置信度准确地模拟人类大脑功能网络的时空动态。在目标1中，我们将开发静息状态fMRI的数据处理和分析技术，该技术使用两级分层观察模型和贝叶斯（条件）方法来估计模型参数。使用这种方法，我们可以计算条件均值和协方差，这反过来又允许在任何时间点计算t统计量。在目标2中，我们将开发生成替代数据的技术，以准确估计零分布数据，这对于准确计算统计显著性是必要的。这是非常重要的，并且是当前动态连接性分析方法所缺乏的。然后，我们将验证我们的方法，并使用来自在线功能连接体数据库的模拟数据集和人类数据来测试它们的可重复性。在Aim 3中，我们将使用我们在聚类和机器学习算法中的方法来定义不同但暂时静止的大脑状态以及它们之间的转换。这些目标将产生新的技术进步，显著提高我们对人类大脑功能连接的认识，并将为未来研究更复杂的大脑系统提供能力。我们的研究项目还将提供一个良好的环境，培养成像技术开发方面的高素质人才，这是加拿大研究和工业加速增长的领域。