Multivariate Dynamical Systems Methods for Identifying Causal Interactions in fMR

用于识别 fMR 中因果相互作用的多元动态系统方法

• 批准号: 8293093
• 负责人: VINOD MENON
• 金额: $ 19.75万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8293093
• 关键词: Address; Adult; Algorithms; Area; Attention; Autistic Disorder; Biological Models; Brain; Brain imaging; Brain region; Cognition; Cognitive; Communities; Computational Science; Computational algorithm; Computer Simulation; Computer software; Consensus; Data; Data Set; Detection; Development; Drug Formulations; Equation; Evaluation; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Human; Knowledge; Lead; Link; Maximum Likelihood Estimate; Mental disorders; Methods; Mission; Modeling; Motor; Movement; NIH Program Announcements; Neurodevelopmental Disorder; Noise; Parkinson Disease; Performance; Receiver Operating Characteristics; Research; Resource Sharing; Schizophrenia; Sensitivity and Specificity; Sensory; Signal Transduction; Simulate; Solutions; Stimulus; System; Techniques; Technology; Testing; Time; United States National Institutes of Health; Variant; Visual attention; base; cognitive function; computerized tools; hemodynamics; improved; information processing; innovation; nervous system disorder; neurophysiology; novel; performance tests; response; scale up; simulation; tool

DESCRIPTION (provided by applicant): Cognitive information processing depends on dynamical interactions between distributed brain areas. In the past decade, functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for investigating human brain function. Although fMRI research has primarily focused on identifying brain regions that are activated during performance of cognitive tasks, there is growing consensus that cognitive functions emerge as a result of dynamic, context-dependent, causal interactions between multiple brain areas. Devising and validating methods for investigating such interactions has therefore taken added significance. Despite the growing need, the accuracy of current methods for identifying causal interactions in fMRI data remain poorly understood. The overall goal of this proposal is to address a critical need in fMRI by developing and testing new algorithms and software for identifying context-dependent causal interactions between distributed brain regions. We will first develop and validate novel methods based on a Multivariate Dynamical Systems (MDS) framework that overcomes several limitations of existing methods. We will then compare the performance of our new methods with other methods on both simulated and real fMRI data. Important contributions of these proposed studies include (1) development of novel multivariate state space methods for estimating causal interactions between brain regions and (2) first and most detailed evaluation of not only MDS but also other effective connectivity methods using both simulated and experimental fMRI data. Together, these studies will lead to new and improved tools for analyzing functional brain connectivity using fMRI. More generally, our proposed methods will help to advance knowledge of the dynamical basis of human cognitive function and will provide new tools for investigating neurodevelopmental, psychiatric and neurological disorders such as autism, schizophrenia and Parkinson's disease. The proposed studies are highly relevant to the mission of the NIH Exploratory Innovations in Biomedical Computational Science and Technology Program Announcement (PA 09-219), which seeks to encourage development of innovative advanced computational tools for brain imaging.

描述（由申请人提供）：认知信息处理取决于分布式大脑区域之间的动态相互作用。在过去的十年中，功能性磁共振成像（fMRI）已成为研究人脑功能的有力工具。虽然功能磁共振成像研究主要集中在识别在认知任务的执行过程中激活的大脑区域，但越来越多的共识是，认知功能是多个大脑区域之间动态的，依赖于上下文的因果关系相互作用的结果。因此，设计和验证研究这种相互作用的方法具有更大的意义。尽管需求不断增长，但目前用于识别fMRI数据中因果相互作用的方法的准确性仍然知之甚少。这项提案的总体目标是通过开发和测试新的算法和软件来识别分布式大脑区域之间依赖于上下文的因果相互作用，以满足功能磁共振成像的关键需求。我们将首先开发和验证基于多变量动态系统（MDS）框架的新方法，该框架克服了现有方法的几个局限性。然后，我们将比较我们的新方法与其他方法的模拟和真实的fMRI数据的性能。这些研究的重要贡献包括：（1）开发了新的多变量状态空间方法来估计大脑区域之间的因果相互作用;（2）首次使用模拟和实验fMRI数据对MDS以及其他有效的连接方法进行了最详细的评估。总之，这些研究将导致新的和改进的工具，用于分析功能性大脑连接使用功能磁共振成像。更一般地说，我们提出的方法将有助于推进人类认知功能的动力学基础的知识，并将提供新的工具，用于调查神经发育，精神和神经系统疾病，如自闭症，精神分裂症和帕金森氏病。拟议的研究与NIH生物医学计算科学与技术计划公告（PA 09-219）的探索性创新的使命高度相关，该计划旨在鼓励开发用于脑成像的创新先进计算工具。