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: 8121040
• 负责人: VINOD MENON
• 金额: $ 23.7万
• 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 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8121040
• 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. PUBLIC HEALTH RELEVANCE: In the past decade, functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for investigating human brain function and dysfunction. Although fMRI studies of brain function have primarily focused on identifying brain regions that are activated during performance of perceptual or cognitive tasks, there is growing consensus that cognitive functions emerge as a result of dynamic context-dependent interactions between multiple brain areas. Developing new methods for investigating causal interactions in fMRI data has therefore taken added significance; the overall goal of this proposal is to address this critical need by developing new methods for studying causal interactions and brain connectivity between distributed brain regions during cognition.

描述(申请人提供)：认知信息处理依赖于分布的大脑区域之间的动态交互。在过去的十年里，功能磁共振成像(FMRI)已经成为研究人类大脑功能的强大工具。尽管功能磁共振成像研究主要集中在识别在认知任务执行过程中被激活的大脑区域，但越来越多的人达成共识，认为认知功能的出现是多个大脑区域之间动态的、上下文相关的因果交互作用的结果。因此，设计和验证调查这种相互作用的方法具有额外的意义。尽管需求日益增长，但目前用于识别功能磁共振数据中因果交互作用的方法的准确性仍然知之甚少。这项提议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的上下文相关因果交互，以满足功能磁共振成像中的关键需求。我们将首先开发和验证基于多元动态系统(MDS)框架的新方法，该框架克服了现有方法的几个限制。然后我们将在模拟和真实的fMRI数据上将我们的新方法与其他方法的性能进行比较。这些研究的重要贡献包括(1)发展了新的多变量状态空间方法来估计大脑区域之间的因果交互作用；(2)不仅对MDS而且使用模拟和实验fMRI数据对其他有效的连接方法进行了首次和最详细的评估。总之，这些研究将导致使用fMRI分析功能性大脑连接的新的和改进的工具。更广泛地说，我们提出的方法将有助于增进对人类认知功能的动力学基础的了解，并将为研究神经发育、精神和神经疾病，如自闭症、精神分裂症和帕金森病提供新的工具。拟议的研究与美国国立卫生研究院生物医学计算科学和技术探索性创新计划公告(PA 09-219)的使命高度相关，该计划旨在鼓励开发创新的高级计算工具用于脑成像。 与公共健康相关：在过去的十年中，功能磁共振成像(FMRI)已成为研究人类大脑功能和功能障碍的强大工具。尽管对大脑功能的fMRI研究主要集中在识别在执行知觉或认知任务时被激活的大脑区域，但越来越多的人达成共识，即认知功能的出现是多个大脑区域之间动态的上下文相关交互作用的结果。因此，开发新的方法来研究功能磁共振数据中的因果交互作用具有额外的意义；这项提议的总体目标是通过开发研究认知过程中分布的大脑区域之间的因果交互作用和大脑连接的新方法来满足这一关键需求。