Temporal Characteristics of Intrinsic Brain Networks using fMRI

使用功能磁共振成像的内在大脑网络的时间特征

• 批准号: 7485324
• 负责人: Catherine Elizabeth Chang
• 金额: $ 4.1万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7485324
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Arousal; Attention; Attention deficit hyperactivity disorder; Awareness; Behavior; Biological Markers; Blood Vessels; Brain; Brain region; Characteristics; Cognition; Cognitive; Collection; Computer information processing; Condition; Coupling; Data Analyses; Dependence; Detection; Disease; Episodic memory; Etiology; Event; Evolution; Frequencies; Functional Magnetic Resonance Imaging; Goals; Impaired cognition; Investigation; Knowledge; Major Depressive Disorder; Maps; Measures; Methods; Metric; Modeling; Motor; Patients; Phase; Physiological; Physiology; Population; Process; Property; Relative (related person); Research; Response Latencies; Rest; Role; Running; Scanning; Schizophrenia; Sensory; Short-Term Memory; Signal Transduction; Stimulus; Task Performances; Time; base; cognitive function; hemodynamics; independent component analysis; insight; regional difference; relating to nervous system; response; stimulus interval; time use

DESCRIPTION (provided by applicant): The broad goal of the proposed research is to further understand the role of intrinsic brain networks in cognition. Intrinsic networks (INs) are collections of disparate brain regions that are consistently identified in task-free functional magnetic resonance imaging (fMRI), and are presumed to underlie sensory, motor, and cognitive functions. Further understanding requires detailed knowledge about the dynamics of the networks, both in task-free "resting state" as well as during cognitive function. What are the interactions between Ins and the traditional "task-active" regions? Is there an integration of information along the networks that may subserve task responses? The current proposal focuses on using fMRI to elucidate dynamic properties of a particular IN known as the default-mode network (DMN). In Specific Aim 1, we model directions of task-evoked information flow among the DMN and task activated regions. The chronometry of initial responses to a brief, attentionally demanding task will be obtained using onset latency analysis, and the evolution of temporal dependences in the (long) poststimulus interval will be quantified using time-varying Granger causality. Specific Aims 2 and 3 pertain to methodological issues in studying INs using fMRI. In Aim 2, we quantify the stationarity of coupling strength and phase differences among regions of the DMN in task-free resting state. Currently, the majority of studies define INs using algorithms that assume that the coupling of IN regions remains constant over time. We employ time-varying functional connectivity and ICA over a long resting-state scan to examine the degree of change overtime, and relate changes in connectivity strength to physiological variables that may signify changes in arousal or awareness level. In Aim 3, we correct for regional differences in hemodynamic latency due to vascular reactivity, so that the relative timing of BOLD signals reflects underlying neural interactions more closely. Our approach is to measure vascular response latencies across the brain using a breath holding task, and develop methods for correcting for these non-neural latency differences. Investigating the temporal behavior of the DMN will provide valuable insight into how the network underlies cognitive dysfunction, as well as function. While the DMN is receiving widespread attention for its purported role in episodic memory and task performance, it is also proving effective as a biomarker for disorders such as Alzheimer's Disease, major depression, ADHD, and schizophrenia. The current proposal provides a framework for studying the temporal behavior of the DMN (and other INs), which will serve as a basis for further investigation of network responses and connectivity in patient populations.

描述（由申请人提供）：拟议研究的广泛目标是进一步了解内在大脑网络在认知中的作用。内在网络（Intrinsic networks，IN）是一组不同的脑区，在无任务功能磁共振成像（fMRI）中被一致识别，并被认为是感觉、运动和认知功能的基础。进一步的理解需要详细了解网络的动态，无论是在无任务的“休息状态”，以及在认知功能。Ins与传统的“任务活跃”区域之间有哪些互动？是否有信息的整合沿着网络， 支持任务响应？目前的建议侧重于使用功能磁共振成像来阐明一个特定的IN称为默认模式网络（DMN）的动态特性。 在具体目标1中，我们模型的DMN和任务激活区之间的任务诱发的信息流的方向。将使用起始潜伏期分析获得对简短的、需要注意力的任务的初始反应的计时法，并且将使用随时间变化的格兰杰因果关系来量化（长）刺激后间隔中的时间依赖性的演变。具体目标2和3涉及使用fMRI研究IN的方法学问题。在目标2中，我们量化了无任务静息状态下DMN各区域之间的耦合强度和相位差的平稳性。目前，大多数研究使用假设IN区域的耦合随时间保持恒定的算法来定义IN。我们采用随时间变化的功能连接和伊卡在长时间的休息状态扫描检查随时间变化的程度，并与连接强度的变化，可能意味着唤醒或意识水平的变化的生理变量。在目标3中，我们校正了由于血管反应性而导致的血流动力学潜伏期的区域差异，使得BOLD信号的相对时序更紧密地反映了潜在的神经相互作用。我们的方法是使用屏气任务测量整个大脑的血管反应潜伏期，并开发用于校正这些非神经潜伏期差异的方法。 研究DMN的时间行为将为了解网络如何成为认知功能障碍的基础以及功能提供有价值的见解。虽然DMN因其在情景记忆和任务表现中的作用而受到广泛关注，但它也被证明是阿尔茨海默病，重度抑郁症，ADHD和精神分裂症等疾病的有效生物标志物。目前的建议提供了一个框架，用于研究DMN（和其他IN）的时间行为，这将作为进一步研究患者人群中的网络响应和连接的基础。