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的大脑网络在认知中的作用。内在网络（INS）是在无任务功能性磁共振成像（fMRI）中始终识别的不同大脑区域的集合，并被认为是感觉，运动，运动和认知功能的基础。进一步的理解需要有关网络动态的详细知识，包括无任务的“休息状态”以及在认知功能期间。 INS与传统的“任务活性”区域之间有什么相互作用？是否存在可能沿网络的信息集成 提供任务响应？当前的提案着重于使用fMRI阐明特定在被称为默认模式网络（DMN）的动态属性。 在特定目标1中，我们建模DMN和任务激活区域之间任务引起的信息流的方向。最初对简短，苛刻任务的初始响应的时间表将使用发作潜伏分析获得，并且将使用时间变化的Granger因果关系来量化（长）后刺激后间隔中时间依赖的演变。具体目的2和3涉及使用fMRI研究INS的方法论问题。在AIM 2中，我们量化了无任务休息状态中DMN区域之间耦合强度和相位差异的平稳性。当前，大多数研究使用算法定义INS，这些算法认为，随着时间的流逝，区域中的耦合一直保持恒定。我们在长时间的休息状态扫描中采用时变功能连接性和ICA来检查随着时间的变化程度，并将连接强度的变化与生理变量联系起来，这可能表示可能表示唤醒或意识水平的变化。在AIM 3中，我们纠正了由于血管反应性引起的血液动力学潜伏期的区域差异，因此大胆信号的相对时机更加紧密地反映了基本的神经相互作用。我们的方法是使用呼吸任务来衡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的时间行为将为网络如何构成认知功能障碍以及功能的宝贵见解。尽管DMN因其在情节记忆和任务表现中的作用而受到广泛关注，但它也被证明是有效作为阿尔茨海默氏病，严重抑郁症，ADHD和精神分裂症等疾病的生物标志物。当前的建议提供了研究DMN（和其他INS）的时间行为的框架，该框架将作为进一步研究患者人群网络响应和连通性的基础。