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: 7670362
• 负责人: Catherine Elizabeth Chang
• 金额: $ 4.12万
• 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/7670362
• 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; 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; Phase; Physiological; Physiology; 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; information processing; insight; patient population; 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）：拟议研究的总体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（IN）是在无任务功能磁共振成像（fMRI）中一致识别的不同大脑区域的集合，并且被认为是感觉、运动和认知功能的基础。进一步的理解需要对网络动态的详细了解，无论是在无任务的“休息状态”还是在认知功能期间。 Ins 和传统的“任务活跃”区域之间有哪些相互作用？是否存在沿着网络的信息整合？ 维护任务响应？目前的提案重点是使用功能磁共振成像来阐明称为默认模式网络（DMN）的特定智能网络的动态特性。 在具体目标 1 中，我们对 DMN 和任务激活区域之间任务诱发信息流的方向进行建模。对简短的、注意力要求较高的任务的初始反应的计时将通过起始潜伏期分析获得，并且（长）刺激后间隔中时间依赖性的演变将使用时变格兰杰因果关系进行量化。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 区域之间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。我们在长时间静息状态扫描中采用时变功能连接和 ICA 来检查随时间变化的程度，并将连接强度的变化与可能表示觉醒或意识水平变化的生理变量联系起来。在目标 3 中，我们校正了由于血管反应性导致的血流动力学潜伏期的区域差异，以便 BOLD 信号的相对时间更紧密地反映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，并开发纠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该网络如何导致认知功能障碍和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据称作用而受到广泛关注，但它也被证明可以作为阿尔茨海默病、重性抑郁症、注意力缺陷多动症和精神分裂症等疾病的生物标志物。当前的提案提供了一个用于研究 DMN（和其他 IN）的时间行为的框架，这将作为进一步研究患者群体中的网络响应和连接性的基础。