Imaging of Intrinsic Connectivity Networks

内在连接网络的成像

• 批准号: 8473926
• 负责人: NAN-KUEI CHEN
• 金额: $ 29.83万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-30 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8473926
• 关键词: Acceleration; Address; Algorithms; Anatomy; Architecture; Behavioral; Biological Markers; Brain Stem; Brain region; Calibration; Cardiac; Cell Nucleus; Cerebellum; Clinical; Cognitive; Data; Data Analyses; Detection; Development; Diffusion Magnetic Resonance Imaging; Disease; Disease Progression; Excision; Frequencies; Functional Magnetic Resonance Imaging; Goals; Hippocampus (Brain); Image; Imaging Techniques; Impaired cognition; Impairment; Individual; Knowledge; Location; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mental Depression; Methods; Morphologic artifacts; Motion; Motor; Neurologic; Neurons; Neuropsychological Tests; Noise; Parkinson Disease; Pathway interactions; Patients; Pattern; Phase; Phenotype; Physiological; Predisposition; Procedures; Protocols documentation; Research; Resolution; Rest; Sampling; Scanning; Scheme; Signal Transduction; Spectrum Analysis; Symptoms; Techniques; Time; base; blood oxygen level dependent; data acquisition; design; disease phenotype; improved; nervous system disorder; novel; patient population; relating to nervous system; spatiotemporal; tool; white matter

DESCRIPTION (provided by applicant): Intrinsic functional connectivity (FC) refers to the spatiotemporal coherence of spontaneous, low-frequency (<0.10 Hz) fluctuations in the blood-oxygen level dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI). Previous research suggest that separable networks of intrinsic functional connectivity can be reliably identified even in the resting state, that is, in the absence of an assigned cognitive or behavioral task. Converging evidence from diffusion tensor imaging (DTI) suggests further that intrinsic functional connectivity is constrained by anatomical connectivity, as reflected in the integrity of white matter pathways associated with individual intrinsic connectivity network (ICN). ICN mapping is promising as a neural biomarker that will be valuable in translational contexts for understanding both healthy development and disease progression. A critical barrier to progress in research on ICN mapping, however, is the limited resolution of current methods for measuring functional connectivity. In addition, little is known regarding the relation of ICN to phenotypic signatures of neurological diseases. Thus, the goals in this project are twofold. The first goal is to design novel high-resolution and high-throughput MRI techniques. This will allow a more reliable detection of critical nodes (e.g., brainstem nuclei and sub-regions of hippocampus, among others) of ICNs, which cannot be reliably measured with conventional low-resolution and artifact-prone fMRI. The second goal is to develop novel data analysis algorithms, so that the ICNs that are commonly or dissociably correlated with different phenotypic signatures of neurological impairment, such Parkinson's disease (e.g., motor function decline, cognitive decline and depression) can be characterized. This new research direction: high-resolution mapping of phenotype-specific ICN vulnerability, makes it possible to investigate the mechanistic connection, at the level of neuronal network, among multiple phenotypes of neurological diseases. To achieve these goals, this research has three specific aims: 1) Development of high-resolution and high-throughput ICN mapping techniques, by integrating a novel time-domain phase-regularized parallel (T-PREP) imaging and state-of-the-art scan acceleration strategies, specifically the simultaneous multi-band parallel imaging; 2) Development of effective and inherent artifact removal techniques for ICN mapping, so that the susceptibility related distortions and intra-scan pulsation artifacts can be eliminated with an improved k-space energy spectrum analysis and a novel multi-band imaging scheme with location-dependent temporal-resolution, respectively; 3) Development of phenotype-based connectivity analysis (PBCA) to characterize the associations among high-resolution ICN patterns, major phenotypic signatures, and the progression from one to multiple disease symptoms in Parkinson's disease.

描述（由申请人提供）：内在功能连接性（FC）是指通过功能性磁共振成像（fMRI）测量的血氧水平依赖性（BOLD）信号中自发低频（<0.10 Hz）波动的时空相干性。先前的研究表明，即使在静息状态下，也就是说，在没有指定的认知或行为任务的情况下，也可以可靠地识别内在功能连接的可分离网络。来自扩散张量成像（DTI）的会聚证据进一步表明，内在功能连接受到解剖连接的约束，如与个体内在连接网络（ICN）相关的白色通路的完整性所反映的。ICN映射作为神经生物标志物是有希望的，其在理解健康发育和疾病进展的翻译背景下将是有价值的。然而，ICN映射研究进展的一个关键障碍是目前测量功能连接性的方法分辨率有限。此外，关于ICN与神经系统疾病表型特征的关系知之甚少。 因此，该项目的目标是双重的。第一个目标是设计新的高分辨率和高通量MRI技术。这将允许更可靠地检测关键节点（例如，脑干核和海马的子区域等）的ICN，这不能可靠地测量与传统的低分辨率和伪影倾向的功能磁共振成像。第二个目标是开发新的数据分析算法，使得通常或不可分离地与神经损伤的不同表型特征相关的ICN，如帕金森病（例如，运动功能衰退、认知衰退和抑郁症）。这个新的研究方向：表型特异性ICN脆弱性的高分辨率映射，使得有可能在神经元网络水平上研究神经系统疾病的多种表型之间的机制联系。 为了实现这些目标，本研究有三个具体的目标：1）通过集成新的时域相位正则化并行（T-PREP）成像和最先进的扫描加速策略，特别是同时多波段并行成像，发展高分辨率和高通量的ICN映射技术; 2）开发用于ICN标测的有效和固有的伪影去除技术，从而可以利用改进的k-空间能谱分析和新颖的多-分别具有位置相关时间分辨率的波段成像方案; 3）开发基于表型的连接性分析（PBCA），以表征高分辨率ICN模式、主要表型特征以及帕金森病中从一种疾病症状到多种疾病症状的进展之间的关联。