Imaging of Intrinsic Connectivity Networks

描述(由申请人提供)：内在功能连通性(FC)是指通过功能磁共振成像(FMRI)测量的血氧水平依赖(BOLD)信号中自发的、低频(&lt；0.10赫兹)波动的时空一致性。以前的研究表明，即使在休息状态下，即在没有分配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。来自弥散张量成像(DTI)的融合证据进一步表明，内在功能连通性受到解剖连通性的限制，这反映在与个体内在连通性网络(ICN)相关的白质通路的完整性上。ICN映射作为一种神经生物标记物是很有前途的，它将在翻译背景下对理解健康发育和疾病进展具有价值。然而，ICN图谱研究进展的一个关键障碍是当前测量功能连通性的方法分辨率有限。此外，关于ICN与神经系统疾病的表型特征之间的关系还知之甚少。因此，这个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的磁共振成像技术。这将允许更可靠地检测ICN的关键节点(例如，脑干核团和海马子区域等)，而传统的低分辨率和易产生伪影的fMRI无法可靠地测量这些节点。第二个目标是开发新的数据分析算法，以便能够表征与神经损伤的不同表型特征相关的ICN，例如帕金森病(例如，运动功能下降、认知下降和抑郁)。这一新的研究方向：高分辨率的表型特异性ICN易损性作图，使得在神经网络水平上研究多种神经疾病表型之间的机制联系成为可能。为了实现这些目标，本研究有三个具体的目标：1)开发高分辨率和高通量的ICN标测技术，将新的时间域相位正则化并行(T-PREP)成像和最新的扫描加速策略，特别是同时多波段并行成像相结合；2)开发有效的ICN标测固有伪影去除技术，以便分别通过改进的k空间能量谱分析和具有位置相关时间分辨率的新的多波段成像方案来消除敏感性相关的失真和扫描内脉动伪影；3)发展基于表型的连通性分析(PBCA)，以表征帕金森病高分辨率ICN模式、主要表型特征和从一种疾病症状到多种疾病症状的进展之间的关系。

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.