Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment

评估轻度认知障碍患者的大规模大脑连接

• 批准号: 8723036
• 负责人: Tianming Liu
• 金额: $ 27.45万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8723036
• 关键词: Algorithms; Alzheimer' s Disease; Anatomic Models; Anatomy; Area; Atlases; Brain; Brain Mapping; Brain imaging; Brain region; Classification; Communities; Data; Data Set; Diffusion Magnetic Resonance Imaging; Disease Progression; Early Diagnosis; Fiber; Fingerprint; Functional Magnetic Resonance Imaging; Image; Individual; Joints; Knowledge; Learning; Literature; Location; Magnetic Resonance Imaging; Maps; Measurement; Measures; Medical center; Modeling; Names; Network-based; Neurosciences Research; Output; Pathogenesis; Patients; Pattern; Pilot Projects; Play; Population; Property; Recruitment Activity; Reporting; Reproducibility; Research; Rest; Role; Scanning; Science; Site; System; Weight; base; insight; interest; mild cognitive impairment; multitask; neuroimaging; next generation; novel; open source; predictive modeling; public health relevance; success; white matter

DESCRIPTION (provided by applicant): There has been significant amount of effort in the literature in measuring the hypothesized widespread structural and functional connectivity alterations in MCI by diffusion tensor imaging (DTI) and/or resting state fMRI (R-fMRI). For instance, the ongoing ADNI-2 project already released dozens of DTI and R-fMRI datasets for early MCI patients. However, a fundamental question arises when attempting to map connectivities in MCI: how to define and localize the best possible network nodes, or Regions of Interests (ROIs), for brain connectivity mapping, and how to perform accurate comparisons of those connectivities across different brains and populations? These still remain as open and urgent problems. Approaches: Our recently developed novel data-driven approach has discovered a map of Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) in healthy brains. These landmarks possess intrinsically-established correspondences across brains, while their locations were defined in each individual's local image space. In this project, we propose to create a universal and individualized ROI reference system for MCI specifically, by predicting and optimizing the DICCCOL map in well-characterized MCI subjects to be recruited from Duke Medical Center. The resulted DICCCOL map in MCI, named DICCCOL-M, will be annotated into functional networks by concurrent task-based fMRI, R-fMRI, DTI and MRI data. We propose to predict DICCCOL-M in ADNI-2 subjects based on DTI/MRI data and assess the hypothesized large-scale connectivity alterations in ADNI-2 subjects and their longitudinal changes for the purpose of MCI conversion prediction. Significance: 1) The created DICCCOL-M map can be considered and used as a next-generation brain atlas, which will have much finer granularity and better functional homogeneity than the Brodmann brain atlas that has been used in the brain science field for over 100 years. 2) The algorithms will be developed and released based on the open source platform of Insight Toolkit (ITK). The dissemination of the algorithms and associated datasets to the community will significantly contribute to numerous applications in brain imaging that rely on accurate localization of ROIs. 3) Despite recent DTI and R-fMRI studies in the literature to assess brain connectivities in MCI/AD, connectivity alterations in large-scale networks, e.g., over 358 DICCCOL ROIs, and their relationships to AD progression are largely unknown. This knowledge gap will be significantly bridged in this project by assessing these large-scale networks represented by DICCCOL-M in Duke and ADNI-2 subjects.

描述（由申请人提供）：在文献中，通过扩散张量成像（DTI）和/或静息状态fMRI（R-fMRI）测量MCI中假设的广泛结构和功能连接性改变的工作量很大。例如，正在进行的ADNI-2项目已经发布了数十个早期MCI患者的DTI和R-fMRI数据集。然而，当试图映射MCI中的连接时，出现了一个基本问题：如何定义和定位最佳网络节点或感兴趣区域（ROI），用于大脑连接映射，以及如何在不同的大脑和人群中执行这些连接的准确比较？这些仍然是公开和紧迫的问题。方法：我们最近开发的新型数据驱动方法在健康大脑中发现了密集的个性化和基于共同连接的皮层标志（DICCCOL）的地图。这些地标具有内在建立的对应关系，而它们的位置是在每个人的局部图像空间中定义的。在这个项目中，我们建议创建一个通用的和个性化的ROI参考系统MCI具体，通过预测和优化的DICCCOL地图在良好的特征MCI主题从杜克医学中心招募。将得到的MCI的DICCCOL图命名为DICCCOL-M，并将其通过并行的基于任务的fMRI、R-fMRI、DTI和MRI数据注释到功能网络中。我们建议根据DTI/MRI数据预测ADNI-2受试者的DICCCOL-M，并评估ADNI-2受试者的假设大规模连接改变及其纵向变化，以预测MCI转换。重要性：1）创建的DICCCOL-M图谱可以被认为是下一代脑图谱，它将比脑科学领域使用了100多年的Brodmann脑图谱具有更细的粒度和更好的功能同质性。2)这些算法将基于Insight Toolkit（ITK）的开源平台开发和发布。该算法和相关数据集的传播到社区将显着有助于脑成像中的许多应用程序，依赖于准确定位的ROI。3)尽管最近文献中的DTI和R-fMRI研究评估了MCI/AD的大脑连接性，但大规模网络中的连接性改变，例如，超过358个DICCCOL ROI，它们与AD进展的关系在很大程度上是未知的。本项目将通过评估以杜克和ADNI-2科目中的DICCCOL-M为代表的这些大型网络来显著弥合这一知识差距。