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）：通过扩散张量成像（DTI）和/或静止状态fMRI（R-FMRI）（R-FMRI）测量MCI中假设的广泛结构和功能连接改变的文献中已经大量精力。例如，正在进行的ADNI-2项目已经为早期的MCI患者发布了数十个DTI和R-FMRI数据集。但是，在尝试映射MCI中的连接性时会出现一个基本问题：如何定义和本地位置最佳的网络节点或利益区域（ROI），用于大脑连接映射，以及如何对不同大脑和人群之间这些连接的准确比较？这些仍然是开放和紧急的问题。方法：我们最近开发的新型数据驱动方法发现了健康大脑中基于密集的个性化和共同连通性的皮质标志（DICCCOL）图。这些地标具有跨大脑的本质上建立的对应关系，而它们的位置是在每个人的本地图像空间中定义的。在这个项目中，我们建议通过预测和优化从杜克医学中心招募的良好特征MCI受试者中的DICCCOL图来为MCI建立通用和个性化的ROI参考系统。 MCI中所得的DICCCOL映射（称为DICCCOL-M）将通过基于任务的FMRI，R-FMRI，DTI和MRI数据将其注释到功能网络中。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受试者中假设的大规模连通性改变及其纵向变化，以实现MCI转换预测的目的。意义：1）可以将创建的DICCCOL-M MAP视为下一代脑图集，与在大脑科学领域使用了超过100年中使用的Brodmann Brain Atlas相比，它的粒度和功能均质更好。 2）将根据Insight Toolkit（ITK）的开源平台开发和发布该算法。将算法和相关数据集传播到社区将极大地促进大脑成像中众多依赖ROI准确定位的应用。 3）尽管最近在文献中进行了DTI和R-FMRI研究，以评估MCI/AD中的大脑连接，但大规模网络中的连通性改变，例如超过358 diCccol ROI及其与AD进展的关系在很大程度上是未知的。通过评估DUKE和ADNI-2主题中DICCCOL-M代表的这些大规模网络，该项目将在该项目中显着弥合该知识差距。