Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment

描述(申请人提供)：通过扩散张量成像(DTI)和/或静息状态功能磁共振成像(R-fMRI)来测量假设的MCI中广泛的结构和功能连接性改变，文献中已经做出了大量的努力。例如，正在进行的ADNI-2项目已经发布了数十个适用于早期MCI患者的DTI和R-fMRI数据集。然而，当试图在MCI中绘制连接性图时，一个基本的问题出现了：如何定义和定位大脑连接性图的最佳可能网络节点或感兴趣区域(ROI)，以及如何对不同大脑和人群的这些连接性进行准确比较？这些仍然是公开和紧迫的问题。方法：我们最近开发的新的数据驱动方法在健康大脑中发现了密集的个性化和基于公共连接的皮质地标(DICCCOL)的地图。这些地标在大脑中具有内在的对应关系，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，我们建议通过预测和优化从杜克医学中心招募的具有良好特征的MCI受试者的DICCCOL图，为MCI创建一个通用的和个性化的ROI参考系统。在MCI中得到的DICCCOL MAP，命名为DICCCOL-M，将通过基于并发任务的fMRI、R-fMRI、DTI和MRI数据被标注到功能网络中。我们建议根据DTI/MRI数据预测ADNI-2受试者的DICCCOL-M，并评估假想的ADNI-2受试者的大规模连接改变及其纵向变化，以达到MCI转换预测的目的。意义：1)创建的DICCCOL-M图谱可以考虑作为下一代脑图谱使用，它将比在脑科学领域使用了100多年的Brodmann脑图谱具有更细微的粒度和更好的功能同质性。2)算法将基于Insight工具包(ITK)开源平台进行开发和发布。算法和相关数据集的传播将极大地促进依赖于ROI准确定位的脑成像中的众多应用。3)尽管最近的DTI和R-fMRI研究评估了MCI/AD的脑连通性，但大规模网络中的连通性改变，例如超过358个DICCCOL ROI，以及它们与AD进展的关系在很大程度上是未知的。在这个项目中，通过评估杜克大学和ADNI-2受试者中以DICCCOL-M为代表的这些大规模网络，这一知识差距将显著弥合。

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.