Assessing Large-scale Connectivities in Prenatal Cocaine Exposure Affected Brains

描述（由申请人提供）：本申请是对PA-10-067（研究项目资助（父R 01））的回应。问题陈述：最近的神经影像学研究在文献中，包括我们自己的，已经表明，几个大脑网络，包括唤醒调节，工作记忆，语言，执行功能，注意力和视觉系统的改变，在产前可卡因暴露（PCE）影响的大脑。然而，在大规模的大脑网络的结构和功能的连接性的改变和结构的大脑结构的改变在PCE影响青少年在很大程度上是未知的。PCE青少年和对照组中大规模脑连接定量评估的主要障碍是严重缺乏在不同大脑中一致的密集脑地标，并且可以作为连接映射的共同网络节点。方法：最近，我们创建并验证了一种变革性的数据驱动方法，该方法在健康的年轻成人大脑中发现了358个一致的大脑地标的密集地图，称为密集个性化和基于共同连接的皮质地标（DICCCOL），每个地标都由来自扩散张量成像（DTI）数据的一致白色纤维连接模式定义。我们的验证结果表明，这358个DTI衍生的DICCCOL地标在单独的数据集中具有惊人的可重复性，具有有意义和准确的功能定位，并具有自动建立的跨学科对应关系。重要的是，这358个大脑标志可以在一个新的，单一的大脑与DTI数据准确，有效地预测。因此，这组358个密集的地标为大规模的结构和功能连接性评估提供了共同的网络节点。在这个项目中，我们建议将我们的DICCCOL模型及其预测框架应用于现有的埃默里PCE/对照数据集，并评估PCE影响的青少年中的大规模连通性。重要性：1）发现的共同DICCCOL图谱，加上一致的结构连接模式，可以考虑作为下一代脑图谱，它将比已经使用了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)基于Insight Toolkit（ITK）开源平台的DICCCOL预测框架和DICCCOL图的传播将有助于依赖于脑ROI精确定位的脑成像中的许多应用。3)PCE中DICCCOL地标的大规模脑网络的连接性改变在很大程度上是未知的。在这个项目中，通过评估PCE/对照大脑的多模态DTI和静息状态fMRI数据集中的大规模网络，这一知识差距将被显著弥合。 公共卫生相关性：简而言之，这个项目的目的是应用UGA密集的个性化和共同的连接为基础的皮质标志（DICCCOL）模型和地标预测框架对现有的埃默里产前可卡因暴露（PCE）/控制数据集和评估大规模的连接在PCE青少年。本项目旨在预测PCE/对照中的DICCCOL图谱，发现三个人群中共同的DICCCOL图谱，并评估PCE/对照中的大规模结构连接，以及评估PCE/对照中的大规模功能连接。其结果将是新的阐明PCE的致畸作用广泛的连接性改变和收集的连接性为基础的标记物，是预测PCE的临床和行为测量。

DESCRIPTION (provided by applicant): This application is in response to the PA-10-067 (Research Project Grant (Parent R01)). Problem statement: Recent neuroimaging studies in the literature, including our own ones, have shown that several brain networks, including arousal regulation, working memory, language, executive function, attention, and vision systems are altered in prenatal cocaine exposure (PCE) affected brains. However, the alterations of structural and functional connectivities in large-scale brain networks and the alterations of structural brain architecture in PCE affected adolescents are largely unknown. The major barrier to the quantitative assessments of large-scale brain connectivities in PCE adolescents and controls is the critical lack of dense brain landmarks that are consistent across different brains and can serve as common network nodes for connectivity mapping. Approaches: Recently, we created and validated a transformative data-driven approach that discovered a dense map of 358 consistent brain landmarks, called Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL), in healthy young adult brains, each of which is defined by consistent white matter fiber connectivity pattern derived from diffusion tensor imaging (DTI) data. Our validation results have shown that these 358 DTI-derived DICCCOL landmarks are strikingly reproducible in separate datasets, have meaningful and accurate functional localizations, and possess automatically-established cross-subjects correspondences. Importantly, these 358 brain landmarks can be accurately and efficiently predicted in a new, single brain with DTI data. Therefore, this set of 358 dense landmarks offers common network nodes for large-scale structural and functional connectivities assessments. In this project, we propose to apply our DICCCOL models and their prediction framework on existing Emory PCE/control datasets and assess large-scale connectivities in PCE affected adolescents. Significance: 1) The discovered common DICCCOL map, together with the consistent structural connection patterns, can be considered and used as the next-generation brain atlas, which will have much finer granularity and better functional homogeneity than the Brodmann brain atlas that has been used for over 100 years. 2) The dissemination of the DICCCOL prediction framework based on the open source platform of Insight Toolkit (ITK) and the DICCCOL map will contribute to numerous applications in brain imaging that rely on accurate localization of brain ROIs. 3) Connectivity alterations in large-scale brain networks of DICCCOL landmarks in PCE are largely unknown. This knowledge gap will be significantly bridged in this project by assessing large-scale networks in multimodal DTI and resting state fMRI datasets of PCE/control brains. PUBLIC HEALTH RELEVANCE: Briefly, this project aims to apply UGA Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) models and landmark prediction framework on existing Emory prenatal cocaine exposure (PCE)/control datasets and assess large-scale connectivities in PCE adolescents. This project aims to predict DICCCOL maps in PCE/controls, discover common DICCCOL maps in three populations and assess large-scale structural connectivities in PCE/controls, and assess large-scale functional connectivities in PCE/controls. The outcome will be novel elucidations of PCE's teratogenic effects on widespread connectivity alterations and a collection of connectivity-based markers that are predictive of PCE clinical and behavior measurements.