Assessing Large-scale Connectivities in Prenatal Cocaine Exposure Affected Brains

评估产前可卡因暴露影响大脑的大规模连接

• 批准号: 8273327
• 负责人: XIAOPING P HU
• 金额: $ 33.24万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8273327
• 关键词: Adolescent; Affect; Architecture; Area; Arousal; Atlases; Attention; Base of the Brain; Behavior; Benchmarking; Brain; Brain imaging; Brain region; Clinical; Collection; Data; Data Set; Diffusion Magnetic Resonance Imaging; Fetal Cocaine Exposure; Fiber; Fingerprint; Foundations; Functional Magnetic Resonance Imaging; Individual; Intelligence; Knowledge; Label; Language; Literature; Maps; Modeling; Neurosciences; Outcome; Output; Parents; Pattern; Population; Regulation; Reproducibility; Research Project Grants; Rest; Shapes; Short-Term Memory; System; Validation; Vision; Work; base; behavior measurement; cocaine exposure; executive function; insight; mind control; neurodevelopment; neuroimaging; next generation; novel; open source; prenatal exposure; relating to nervous system; response; skills; white matter; young adult

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.

描述（由申请人提供）：此申请响应PA-10-067（研究项目赠款（父r01））。问题陈述：文献中的最新神经影像学，包括我们自己的神经影像研究表明，包括唤醒调节，工作记忆，语言，执行功能，注意力，注意力和视力系统在内的几个大脑网络正在改变产前可卡因暴露（PCE）受影响的大脑。然而，大规模脑网络中结构和功能连接的改变以及PCE影响的青少年中结构性大脑结构的改变是未知的。 PCE青少年和控制措施中大规模脑连接性的定量评估的主要障碍是严重缺乏密集的脑部标记，这些脑标志是一致的不同大脑，并且可以用作通用网络节点进行连接映射。方法：最近，我们创建并验证了一种变革性数据驱动的方法，该方法在健康的年轻成人大脑中发现了358个一致的脑部标记，称为密集的个性化和共同的基于连接性的皮质地标（DICCCOL），在健康的年轻成人大脑中，每种都由一致的白纤维连通性模式定义为从扩散的tensor Tensor Tensor Tensor Imaging（DTI）得出的（DTI）。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重现，具有有意义且准确的功能定位，并且具有自动建立的交叉主题对应关系。重要的是，在具有DTI数据的新的单个大脑中，可以准确，有效地预测这358个脑部标记。因此，这组358个密集地标提供了用于大规模结构和功能连接性评估的通用网络节点。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Emory PCE/Control数据集，并评估PCE受影响青少年中的大规模连接性。意义：1）发现的常见DICCCOL图以及一致的结构连接模式可以被视为和用作下一代大脑图集，与已使用已有1​​00多年的Brodmann Brain相比，它的粒度和功能性均质比Brodmann Brain具有更好的功能同质性。 2）基于Insight Toolkit（ITK）和DICCCOL地图的开源平台DICCCOL预测框架的传播将有助于大脑成像中的许多应用，这些应用依赖于精确定位脑ROI。 3）在PCE中DICCCOL地标的大型大脑网络中的连通性改变在很大程度上是未知的。通过评估PCE/控制大脑的多模式DTI和静止状态fMRI数据集中的大规模网络，该项目将在该项目中显着桥接该知识差距。 公共卫生相关性：简而言之，该项目旨在应用UGA密集的个性化和共同的基于连通性的皮质地标（DICCCOL）模型和地标预测框架，以对现有的emory产前可卡因暴露（PCE）/控制数据集进行，并评估PCE青少年的大规模连接性。该项目旨在预测PCE/控件中的DICCCOL图，在三个人群中发现常见的DICCCOL图，并评估PCE/对照中的大规模结构连接性，并评估PCE/对照中的大规模功能连接性。结果将是PCE对广泛的连通性改变的致力作用以及可预测PCE临床和行为测量值的基于连接性的标记的新颖阐明。