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: 8826091
• 负责人: XIAOPING P HU
• 金额: $ 31.55万
• 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 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8826091
• 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; predictive marker; 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.

描述（由申请人提供）：本申请是对 PA-10-067（研究项目拨款（父 R01））的回应。问题陈述：最近文献中的神经影像学研究（包括我们自己的研究）表明，在产前可卡因暴露（PCE）影响的大脑中，多个大脑网络，包括觉醒调节、工作记忆、语言、执行功能、注意力和视觉系统发生了改变。然而，大规模大脑网络结构和功能连接的改变以及受 PCE 影响的青少年大脑结构的改变在很大程度上是未知的。对 PCE 青少年和对照组大规模大脑连接进行定量评估的主要障碍是严重缺乏在不同大脑之间一致的密集大脑标志，并且可以作为连接映射的公共网络节点。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人大脑中发现了 358 个一致大脑标志的密集图，称为密集个体化和基于共同连接的皮质标志 (DICCCOL)，每个标志都是由源自扩散张量成像 (DTI) 数据的一致白质纤维连接模式定义的。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，具有有意义且准确的功能定位，并且具有自动建立的跨主题对应关系。重要的是，可以利用 DTI 数据在新的单一大脑中准确、高效地预测这 358 个大脑标志。因此，这组 358 个密集地标为大规模结构和功能连通性评估提供了公共网络节点。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大学 PCE/对照数据集，并评估受 PCE 影响的青少年的大规模连通性。意义：1）所发现的通用DICCCOL图谱，加上一致的结构连接模式，可以被认为并用作下一代脑图谱，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）开源平台和 DICCCOL 图谱的 DICCCOL 预测框架的传播，将有助于依赖大脑 ROI 准确定位的脑成像领域的众多应用。 3）PCE中DICCCOL标志物的大规模脑网络的连接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 和静息状态 fMRI 数据集中的大规模网络，该项目将显着弥补这一知识差距。