Longitudinal Imaging Study of ADHD Brain Development

描述(由申请人提供)：ADHD是一个严重的长期后果严重的公共卫生问题。现有的治疗方法不能实现长期的改变，这增加了发现生物标记物、异质性的生物学基础和效应的发育特异性的紧迫性。这项拟议的项目将在纵向设计中测试ADHD患者大脑网络异常发育组织的集中理论。它还将寻求基于大脑网络识别ADHD的亚型。将使用两种最新一代的方法：扩散张量成像(DTI)和功能连接磁共振成像(FcMRI)。这些方法的使用直接符合NIH人类连接组指南，因此数据集也将作为最终创建儿童连接组的资源。在数据因磨损和运动伪影而丢失之前，总共有490名7-10岁的儿童将被登记。它们将分三次每年进行跟踪，产生基于年龄的加速纵向设计，跨度为7-12年。70%的样本将患有ADHD(所有亚型)，30%的样本将典型地发展为对照青年。强调以社区为基础的招募，以便获得一些治疗NANEV扫描。将跟踪临床变化、治疗使用和心理社会调节剂。选定的认知测量将用于交叉验证临床结果，并确定关于大脑回路的机械性假设是否适用于分析的认知水平。该项目的主要创新是：(A)将ADHD评估为动态实体而不是静态现象，(B)首次对ADHD的功能连接或DTI进行纵向研究，(C)结合ADHD的功能和结构连接措施，(D)引入图论的复杂应用，以便基于大脑网络确定ADHD的生物学亚型。其主要意义在于，它将使有关ADHD大脑网络的新发现能够在描述易感性的神经生物学标记(那些无论症状变化如何都会持续存在)和病程标记(那些对表型变化或预测结果或治疗反应非常敏感的标记)方面取得重大进展。这些数据可以为评估、个体化治疗和治疗监测的新模型提供信息。如果该项目成功，它将代表着在利用大脑发育的最新见解绘制ADHD机制的综合、范例模型方面向前迈出的重要一步。

DESCRIPTION (provided by applicant): ADHD is a major public health problem with severe long term outcomes. Existing treatments do not achieve long term change, which has increased the urgency of discovering biomarkers, biological bases of heterogeneity, and developmental specificity of effects. The proposed project would test focused theories of aberrant developmental organization of brain networks in ADHD in a longitudinal design. It would also seek to identify subtypes of ADHD based on brain networks. Two latest-generation methods will be utilized: Diffusion tensor imaging (DTI) and functional connectivity MRI (fcMRI). Use of these methods is directly in line with the NIH Human Connectome guidelines, so the data set will also serve as a resource in the eventual creation of a child connectome. A total of 490 children age 7-10 would be enrolled prior to data loss to attrition and motion artifact. They will be followed annually in three waves, yielding an age based accelerated longitudinal design spanning ages 7-12 years. 70% of the sample will have ADHD (all subtypes) and 30% will be typically developing comparison youth. Community- based recruitment is emphasized so that some treatment naove scans will be obtained. Clinical change, treatment use, and psychosocial moderators will be tracked. Selected cognitive measures will be used to cross validate clinical findings and determine whether mechanistic suppositions about brain circuits hold at the cognitive level of analysis. The major innovations in this project are: (a) evaluates ADHD as a dynamic entity rather than a static phenomenon, (b) the first longitudinal study of functional connectivity or DTI in ADHD, (c) combines functional and structural connectivity measures in ADHD, (d) introduces sophisticated usage of graph theory in order to determine biological subtypes of ADHD based on brain networks. The major significance is that it will enable new discoveries about brain networks in ADHD that can enable significant advances in describing neurobiological markers of liability (those that persist regardless of symptom change) and markers of course of illness (those that are acutely sensitive to phenotypic change or predict outcome or treatment response). Those data can inform new models of assessment, individualized treatments, and treatment monitoring. If the project is successful it will represent an important step forward in mapping integrative, paradigmatic models of ADHD mechanism using the newest insights about brain development.