Temporal connectomics for infant brain: neurodevelopment modulated by pathology

摘要 自闭症谱系障碍(ASD)是一种高度遗传性的神经发育障碍，影响超过1%的 儿童(和近2%的男孩)，那些出生在高危(HR)家庭的孩子被诊断出患有自闭症 具有比普通人群高20倍的风险。尽管它只能在 在生命的第二年，ASD起源于神经发育机制，症状很早就出现了 六个月大的时候。前瞻性的大规模纵向神经成像研究，如婴儿脑成像 对出生在高危(HR)家庭的人进行的研究(IBIS)非常有价值，因为它们有助于发现 房间隔缺损最早的表现。这项建议的首要目标是全面比较 IBIS扩散磁共振成像(DMRI)对高危人群和低危人群脑组织的纵向分析 在生命最初2年的3个时间点收集的数据，以阐明ASD和 无序轨迹。此外，我们的目标是从大脑连通性特征和 他们的发育轨迹，并了解家族性风险的大脑基础(独立于自闭症)。DMRI 提供对大脑的结构组织的洞察，表示为连接体。的“接线错误” 结构连接体可表现为WM的神经不成熟和网络结构的变化，即， 子网络(与不同通信模式相关联的强互连区域的集合)， 以及通信主干(子网络之间通信的整体架构)。两者都有 与控制相比，ASD中的连接性和WM质量可能会受到影响，这表明存在“连接错误” 连接体。研究与ASD相关的连接性早期发育变化需要设计新颖的 基于“纵向”连通特征的分析方法，这些连通特征是包含其 在发展过程中的时间演变，最终创造了基于成像的新型生物标记物。在AIM 1，我们将创建一种新的提取纵向纤维束的方法来识别和分析差异 用几何和质量研究LR-、HR-和HR+受试者的发育轨迹 从纤维束派生的特征，以识别神经不成熟的差异。在目标2中，我们将设计 提取结构内聚和功能定义的纵向网络结构的新方法 子网络和全球通信主干，并调查调制的发育差异 按家庭风险和性别分类。最后，在目标3中，我们将使用从目标1提取的区域和网络要素 和2开发基于成像的标记，将有助于ASD的早期预测，识别 可以从早期的治疗干预中获益，并以大脑的形式提供发育指数 连通性“成熟年龄”有助于表征发育迟缓和对应的大脑模式 一样的。该方案开发的纵向分析方法将推广到其他临床 可以从跨越多个时间点的纵向分析中获益的人口。

Abstract Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder affecting more than 1% of children (and almost 2% of boys), with those born in a high risk (HR) family with a child diagnosed with ASD having a ~20 fold greater risk than the general population. Although it can only be reliably diagnosed after the second year of life, ASD originates from a neurodevelopmental mechanism, with symptoms emerging as early as 6 months of age. Prospective large-scale longitudinal neuroimaging studies like the Infant Brain Imaging Study (IBIS), of those born into a high-risk (HR) family, are extremely valuable as they facilitate discovery of the earliest manifestations of ASD. The overarching goal of this proposal is a comprehensive comparative longitudinal analysis of brain organization of high- and low-risk populations, by using IBIS diffusion MRI (dMRI) data collected at 3 time points in the first 2 years of life, to elucidate the earliest manifestations of ASD and disorder trajectory. In addition, we aim to create a biomarker of ASD risk from brain connectivity features and their developmental trajectories, and to understand the brain bases of familial risk (independent of ASD). dMRI provides an insight into the structural organization of the brain represented as a connectome. “Miswiring” of the structural connectome can manifest as neuro-immaturity of WM and changes in network structures, that is, subnetworks (collection of strongly inter-connected regions associated with a distinct communication pattern), and the communication backbone (overall architecture of communication between subnetworks). Both connectivity and WM quality can be compromised in ASD compared to controls, suggesting a “miswired” connectome. Studying ASD related early developmental changes in connectivity requires design of novel analysis methods based on “longitudinal” connectomic features that are 4D features that incorporate their temporal evolution over development, culminating in the creation of novel imaging-based biomarkers. In Aim 1, we will create a new method of extracting longitudinal fiber tracts to identify and analyze differences between the developmental tract trajectories in LR-, HR- and HR+ subjects using geometry and quality features derived from the fiber bundles, to identify differences in neuro-immaturity. In aim 2, we will design novel methods to extract longitudinal network structures like structurally-cohesive and functionally-defined subnetworks and the global communication backbone, and investigate developmental differences modulated by familial risk and gender. Finally, in Aim 3 we will use the tract and network features extracted from Aims 1 and 2 to develop imaging based markers that will assist with early ASD prediction, identifying siblings who could gain from an early therapeutic intervention and provide an index of development in the form of brain connectivity “maturational age” to help characterize developmental delays and brain patterns corresponding to the same. The longitudinal analysis methods developed in the proposal will be generalizable to other clinical populations that can gain from longitudinal analysis spanning several time points.