Temporal connectomics for infant brain: neurodevelopment modulated by pathology

摘要 自闭症谱系障碍（ASD）是一种高度遗传的神经发育障碍，影响超过1%的 儿童（和近2%的男孩），与那些出生在高风险（HR）家庭的孩子被诊断为ASD 比普通人群的风险高20倍。虽然只有在检查后才能可靠地诊断出来， 在第二年的生活中，ASD起源于神经发育机制，早期出现症状， 6个月大的时候。前瞻性大规模纵向神经成像研究，如婴儿脑成像 研究（IBIS），那些出生在一个高风险（HR）家庭，是非常有价值的，因为它们有助于发现 ASD的早期表现本提案的总体目标是全面比较 使用IBIS弥散MRI（dMRI）对高风险和低风险人群的脑组织进行纵向分析 在出生后2年内的3个时间点收集数据，以阐明ASD的最早表现， 无序轨迹此外，我们的目标是从大脑连接特征中创建ASD风险的生物标志物， 他们的发展轨迹，并了解家庭风险的大脑基础（独立于ASD）。dMRI 提供了一个深入了解大脑的结构组织表示为连接体。“误接线” 结构连接体可以表现为WM神经元不成熟和网络结构的改变，即， 子网（与不同通信模式相关联的强互连区域的集合）， 以及通信骨干网（子网之间通信的总体架构）。两 与对照组相比，ASD的连接性和WM质量可能会受到影响，这表明“接线错误” 连接体研究ASD相关的连接性早期发育变化需要设计新的 基于“纵向”连接组学特征的分析方法，这些特征是4D特征， 时间演变超过发展，最终在创造新的成像为基础的生物标志物。在Aim中 1，我们将创建一种新的方法提取纵向纤维束，以识别和分析差异 LR-、HR-和HR+受试者的发育轨迹之间的关系， 来自纤维束的特征，以识别神经不成熟的差异。在目标2中，我们将设计 提取纵向网络结构如结构内聚和功能限定的新方法 子网络和全球通信骨干，并调查发展差异调制 家庭风险和性别。最后，在目标3中，我们将使用从目标1中提取的轨迹和网络特征 和2开发基于成像的标记物，这将有助于早期ASD预测， 可以从早期治疗干预中获益，并以大脑的形式提供发育指数。 连接“成熟年龄”，以帮助表征发育迟缓和大脑模式对应 一样的该提案中开发的纵向分析方法将推广到其他临床 可以从跨越几个时间点的纵向分析中获益的人群。

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.