SCH: Using Data-Driven Computational Biomechanics to Disentangle Brain Structural Commonality, Variability, and Abnormality in ASD

自闭症谱系障碍(ASD)在美国影响多达1%的儿童，导致显著的 大多数受影响的人终身残疾。以前的神经成像研究仅限于GroupWise ASD和对照组之间的分析，无法区分或分离皮质异常与 特定ASD受试者的可变性。这些困难源于缺乏新的大脑结构 能够有效地表示每个个体的人脑结构并提取大脑的描述符 个人之间的结构性共性。与此同时，先前的研究已经证明，机械 各种因素在脑结构的形成中起着重要作用，包括在ASD中观察到的异常。 当前的大脑机械模型建立在简化模型的基础上，侧重于一种特定的机械模型 努力，但未能明确地捕捉到大脑模型的物理复杂性以及多个 机械因素同时存在。这种知识的缺乏是发展不偏不倚的关键障碍 了解个体大脑结构共性的模型，以及可以 精确定位个体ASD脑内的异常。这项研究的总体目标是构建一个 针对每个个体大脑的变革性脑结构网络(BSN)，解开BSN的共性和 个体健康大脑的可变性，发现机械在BSN的共性和 通过成像分析和数据驱动的计算模拟实现个体间的可变性，并精确定位 ASD与BSN的对比研究ASD脑皮质异常及其相关影响 健康的大脑。我们的中心假设是大脑结构网络及其潜在的机械 原则可以通过数据驱动的保存的、描述性的、通用的和 明显的个体大脑结构描述符。拟议工作的目标将通过以下方式实现 完成以下三个具体目标：(1)我们将重建单个皮质表面以识别 并评估三铰环连接(3HGs)和基于3HG的大脑结构网络，因此检查 跨个体大脑的脑结构共性；(2)我们将构建数据驱动的胎儿全脑 模型，在不同的力学条件下进行大量的模拟，并收集用于机器学习分析的数据；(3)我们将通过 与健康大脑和精确定位导致这种异常的机械因素的对比分析 个人。

Autism spectrum disorder (ASD) affects up to 1% of children in the United States, resulting in significant lifelong disability for the majority of those affected. Prior neuroimaging studies are limited to groupwise analysis between ASD and controls, which cannot differentiate or disentangle cortical abnormality from variability for a specific ASD subject. These difficulties originate from a lack of a novel brain structural descriptor that can effectively represent the human brain architectures of each individual and extract brain structural commonalities across individuals. Meanwhile, prior studies have demonstrated that mechanical factors play important roles in the formation of brain architecture, including abnormalities observed in ASD. Current brain mechanical models build upon simplified models with a focus on one specific mechanical effort, but fail to explicitly capture the physical complexity of brain models and the interplay of multiple mechanical factors simultaneously. This lack of knowledge is a crucial barrier to developing unbiased models to understand the brain structural commonalities across individuals, as well as models that can pinpoint the abnormalities in individual ASD brain. The overall objective of this research is to construct a transformative brain structural network (BSN) for each individual brain, disentangle BSN’s commonality and variability across individual health brains, discover the role of mechanics on the BSN’s commonality and variability across individuals via imaging analyses and data-driven computational simulations, and pinpoint cortical abnormality and evaluate their relevant impact in ASD brains by comparing BSN between ASD and healthy brains. Our central hypothesis is that the brain structural network and its underlying mechanical principles can be interpreted through a data-driven discovery of preserved, descriptive, universal, and evident brain structural descriptor across individuals. The goal of the proposed work will be achieved by completing the following three specific aims: (1) we will reconstruct individual cortical surfaces to identify and assess 3-hinge gyral junctions (3HGs) and 3HG-based brain structural network and therefore examine brain structure commonality across individual brains; (2) we will construct data-driven fetal whole brain models, perform massive simulations with varying mechanical conditions, and collect data for machine-learning analysis; (3) we will evaluate brain structural network’s abnormality in ASD by conducting comparison analysis with health brain and pinpoint mechanical factors that lead to this abnormality across individuals.