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

SCH：利用数据驱动的计算生物力学来解开 ASD 中脑结构的共性、变异性和异常性

• 批准号: 10814620
• 负责人: Xianqiao Wang
• 金额: $ 29.35万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10814620
• 关键词: Affect; Algorithmic Software; Architecture; Axon; Biomechanics; Brain; Brain Diseases; Brain imaging; Cephalic; Child; Childhood; Clinical; Computer Models; Computer Simulation; Coupled; Data; Descriptor; Development; Diagnosis; Electronic Medical Records and Genomics Network; Fiber; Goals; Growth; Heterogeneity; Human; Image; Individual; Intervention; Knowledge; Longevity; Machine Learning; Magnetic Resonance Imaging; Mechanics; Microscopic; Modeling; Neurodevelopmental Disorder; Outcome Study; Pattern; Play; Process; Property; Public Health; Reporting; Reproducibility; Research; Role; Scientific Advances and Accomplishments; Structural defect; Structure; Surface; Testing; United States; Work; autism spectrum disorder; brain abnormalities; brain based; brain health; brain magnetic resonance imaging; computerized tools; data modeling; density; disability; fetal; gray matter; infancy; innovation; mechanical properties; model building; multi-scale modeling; neuroimaging; novel; personalized predictions; preservation; simulation; white matter

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.

自闭症谱系障碍（ASD）影响了美国高达1%的儿童，导致了显着的 对大多数受影响的人来说是终身残疾。以前的神经影像学研究仅限于分组 ASD和对照组之间的分析，不能区分或区分皮质异常与 特定ASD受试者的变异性。这些困难源于缺乏新颖的大脑结构 描述符，可以有效地表示每个个体的人脑架构，并提取大脑 个体之间的结构共性。与此同时，先前的研究表明， 这些因素在大脑结构的形成中起着重要作用，包括在ASD中观察到的异常。 目前的脑力学模型建立在简化模型的基础上，重点是一个特定的力学模型。 努力，但未能明确捕捉大脑模型的物理复杂性和多种因素的相互作用。 机械性能同时这种知识的缺乏是发展公正的一个关键障碍 模型来了解个体之间的大脑结构共性，以及模型， 找出自闭症患者大脑的异常本研究的总体目标是构建一个 每个大脑的变革性大脑结构网络（BSN），解开BSN的共性， 个体健康大脑的差异，发现力学对BSN共性的作用， 通过成像分析和数据驱动的计算模拟， 皮质异常，并通过比较ASD和 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发现来解释，这些发现是保存的，描述性的，普遍的， 明显的大脑结构描述符。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3-铰链脑回连接（3 HG）和基于3 HG的脑结构网络， 个体大脑的大脑结构共性;（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，并收集数据进行机器学习分析;（3）我们将通过进行评估ASD患者大脑结构网络的异常， 与健康大脑的比较分析，并查明导致这种异常的机械因素， 个体