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）在美国影响多达1％的儿童，导致重大 大多数受影响的人的终身残疾。先前的神经影像学研究仅限于群 ASD和对照之间的分析，这些分析无法区分或解开皮质异常与 特定ASD主题的可变性。这些困难源于缺乏新颖的大脑结构 可以有效地表示每个人的人脑体系结构并提取大脑的描述符 跨个体的结构共同点。同时，先前的研究表明机械 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以简化模型为基础，重点是一种特定的机械 努力，但无法明确捕获大脑模型的身体复杂性和多重相互作用 机械因素很简单。缺乏知识是发展公正的关键障碍 了解个人跨个体的大脑结构共同点的模型，以及可以 查明单个ASD大脑中的异常。这项研究的总体目的是构建 每个大脑的变革性大脑结构网络（BSN），解开BSN的共同点和 跨个体健康大脑的变异性，发现力学在BSN的共性中的作用 通过成像分析和数据驱动的计算模拟，个人之间的可变性，并查明 皮质异常并通过比较ASD和ASD之间的BSN来评估其在ASD大脑中的相关影响 健康的大脑。我们的中心假设是大脑结构网络及其基础机械 可以通过数据驱动的发现，描述性，通用和 跨个体的大脑结构描述符。拟议工作的目标将由 完成以下三个特定目标：（1）我们将重建单个皮质表面以识别 并评估3-铰链体操（3HGS）和基于3HG的大脑结构网络，因此检查 大脑之间的大脑结构通用性； （2）我们将构建数据驱动的胎儿全脑 模型，具有不同机械条件的大规模模拟，并收集数据以进行机器学习分析； （3）我们将通过进行ASD评估大脑结构网络异常 与健康大脑的比较分析，并确定导致这种异常的机械因素 个人。