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) 影响着美国多达 1% 的儿童，导致严重的 大多数受影响者将终身残疾。之前的神经影像学研究仅限于分组。 ASD 和对照组之间的分析，无法区分或区分皮质异常和 特定 ASD 受试者的变异性源于缺乏新的大脑结构。 能够有效表征每个个体的人脑结构并提取大脑的描述符 同时，先前的研究表明，机械结构具有共性。 因素在大脑结构的形成中发挥着重要作用，包括在自闭症谱系障碍中观察到的异常。 当前的大脑机械模型建立在专注于一种特定机械的模型之上 努力，但未能明确捕捉大脑模型的物理复杂性和多个相互作用 这种知识的缺乏是发展公正性的一个关键障碍。 了解个体大脑结构共性的模型，以及能够 查明个体 ASD 大脑的异常情况 这项研究的总体目标是构建一个 为每个大脑构建变革性大脑结构网络（BSN），理清 BSN 的共性并 个体健康大脑的变异性，发现机制对 BSN 共性的作用，以及 通过成像分析和数据驱动的计算模拟来确定个体之间的变异性，并精确定位 皮质异常，并通过比较 ASD 和 ASD 之间的 BSN 来评估其对 ASD 大脑的相关影响 我们的中心假设是大脑结构网络及其潜在的机械作用。 原则可以通过数据驱动的发现来解释，这些发现包括保存的、描述性的、普遍的和 跨个体的明显大脑结构描述符将通过以下方式实现拟议工作的目标。 (1) 我们将重建各个皮质表面来识别 并评估 3 铰链回旋连接 (3HG) 和基于 3HG 的大脑结构网络，从而检查 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； (3) 我们将通过进行自闭症谱系障碍患者大脑结构网络的异常评估 与健康大脑进行比较分析，找出导致这种异常的机械因素 个人。