# SCH: Using Data-Driven Computational Biomechanics to Disentangle Brain Structural Commonality, Variability, and Abnormality in ASD > **NIH NIH R01** · UNIVERSITY OF GEORGIA · 2024 · $280,725 ## Abstract 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. ## Key facts - **NIH application ID:** 10916511 - **Project number:** 5R01NS135574-02 - **Recipient organization:** UNIVERSITY OF GEORGIA - **Principal Investigator:** Xianqiao Wang - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $280,725 - **Award type:** 5 - **Project period:** 2023-09-01 → 2027-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10916511 ## Citation > US National Institutes of Health, RePORTER application 10916511, SCH: Using Data-Driven Computational Biomechanics to Disentangle Brain Structural Commonality, Variability, and Abnormality in ASD (5R01NS135574-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10916511. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*