HCC: Small: Toward Computational Modeling of Autism Spectrum Disorder: Multimodal Data Collection, Fusion, and Phenotyping

HCC：小型：自闭症谱系障碍的计算模型：多模式数据收集、融合和表型分析

• 批准号: 2401748
• 负责人: Xin Li
• 金额: $ 50万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2401748&HistoricalAwards=false
• 关键词: HCC; Small; Toward; Computational; Modeling

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder affecting one out of 54 children in the US. ASD is arguably one of the greatest public health challenges of our time, which has imposed a significant impact on children and their families, not to mention the burden on the current healthcare and educational systems. Despite decades of research, many fundamental issues related to ASD remain from early diagnosis to personalized intervention. The heterogeneity of ASD has contributed significantly to the difficulty in identifying the specific traits associated with this disorder (i.e., phenotyping), genetically or behaviorally. In addition to apparently increasing prevalence and unknown etiology, modeling the ASD phenotype has remained a long-standing open problem in autism research. An improved understanding of ASD phenotypes can shed novel insight to both more accurate diagnosis and more effective intervention of ASD. This project aims to understand ASD biomarkers based on behavioral measurement and sensor-gathered data, including neural recording, eye tracking, video/audio capture, and other sensor data. Through multi-disciplinary collaboration, this project will lead to transformative advances in behavioral science and data-driven computational neuroscience for ASD phenotyping. Improved and earlier diagnosis can substantially improve quality of life of ASD individuals and their communities. This project will provide an excellent platform to train both graduate and undergraduate students at the intersection of neuroscience and computer science.This project will address the problem of ASD modeling by taking a multimodal data-driven approach integrating behavior imaging data (eye-tracking, audio/video) with neuroimaging data such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG)/ magnetoencephalography (MEG). The research team will carry out multimodal data fusion to extract ASD-relevant biomarkers without feature engineering, and data-driven modeling to obtain an understanding of the neural underpinnings of ASD, especially in the relationship between behavioral and sensor-oriented signals. This multimodal data-based modeling will combine complementary information about salient ASD biomarkers, such as dynamic functional connectivity, across different modalities. To avoid heuristics-based feature engineering for ASD phenotyping, the researchers will use two stream-based deep learning techniques along with XAI explainable AI (Artificial Intelligence). XAI will provide the interpretations for the decisions made by the deep learning algorithms to identify the traits associated with ASD. In addition to ASD diagnosing, multimodal neuroimaging will lead to investigations into the richness and complexity of ASD, referred to here as ASD phenotyping.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自闭症谱系障碍 (ASD) 是一种复杂的神经发育障碍，美国每 54 名儿童中就有一人患有这种疾病。自闭症谱系障碍可以说是我们这个时代最大的公共卫生挑战之一，它对儿童及其家庭产生了重大影响，更不用说给当前的医疗保健和教育系统带来负担了。尽管经过数十年的研究，从早期诊断到个性化干预，与 ASD 相关的许多基本问题仍然存在。自闭症谱系障碍的异质性极大地增加了从遗传或行为角度识别与该疾病相关的特定特征（即表型）的难度。除了明显增加的患病率和未知的病因之外，对自闭症谱系障碍表型进行建模仍然是自闭症研究中长期存在的一个悬而未决的问题。加深对 ASD 表型的了解可以为更准确的 ASD 诊断和更有效的干预提供新的见解。该项目旨在基于行为测量和传感器收集的数据（包括神经记录、眼球追踪、视频/音频捕获和其他传感器数据）了解 ASD 生物标志物。通过多学科合作，该项目将推动行为科学和数据驱动的计算神经科学在 ASD 表型分析方面取得变革性进展。改进和早期诊断可以大大提高自闭症谱系障碍个人及其社区的生活质量。该项目将为培训神经科学和计算机科学交叉领域的研究生和本科生提供一个优秀的平台。该项目将通过采用集成行为成像数据（眼动追踪、音频数据）的多模态数据驱动方法来解决 ASD 建模问题。 /视频）以及神经影像数据，例如功能性磁共振成像（fMRI）、脑电图（EEG）/脑磁图（MEG）。研究团队将进行多模态数据融合，以在不进行特征工程的情况下提取 ASD 相关生物标志物，并进行数据驱动建模，以了解 ASD 的神经基础，特别是行为和面向传感器的信号之间的关系。这种基于多模式数据的建模将结合有关显着 ASD 生物标志物的补充信息，例如跨不同模式的动态功能连接。为了避免 ASD 表型分析中基于启发式的特征工程，研究人员将使用两种基于流的深度学习技术以及 XAI 可解释的 AI（人工智能）。 XAI 将为深度学习算法做出的决策提供解释，以识别与 ASD 相关的特征。除了 ASD 诊断之外，多模式神经影像学还将促进对 ASD 丰富性和复杂性的研究，此处称为 ASD 表型分析。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。