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

自闭症谱系障碍（ASD）是一种复杂的神经发育障碍，在美国每54名儿童中就有一名受到影响。ASD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，更不用说对当前医疗保健和教育系统的负担了。尽管经过数十年的研究，但与ASD相关的许多基本问题仍然存在，从早期诊断到个性化干预。ASD的异质性显著地导致了识别与这种疾病相关的特定性状的困难（即，表型分型），遗传上或行为上。除了明显增加的患病率和未知的病因外，ASD表型的建模一直是自闭症研究中的一个长期悬而未决的问题。对ASD表型的进一步了解可以为ASD的更准确诊断和更有效的干预提供新的见解。该项目旨在了解基于行为测量和传感器收集的数据的ASD生物标志物，包括神经记录，眼动跟踪，视频/音频捕获和其他传感器数据。通过多学科合作，该项目将导致ASD表型的行为科学和数据驱动的计算神经科学的变革性进展。改善和早期诊断可以大大提高ASD患者及其社区的生活质量。本项目将为培养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良好的平台。本项目将通过采用多模态数据驱动方法，将行为成像数据（眼动跟踪，音频/视频）与功能磁共振成像（fMRI），脑电图（EEG）/脑磁图（MEG）等神经成像数据相结合，解决ASD建模问题。研究团队将进行多模态数据融合，以提取ASD相关的生物标志物，而无需特征工程，并进行数据驱动建模，以了解ASD的神经基础，特别是行为和传感器导向信号之间的关系。这种基于多模态数据的建模将联合收割机组合关于显著ASD生物标志物的互补信息，例如跨不同模态的动态功能连接。为了避免ASD表型的基于几何学的特征工程，研究人员将使用两种基于流的深度学习技术沿着XAI可解释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，多模式神经影像学将导致ASD的丰富性和复杂性的调查，这里称为ASD表型。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.