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。自闭症可以说是我们这个时代最大的公共卫生挑战之一，它对儿童及其家庭造成了重大影响，更不用说对当前医疗保健和教育系统的负担了。尽管经过了几十年的研究，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的异质性在很大程度上造成了识别与这种疾病相关的特定特征(即表型)的难度，无论是在遗传上还是在行为上。除了明显增加的患病率和未知的病因外，ASD表型的建模在自闭症研究中一直是一个长期悬而未决的问题。对ASD表型的更好理解可以为ASD更准确的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生物标记物，包括神经记录、眼睛跟踪、视频/音频捕获和其他传感器数据。通过多学科合作，该项目将在行为科学和数据驱动的ASD表型计算神经科学方面取得革命性进展。改进和早期诊断可以显著提高ASD患者及其社区的生活质量。这个项目将提供一个很好的平台来培养神经科学和计算机科学交叉领域的研究生和本科生。这个项目将通过采用多模式数据驱动的方法来解决ASD建模的问题，该方法将行为成像数据(眼睛跟踪、音频/视频)与神经成像数据如功能磁共振成像(FMRI)、脑电(EEG)/脑磁图(MEG)相结合。研究团队将进行多模式数据融合，在没有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，特别是行为信号和面向传感器的信号之间的关系。这种基于数据的多模式建模将结合有关显著ASD生物标记物的补充信息，例如跨不同模式的动态功能连接。为了避免基于启发式特征工程的ASD表型，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AI(人工智能)。XAI将为深度学习算法做出的决定提供解释，以识别与ASD相关的特征。除了ASD诊断，多模式神经成像还将导致对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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.