CRII: RI: Learning novel multi-resolution representations of graphs: Applications to Brain Connectivity analysis for Alzheimer's Disease

该项目旨在通过开发一种适用于具有任意结构（如图形）的数据的新型深度学习框架，在早期阶段识别人类大脑连接的疾病特异性变化。这一点很重要，因为区域性大脑变化通常不会表现为认知变化，直到显著的大脑病理学已经积累，并且通过表征由不同大脑区域之间的关系定义的连接性的变化，可以更好地理解大脑。深度学习的最新技术在各种图像分析任务（如图像分类、对象检测和图像分割）中表现出了人类水平精度的成功结果，但由于其任意结构，它们不能直接应用于分析大脑连接。关键是获得数据的有效表示;然而，目前还不清楚如何获得复杂数据（如图形）的复杂表示，并且通常需要大规模数据集。因此，一种新的图深度学习技术，可以用少量样本检测大脑连接的细微变化是必要的。该项目的成功将有助于理解大脑连接和神经退行性疾病之间的关系，早期诊断机制和发现新的治疗方法。从技术上讲，该项目的首要目标是为图形数据设计卷积神经网络（CNN）模型，并确定它在神经科学中产生新科学发现的程度。为了实现这个目标，这个项目将集中在：1）开发一个新的图变换（例如，脑网络），因为它们在理论上由卷积描述的新颖的多分辨率表示，2）开发用于在小样本大小范围内操作的图的有效深度学习架构，以提高疾病诊断的性能和统计推断的灵敏度，以及3）在模拟研究以及用于阿尔茨海默病的真实的脑网络数据集上验证所开发的模型以表征疾病-大脑连接的特定模式。该框架将使神经影像学研究的各个领域受益，并促进进一步研究的发展。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审查标准进行评估，被认为值得支持。

This project aims to identify disease-specific changes in human brain connectivity in early stages by developing a novel Deep Learning framework applicable to data with arbitrary structure such as graphs. This is important because regional brain variations often do not manifest as cognitive changes until significant brain pathology has accumulated, and a better understanding of the brain may be possible by characterizing changes in connectivity defined by relationships between different brain regions. Recent techniques with Deep Learning have demonstrated successful results with human-level precision in various image analysis tasks such as image classification, object detection, and image segmentation, but they cannot be directly applied to analyze brain connectivity because of its arbitrary structure. The key is to derive effective representation of the data; however, it is still unclear how to derive sophisticated representations for complex data such as graphs and it often requires large-scale datasets. A novel Graph Deep Learning technique that can detect subtle changes in brain connectivity with small numbers of samples is therefore necessary. Success of this project will facilitate understanding of the relationship between brain connectivity and neurodegenerative disease, mechanisms for early diagnosis, and discovery of new treatments. Technically, the overarching goal of this project is to design a Convolution Neural Network (CNN) model for graph data and to determine the extent to which it yields new scientific findings in neuroscience. To meet the goal, this project will focus on: 1) Developing a novel transform for graphs (e.g., brain networks) for their novel multi-resolution representations that are theoretically described by convolution, 2) Developing an efficient deep learning architecture for graphs that operates within a small sample-size regime to improve performance of disease diagnosis and sensitivity of statistical inferences, and 3) Validating the developed models on a simulation study as well as real brain network datasets for Alzheimer’s Disease to characterize disease-specific patterns in the brain connectivity. The developed framework will benefit various areas of neuroimaging research with functional and structural brain connectivity that are locally carried at small scales, and spur development of further studies.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.