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的数据(如图表)的新型深度学习框架，在早期阶段识别人脑连接中特定于疾病的变化。这一点很重要，因为在积累重大的大脑病理之前，大脑的区域差异通常不会表现为认知变化，而通过表征由不同大脑区域之间的关系定义的连接变化，可能可以更好地了解大脑。最近的深度学习技术已经在图像分类、目标检测和图像分割等各种图像分析任务中证明了具有人类水平的精度的成功结果，但由于其任意的结构，它们不能直接用于分析大脑的连通性。关键是得到数据的有效表示；然而，如何为复杂数据(如图形)得出复杂的表示仍然不清楚，而且它通常需要大规模的数据集。因此，一种新的图形深度学习技术是必要的，它可以在少量样本的情况下检测大脑连接性的细微变化。该项目的成功将有助于了解大脑连接和神经退行性疾病之间的关系，早期诊断的机制，以及发现新的治疗方法。从技术上讲，这个项目的首要目标是为图形数据设计一个卷积神经网络(CNN)模型，并确定它在神经科学中产生新科学发现的程度。为了实现这一目标，该项目将专注于：1)为其理论上由卷积描述的新型多分辨率表示开发一种新的图形变换(例如，大脑网络)；2)为在小样本范围内操作的图形开发有效的深度学习架构，以提高疾病诊断的性能和统计推断的灵敏度；以及3)在模拟研究以及阿尔茨海默病的真实大脑网络数据集上验证所开发的模型，以表征大脑连接中的疾病特定模式。开发的框架将有利于神经成像研究的各个领域，具有本地小规模进行的大脑功能和结构连接，并促进进一步研究的发展。这一奖项反映了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。

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.