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

CRII：RI：学习图形的新颖多分辨率表示：在阿尔茨海默氏病大脑连接分析中的应用

基本信息

批准号：
1948510
负责人：
Hong Jiang
金额：
$ 17.5万
依托单位：
University of Texas at Arlington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948510&HistoricalAwards=false
关键词：
CRII RI Learning novel multi

项目摘要

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.

该项目旨在通过开发适用于具有任意结构（例如图形）数据的新型深度学习框架来确定早期疾病特异性变化。这很重要，因为在积累了重要的大脑病理之前，区域大脑的变化通常不会表现为认知变化，并且通过表征由不同大脑区域之间关系定义的连通性变化，可以更好地理解大脑。在各种图像分析任务（例如图像分类，对象检测和图像分割）中，具有深入学习的最新技术证明了人级精度的成功结果，但是由于其任意结构，它们无法直接应用于分析大脑连接性。关键是得出有效的数据表示；但是，目前尚不清楚如何得出复杂数据（例如图形）的复杂表示形式，并且通常需要大规模数据集。因此，需要一种新的图形深度学习技术，可以检测出与少量样品的大脑连接性的细微变化。该项目的成功将有助于理解大脑连通性与神经退行性疾病之间的关系，早期诊断的机制以及发现新治疗方法。从技术上讲，该项目的总体目标是为图数据设计一个卷积神经网络（CNN）模型，并确定其在神经科学中产生新的科学发现的程度。为了实现目标，该项目将重点关注：1）为其新颖的图形（例如大脑网络）开发其新颖的多分辨率表示形式（理论上是通过卷积上描述的，它们在图形上进行了有效的深度学习体系结构，2）在小样本尺寸型中运行的图形有效的深度学习体系结构，该体系在一个小样本大小的方案中运行，以改善疾病诊断和敏感性的统计型型型模型，并在3）上有效地研究大脑的表现。阿尔茨海默氏病在大脑连通性中表征特异性疾病的模式。开发的框架将通过本地携带的功能和结构性大脑连通性受益于神经成像研究的各个领域，并在小规模上携带，并刺激了进一步研究的发展。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响来审查标准，通过评估来诚实地对其进行评估。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Dynamic covariance estimation via predictive Wishart process with an application on brain connectivity estimation

DOI：
10.1016/j.csda.2023.107763
发表时间：
2023-04
期刊：
Comput. Stat. Data Anal.
影响因子：
0
作者：
Rui Meng;Fan Yang;Won Hwa Kim
通讯作者：
Rui Meng;Fan Yang;Won Hwa Kim

Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders

学习多分辨率图边缘嵌入以发现神经系统疾病中的大脑网络功能障碍

DOI：
发表时间：
2021
期刊：
Lecture notes in computer science
影响因子：
0
作者：
Ma, Xin;Wu, Guorong;Hwang, Seongjae;Kim, Won Hwa
通讯作者：
Kim, Won Hwa

Image-Label Recovery on Fashion Data Using Image Similarity from Triple Siamese Network

DOI：
10.3390/technologies9010010
发表时间：
2021-03-01
期刊：
TECHNOLOGIES
影响因子：
3.6
作者：
Banerjee, Debapriya;Kyrarini, Maria;Kim, Won Hwa
通讯作者：
Kim, Won Hwa

Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification

DOI：
10.1109/isbi53787.2023.10230493
发表时间：
2023-04
期刊：
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
影响因子：
0
作者：
Seung-Bin Baek;Injun Choi;Mustafa Dere;Minjeong Kim;Guorong Wu;Won Hwa Kim
通讯作者：
Seung-Bin Baek;Injun Choi;Mustafa Dere;Minjeong Kim;Guorong Wu;Won Hwa Kim

Locally Normalized Soft Contrastive Clustering for Compact Clusters