Continuing Tool Development for Longitudinal Network Analysis: Enriching the Diagnostic Power of Disease-Specific Connectomic Biomarkers by Deep Graph Learning

纵向网络分析的持续工具开发：通过深度图学习丰富疾病特异性连接组生物标志物的诊断能力

• 批准号: 10359157
• 负责人: Guorong Wu
• 金额: $ 14.47万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10359157
• 关键词: Address; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Artificial Intelligence; Biological Markers; Brain; Clinical; Cognitive; Communities; Computer software; Computer-Assisted Diagnosis; Data; Databases; Diagnostic; Dimensions; Disease; Early Diagnosis; Event; Evolution; Frontotemporal Dementia; Goals; Graph; Image; Impaired cognition; Individual; Industry; Investigation; Label; Learning; Longitudinal Studies; Machine Learning; Measurement; Methods; Modeling; Nerve Degeneration; Network-based; Neural Network Simulation; Neurodegenerative Disorders; Neurofibrillary Tangles; Neurosciences; Outcome; Parkinson Disease; Pathway Analysis; Pattern; Population; Process; Prognosis; Research; Resolution; Resources; Sample Size; Senile Plaques; Sensitivity and Specificity; Source Code; Supervision; Symptoms; Syndrome; Techniques; Text; Time; United States National Institutes of Health; base; clinical practice; cohort; collaboratory; comparison group; computerized tools; data mining; data structure; deep learning; design; diagnostic value; graph learning; graph neural network; high dimensionality; machine learning method; method development; network dysfunction; neural network; neuroimaging; novel; software development; success; tool; tool development

Project Summary/Abstract A plethora of neuroscience studies shows mounting evidence that neurodegenerative diseases manifest distinct network dysfunction patterns much earlier prior to the onset of clinical symptoms. Since the subject-specific longitudinal network changes are more relevant to the neuropathological process than topological patterns derived from cross-sectional data, recognizing the subtle and dynamic longitudinal network biomarkers from noisy network data is of great demand to enhance the sensitivity and specificity of computer-assisted diagnosis in neurodegenerative diseases. However, current popular statistical inference or machine learning approaches used for neuroimages (in a regular data structure such as grid and lattice) are not fully optimized for the learning task on brain network data which is often encoded in a high dimensional graph (an irregular and non-linear data structure). Such gross adaption is partially responsible for the lack of reliable biomarkers that can be used to predict cognitive decline in routine clinical practice. To address this challenge, we aim to (1) develop a novel GNN (graph neural network) based learning framework to hierarchically discover the multi-scale network biomarkers that can recognize the disease-relevant network alterations over time, and (2) examine the diagnostic power of the new network biomarkers derived from our GNN-based machine learning engine across neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and frontotemporal dementia. The success of this project will allow us to integrate the novel GNN-based learning component into our current longitudinal network analysis toolbox and release the AI (artificial intelligence) based network analysis software to the neuroscience and neuroimaging community.

项目摘要/摘要 大量神经科学研究表明，神经退行性疾病表现出不同的证据 网络功能障碍模式在临床症状发作之前很早。由于特定于主题 纵向网络变化与神经病理学过程相比，与拓扑模式更相关 源自横截面数据，识别从中微妙而动态的纵向网络生物标志物 嘈杂的网络数据非常需要增强计算机辅助诊断的敏感性和特异性 在神经退行性疾病中。但是，当前流行的统计推断或机器学习方法 用于神经图像（在常规数据结构（例如网格和晶格）中）并未完全优化学习 大脑网络数据的任务通常在高维图中编码（不规则和非线性数据 结构）。这种总体适应是由于缺乏可靠的生物标志物的部分原因 预测常规临床实践的认知能力下降。为了应对这一挑战，我们的目标是（1）发展小说 基于GNN（图神经网络）的学习框架，以层次发现多尺度网络 可以识别疾病与疾病相关的网络变化的生物标志物，以及（2）检查诊断 新网络生物标志物的功能来自我们基于GNN的机器学习引擎 神经退行性疾病，例如阿尔茨海默氏病，帕金森氏病和额颞痴呆。 该项目的成功将使我们能够将基于GNN的新型学习组成部分整合到当前 纵向网络分析工具箱并发布基于AI（人工智能）的网络分析软件 致神经科学和神经影像社区。

项目成果

Joint hub identification for brain networks by multivariate graph inference.

• DOI: 10.1016/j.media.2021.102162
• 发表时间: 2021-10
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Yang D;Zhu X;Yan C;Peng Z;Bagonis M;Laurienti PJ;Styner M;Wu G
• 通讯作者: Wu G

Group-Wise Hub Identification by Learning Common Graph Embeddings on Grassmannian Manifold.

• DOI: 10.1109/tpami.2021.3081744
• 发表时间: 2022-11
• 期刊: IEEE transactions on pattern analysis and machine intelligence
• 影响因子: 23.6

Characterizing Network Selectiveness to the Dynamic Spreading of Neuropathological Events in Alzheimer's Disease.

• DOI: 10.3233/jad-215596
• 发表时间: 2022
• 期刊: JOURNAL OF ALZHEIMERS DISEASE
• 影响因子: 4
• 作者: Li, Wenchao;Yang, Defu;Yan, Chenggang;Chen, Minghan;Li, Quefeng;Zhu, Wentao;Wu, Guorong
• 通讯作者: Wu, Guorong

Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.

用于功能性神经影像生物标志物识别的多波段脑网络分析

• DOI: 10.1109/tmi.2021.3099641
• 发表时间: 2021-12
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Hu R;Peng Z;Zhu X;Gan J;Zhu Y;Ma J;Wu G
• 通讯作者: Wu G

Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.

• DOI: 10.1002/hbm.25897
• 发表时间: 2022-09
• 期刊: HUMAN BRAIN MAPPING
• 影响因子: 4.8
• 作者: Dan, Tingting;Huang, Zhuobin;Cai, Hongmin;Lyday, Robert G.;Laurienti, Paul J.;Wu, Guorong
• 通讯作者: Wu, Guorong