Longitudinal Analysis of the Dynamic Network Disruptions in Alzheimer's Disease

阿尔茨海默病动态网络中断的纵向分析

• 批准号: 9508126
• 负责人: Martin Andreas Styner
• 金额: $ 23.33万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9508126
• 关键词: Address; Aging; Alzheimer' s Disease; Anatomy; Area; Biometry; Brain; Brain region; Clinical; Code; Communities; Complex; Computer software; Data; Databases; Dementia; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Disease Progression; Early Diagnosis; Ensure; Evolution; Exhibits; Functional disorder; Goals; Graph; Heterogeneity; Human; Image; Individual; Informatics; Learning; Longitudinal Studies; Machine Learning; Maps; Measures; Medical Imaging; Methods; Modernization; Molecular; Neural Pathways; Neurodegenerative Disorders; Neurosciences; Outcome; Pathology; Pathway Analysis; Pattern; Peripheral; Population; Process; Research; Resources; Role; Science; Series; Source Code; Statistical Data Interpretation; Syndrome; System; Techniques; Time; base; computational neuroscience; computerized tools; connectome; design; endophenotype; longitudinal analysis; mental state; network dysfunction; neurocognitive disorder; neuroimaging; novel; phenotypic data; potential biomarker; pre-clinical; prevent; programs; relating to nervous system; therapy design; tool

Abstract A plethora of neuroscience studies has found that Alzheimer’s disease (AD) can be understood as a dysfunction syndrome where the structural and functional connectivity of the large-scale network are progressively disrupted by molecular pathomechanism that is not fully understood. The disruptions to the network exhibit dynamic patterns at different stages of AD, which holds valuable clues to understand AD progression. Current network computational tools are designed for cross-sectional data only, which is insufficient to maintain temporal consistency in investigating longitudinal network changes. To address this problem, we will develop the first extensive computational tool for longitudinal network analysis. Specifically, we will propose a learning-based approach to precisely quantify the evolution of brain network from noisy imaging data (Aim 1). Sparse representation and tensor analysis technique will be integrated to seek for the consistent longitudinal brain networks. We will apply our longitudinal network analysis tool to the series diffusion-weighted imaging (DWI) data from ADNI database to investigate how Alzheimer’s disease attacks human brain network by inspecting the dynamic interactions between the hub and non-hub nodes in AD progression (Aim 2). The outcome of this project will be the first longitudinal brain network analysis tool in computational neuroscience and neuroimaging fields. We will release the software (both binary program and source code), to facilitate the network studies in other neuro diseases that show brain network dysfunction.

摘要 大量神经科学研究发现，阿尔茨海默病（AD）可以被理解为一种 功能障碍综合征，其中大规模网络的结构和功能连接是 被尚未完全理解的分子病理机制逐渐破坏。遭受干扰的程度 网络在AD的不同阶段呈现出动态的模式，这为理解AD提供了有价值的线索 进展当前的网络计算工具仅针对横截面数据设计， 不足以在调查纵向网络变化时保持时间一致性。为了解决这个 问题，我们将开发第一个广泛的纵向网络分析计算工具。具体地说， 我们将提出一种基于学习的方法来精确量化大脑网络从噪声 成像数据（Aim 1）。将稀疏表示和张量分析技术相结合， 一致的纵向大脑网络我们将把我们的纵向网络分析工具应用于该系列 ADNI数据库中的弥散加权成像（DWI）数据，以研究阿尔茨海默病如何发作 通过检测AD中的中枢节点和非中枢节点之间的动态交互， 进展（目标2）。该项目的成果将是第一个纵向脑网络分析工具， 计算神经科学和神经成像领域。我们将发布软件（二进制程序和 源代码），以促进在其他神经系统疾病，显示大脑网络功能障碍的网络研究。