论文标题
通过MEG脑网络预测阿尔茨海默氏病进展的一种图形高斯嵌入方法
A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks
论文作者
论文摘要
表征与阿尔茨海默氏病（AD）相关的功能性脑网络的细微变化对于早期诊断和预测临床症状之前的疾病进展至关重要。我们开发了一种新的深度学习方法，称为多个Graph Gaussian嵌入模型（MG2G），可以通过将高维静息状态脑网络映射到低维的潜在空间来学习高度信息的网络特征。这些潜在的基于分布的嵌入能够对不同区域的微妙和异构脑连接模式进行定量表征，并可以用作各种下游图分析任务的传统分类器的输入，例如AD早期阶段预测，以及跨大脑区域群体周间重大变化的统计评估。我们使用MG2G检测MEG脑网络的内在潜在维度，预测了轻度认知障碍（MCI）的患者进行AD的进展，并鉴定出具有与MCI相关的网络变化的大脑区域。
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
