Peterson Christine B, Osborne Nathan, Stingo Francesco C, Bourgeat Pierrick, Doecke James D, Vannucci Marina
Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas.
Department of Statistics, Rice University, Houston, Texas.
Biometrics. 2020 Dec;76(4):1120-1132. doi: 10.1111/biom.13235. Epub 2020 Feb 19.
Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity that may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.
阿尔茨海默病是最常见的神经退行性疾病。本研究的目的是利用一组参与者（包括健康对照者、轻度认知障碍者或阿尔茨海默病患者）的皮质厚度测量值，推断疾病进展导致的脑连接结构变化。为此，我们开发了一种新方法，用于推断跨组具有相关边值的多个网络。具体来说，我们在一个联合框架内为每个组推断一个高斯图形模型，在该框架中，我们依靠贝叶斯层次先验来链接跨组的精度矩阵条目。我们的提议与现有方法的不同之处在于，它能灵活地了解哪些组具有最相似的边值，并在跨组共享信息时考虑连接强度（而不仅仅是边的存在或不存在）。我们的结果确定了结构连接中的关键改变，这些改变可能反映了健康大脑的破坏，例如随着疾病严重程度的增加，枕叶内的连接减少。我们还通过模拟说明了所提出的方法，在模拟中我们展示了其在结构学习和精度矩阵估计方面相对于其他方法的性能。
