Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease. Issue 4 (19th February 2020)

• Record Type: Journal Article
• Authors: Peterson, Christine B.; Osborne, Nathan; Stingo, Francesco C.; Bourgeat, Pierrick; Doecke, James D.; Vannucci, Marina
• Is Part Of: Biometrics. Volume 76:Issue 4(2020)
• Page Start: 1120
• Page End: 1132
• Publication Date: 2020-02-19
• Subjects: AIBL study -- Alzheimer's disease -- Bayesian inference -- Gaussian graphical model -- MRI data; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.13235
• Languages: English
• ISSNs: 0006-341X

Abstract

Abstract: 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.