Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease

TL;DR

This paper introduces a Bayesian hierarchical model to infer and compare multiple brain connectivity networks across different stages of Alzheimer's disease, revealing key structural changes associated with disease progression.

Contribution

It develops a novel joint Bayesian framework that learns group similarities and differences in brain connectivity, accounting for connection strength, which improves structural network inference in Alzheimer's research.

Findings

Decreased occipital lobe connectivity with disease progression

Method outperforms existing approaches in structure learning

Identifies key brain alterations linked to Alzheimer's severity

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 which 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.