The whole-brain connectome in Alzheimer's disease spectrum: A Bayesian meta-analysis of graph theoretical characteristics

Motivation: Alzheimer's disease (AD) is increasingly recognized as a progressive network-disconnection syndrome, but studies on brain network topology changes have yielded inconsistent results. Goal(s): To identify robust changes in multimodal graph theoretical analysis metrics within the AD spectrum. Approach: Conducted Bayesian random-effects meta-analyses on available structural and functional GTA studies, comparing whole-brain network segregation and integration properties among patients with AD, preclinical AD, and healthy controls (HC). Results: The meta-analysis of 53 studies (1753 AD, 1606 preclinical AD, 1937 HC) showed reduced structural network integration and segregation in AD and preclinical AD, and lower functional segregation in AD. Impact: Structural and functional network topology properties change in patients with AD, while in patients with preclinical AD, structural network topology changes occur with relatively preserved functional topology. This supports the progressive disconnection hypothesis in AD spectrum, and further suggests that alterations in structural network topology may precede those in functional network topology.