Resting-State Brain in Cognitive Decline: Analysis of Brain Network Architecture Using Graph Theory

Abstract

Resting-State functional magnetic resonance imaging (rs-fMRI) provides the assessment of some brain functions without tasks. Through rs-fMRI, it is possible to discover that the brain is organized in spatially distributed and interconnected brain regions. Studies suggest that aging and certain neurological or neuropsychiatric diseases affect brain connectivity, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). The general objective of this work is to investigate the evolution of the brain connectivity of individuals with healthy aging who convert to MCI, and individuals with MCI who convert to AD, using rs-fMRI and analysis based on graph theory (GT). The processing was implemented in SPM12-MATLAB, and the analysis was performed in the CONN Toolbox. The GT metrics chosen to describe the main topological characteristics of the networks were: characteristic path length, global efficiency, local efficiency, clustering coefficient, and degree. Two main findings emerged from this study. When using GT metrics and analyzing healthy subjects converting to MCI, it was possible to observe a decrease in all GT metrics. Second, changes in GT metrics indicated a rupture in the functional connectivity when the cognitive decline occurs from healthy aging to MCI, and from MCI to AD.

Denoising Approach Affects Diagnostic Differences in Brain Connectivity across the Alzheimer’s Disease Continuum

“…A significant issue in this literature, however, is that there is little consensus in the reported direction of diagnostic group differences which impedes efforts to draw conclusions about the mechanisms underlying network dysfunction and their functional consequences in AD. For example, studies investigating clustering coefficient, a measure of network segregation, have reported higher clustering coefficient in MCI or AD than CN (Maulaz, de Almeida Mantovani, & da Silva, 2020; Zhao et al, 2012; Z. Liu et al, 2012; Dai et al, 2019), lower clustering coefficient in MCI or AD than CN (Xiang, Guo, Cao, Liang, & Chen, 2013; Si et al, 2019; Li et al, 2020; Brier et al, 2012; Dai & He, 2014; Xue et al, 2020, 2020), or no diagnostic differences (L.…”