Age-Related Reorganizational Changes in Modularity and Functional Connectivity of Human Brain Networks

Abstract

The human brain undergoes both morphological and functional modifications across the human lifespan. It is important to understand the aspects of brain reorganization that are critical in normal aging. To address this question, one approach is to investigate age-related topological changes of the brain. In this study, we developed a brain network model using graph theory methods applied to the resting-state functional magnetic resonance imaging data acquired from two groups of normal healthy adults classified by age. We found that brain functional networks demonstrated modular organization in both groups with modularity decreased with aging, suggesting less distinct functional divisions across whole brain networks. Local efficiency was also decreased with aging but not with global efficiency. Besides these brain-wide observations, we also observed consistent alterations of network properties at the regional level in the elderly, particularly in two major functional networks-the default mode network (DMN) and the sensorimotor network. Specifically, we found that measures of regional strength, local and global efficiency of functional connectivity were increased in the sensorimotor network while decreased in the DMN with aging. These results indicate that global reorganization of brain functional networks may reflect overall topological changes with aging and that aging likely alters individual brain networks differently depending on the functional properties. Moreover, these findings highly correspond to the observation of decline in cognitive functions but maintenance of primary information processing in normal healthy aging, implying an underlying compensation mechanism evolving with aging to support higher-level cognitive functioning.

Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest

“…Although few studies have directly investigated age-related changes in network anticorrelations (see below), reductions in whole-brain functional segregation, or network modularity, have been reported (Betzel et al, 2014; Chan et al, 2014; Geerligs, Maurits, Renken, & Lorist, 2014; Geerligs et al, 2015; Meunier et al, 2009; Onoda & Yamaguchi, 2013; Song et al, 2014). Age-related changes have also been investigated within specific functional networks.…”

Brain functional connectivity in regions that exhibit age-related cortical thinning

“…This hypothesis maintains that the brain loses some of its segregation with aging due to compensatory mechanisms, with more areas having to work in tandem to perform the same functions than what is observed in the younger adults, thus increasing the temporal correlations between them. Results in this same vein have been previously reported (FERREIRA et al, 2016;GEERLIGS et al, 2015;SONG et al, 2014), though contrasting our specific results, such as proportions of increased or decreased correlations, with the literature is made difficult due to the different ROI definitions, e.g. studies using random parcellations tend to have uniformly sized Contrary to the morphometric analyses, here the linear hypothesis is much more reasonable and parsimonious due to the sheer number of hypotheses being tested, which is equal to 10878, even though evidences of nonlinear functional connectivity trajectories exist (WANG et al, 2012).…”

“…Overall, increased connectivity between networks and decreased connectivity within networks, or results otherwise pointing to cortical de-differentiation or de-modularization, have been often noted in brain-wide studies . (BETZEL et al, 2014;CAO et al, 2014;FERREIRA et al, 2016;GEERLIGS et al, 2015;SALA-LLONCH;BARTRÉS-FAZ;JUNQUÉ, 2015;SONG et al, 2014).…”

Disrupted Brain Functional Organization in Epilepsy Revealed by Graph Theory Analysis

“…This process generated a 187 · 187 connectivity matrix for each subject within each group. We then thresholded each of these connectivity matrices using a minimum spanning tree (MST) approach to obtain a sparse connectivity matrix with optimal functional connections for detecting epileptic alterations of the brain networks (Song et al, 2014). Each MST per subject is a spanning tree of a weighted subgraph that is fully connected with all nodes containing maximum total weights of all links, which can be considered as the skeleton structure of the brain network for each subject.…”