Age‐related changes in topological organization of structural brain networks in healthy individuals

Abstract

The aim of this study was to examine structural brain networks using regional gray matter volume, as well as to investigate changes in small-world and modular organization with normal aging. We constructed structural brain networks composed of 90 regions in young, middle, and old age groups. We randomly selected 350 healthy subjects for each group from a Japanese magnetic resonance image database. Structural brain networks in three age groups showed economical small-world properties, providing high global and local efficiency for parallel information processing at low connection cost. The small-world efficiency and node betweenness varied significantly and revealed a U- or inverted U-curve model tendency among three age groups. Results also demonstrated that structural brain networks exhibited a modular organization in which the connections between regions are much denser within modules than between them. The modular organization of structural brain networks was similar between the young and middle age groups, but quite different from the old group. In particular, the old group showed a notable decrease in the connector ratio and the intermodule connections. Combining the results of small-world efficiency, node betweenness and modular organization, we concluded that the brain network changed slightly, developing into a more distributed organization from young to middle age. The organization eventually altered greatly, shifting to a more localized organization in old age. Our findings provided quantitative insights into topological principles of structural brain networks and changes related to normal aging.

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

“…local clustering and global efficiency) over all age categories, in contrast with previous literature showing alterations in both structural (Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2010;Montembeault et al, 2012;Otte et al, 2015;Wu et al, 2012;Zhu et al, 2012) and functional brain networks across life-span (Achard and Bullmore, 2007;Betzel et al, 2014;Meier et al, 2012;Meunier et al, 2009;Nathan Spreng and Schacter, 2012;Simpson and Laurienti, 2015;Smit et al, 2016;Wang et al, 2012). Furthermore, our findings are also in contrast with significant differences found in a previous exponential random graph modeling study in functional networks , and a recently developed similar approach (also discussed below) which revealed differences in functional networks across the lifespan, such as older adults having stronger connections between highly clustered nodes, or less assortativity in visual and multisensory regions (Simpson and Laurienti, 2015).…”

“…Other studies have shown a higher local clustering and lower global efficiency in older adults compared to younger adults (Zhu et al, 2012), where modularity decreases across networks (Meunier et al, 2009). In line with these findings several studies have shown inverted-U shaped global efficiency across lifespan (Otte et al, 2015;Wu et al, 2012). Functional connectivity assessment has revealed increased integration and decreased randomness, whereas connectivity decreased significantly during adulthood (Smit et al, 2016).…”

“…the average shortest path length, maximum betweenness centrality or overall clustering coefficient) (Bullmore and Sporns, 2009) and/or network properties such as small-worldness, rich club connectedness (Bullmore and Sporns, 2012;Cao et al, 2014) and modularity (Rubinov and Sporns, 2010). In the past decade, multiple studies have shown that normal aging is associated with substantial alterations in NeuroImage 135 (2016) [79][80][81][82][83][84][85][86][87][88][89][90][91] structural Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2010;Lim et al, 2015;Montembeault et al, 2012;Otte et al, 2015;Wu et al, 2012;Zhu et al, 2012) and functional (Achard and Bullmore, 2007;Andrews-Hanna et al, 2007;Betzel et al, 2014;Meier et al, 2012;Meunier et al, 2009;Nathan Spreng and Schacter, 2012;Wang et al, 2012) brain networks. Some of these studies focused on specific age categories: childhood to adulthood (Dennis et al, 2013;Hagmann et al, 2010) or young and older adults (e.g.…”

Graph-based network analysis in schizophrenia

“…Modular organization changes greatly in elderly people compared with the young and middleaged. The old showed a decrease in the connector ratio and inter-module connections [51] . In another study, older people showed a decrease in the inter-modular organization frontal and an increase in the posterior and central modules [35] .…”

Structure and function of the aging brain

“…As with global and regional measures of structural brain changes, structural covariance changes with age are more prominent between frontal brain and posterior cortices, reflecting a loss of long-range covariance in favor of increased local processing [61,63,64]. Another prominent feature of network-level changes is declining structural covariance within the default network, a collection of functionally-connected brain regions implicated in mnemonic and associative processing [65].…”