Age- and Gender-Related Differences in the Cortical Anatomical Network

Abstract

Neuroanatomical differences attributable to aging and gender have been well documented, and these differences may be associated with differences in behaviors and cognitive performance. However, little is known about the dynamic organization of anatomical connectivity within the cerebral cortex, which may underlie population differences in brain function. In this study, we investigated age and sex effects on the anatomical connectivity patterns of 95 normal subjects ranging in age from 19 to 85 years. Using the connectivity probability derived from diffusion magnetic resonance imaging tractography, we characterized the cerebral cortex as a weighted network of connected regions. This approach captures the underlying organization of anatomical connectivity for each subject at a regional level. Advanced graph theoretical analysis revealed that the resulting cortical networks exhibited "small-world" character (i.e., efficient information transfer both at local and global scale). In particular, the precuneus and posterior cingulate gyrus were consistently observed as centrally connected regions, independent of age and sex. Additional analysis revealed a reduction in overall cortical connectivity with age. There were also changes in the underlying network organization that resulted in decreased local efficiency, and also a shift of regional efficiency from the parietal and occipital to frontal and temporal neocortex in older brains. In addition, women showed greater overall cortical connectivity and the underlying organization of their cortical networks was more efficient, both locally and globally. There were also distributed regional differences in efficiency between sexes. Our results provide new insights into the substrates that underlie behavioral and cognitive differences in aging and sex.

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).…”

“…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.…”

“…Functional connectivity assessment has revealed increased integration and decreased randomness, whereas connectivity decreased significantly during adulthood (Smit et al, 2016). Despite these significant network changes, throughout development brain networks largely maintain small-world properties, modularity and stable hub-regions (Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2007). In general, the aging brain can be characterized by reduced centrality of hub regions with a decrease in global efficiency and an increase in local network clustering.…”

“…The most commonly used node metrics, like the clustering coefficient and path length, are highly dependent on the total number of connections and the average degree of a network van Wijk et al, 2010). This hampers comparability of brain network topology across the human lifespan, as network densities substantially differ between ages (Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2010), which may be explained by changes in the integrity of connecting fibers or the cortical density of neurons (Salat, 2011;Westlye et al, 2010).…”

Bayesian inference of structural brain networks

“…Several approaches have been suggested to extract whole-brain networks from probabilistic tractography results (Robinson et al, 2008;Hagmann et al, 2007;Gong et al, 2009). Unfortunately, inference of whole-brain networks from probabilistic tractography estimates remains somewhat ad hoc.…”