Regular articleReconfiguration of brain network architecture to support executive control in aging
Introduction
Cognitive decline is pervasive in older adulthood, notably in executive control processes thought to be subserved by the frontal cortex (Grady, 2008, Grady, 2012, Park et al., 2002). Extensive alterations in brain structure and function are also observed in older adults. Functional changes in aging have been documented in the activation of individual brain regions and in the functional connectivity between brain regions (Grady, 2012, Spreng et al., 2010, Turner and Spreng, 2012, Turner and Spreng, 2015). Alterations in functional connectivity are thought to be related, in part, to a decline in structural connectivity, such as through long-range white matter fiber tracts (Bennett and Madden, 2014).
Further work in older adults has examined the functional communication among groups of brain regions by quantifying the connectivity of brain subnetworks (Andrews-Hanna et al., 2007, Damoiseaux et al., 2008, Ferreira and Busatto, 2013), such as the default-mode and fronto-parietal networks. However, executive control processes rely on the integration of signals from frontal cortex to widely distributed brain regions, likely not limited to specific subnetworks as has been examined thus far (Barceló et al., 2000, Chao and Knight, 1998, Fuster et al., 1985, Knight et al., 1999, Lee and D'Esposito, 2012, Miller and D'Esposito, 2005). Thus, changes in executive control processing in aging may be better examined by methods that quantify the large-scale (e.g., whole brain) network organization of the brain. Graph theoretical methods describe the brain as a complex network, comprised of functionally separable subnetworks or modules. This type of organization is critical for supporting both local processing within and global processing between modules. Using graph theory, the modularity of network organization can be quantified (Meunier et al., 2009b, Meunier et al., 2010), where networks with high modularity have dense connections within modules and sparser connections between modules.
Studies examining modular network organization during working memory have shown that increasing executive control demands (i.e., increasing N-back load) are supported by a more integrated network organization, manifested in decreased modularity (Kitzbichler et al., 2011, Vatansever et al., 2015, Wen et al., 2015) and increased connectivity between network modules (Liang et al., 2015, Stanley et al., 2014). This reconfiguration of brain networks has also been observed when comparing networks from a task-free ‘resting state’ to those during the performance of tasks with increasing demands (Wen et al., 2015).
In older adults, analyses of structural MRI and resting-state fMRI data have demonstrated that aging is associated with declines in modularity (Chan et al., 2014, Chen et al., 2011b, Geerligs et al., 2014a, Meunier et al., 2009a, Onoda and Yamaguchi, 2013). Importantly, these studies have not examined how networks reconfigure during the performance of a task in older adults. To understand how network-level changes contribute to age-related alterations in executive control, it is critical to investigate changes in brain network properties during cognitive processing.
In this study, we examine how the modular organization of the brain reconfigures between the absence of a task (e.g., a resting state) and the performance of an N-back task in older and young adults. We first quantify the topological overlap of modules identified during resting state and task. We next examine changes in between-module connectivity with increasing cognitive demands, both across the entire brain (i.e., modularity) and in a subset of lateral frontal regions. We also examine how changes in between-module connectivity from a resting state to task are related to behavioral performance. Finally, as aging is associated with declines in white matter pathways (Bennett and Madden, 2014), we investigate how frontal-posterior structural connectivity is related to functional reconfiguration of brain networks in older adults.
Section snippets
Participants
Eighteen young (10 females; mean age = 21.08, range = 18–26) and 38 older (24 females; mean age = 66.97, range = 60–80) adults were included in this analysis. Young and older participants were matched on distribution of gender (χ2(1, N = 56) = 0.30, p = 0.59). Older participants had greater years of education compared with young participants (mean ± standard error of the mean, older: 17.42 ± 0.49; young: 14.53 ± 0.48; t(54) = 3.67, p = 0.001). Participants were prescreened for the presence of
Task performance
Accuracy and reaction time (RT) analyses revealed main effects of age group (accuracy: F(1,54) = 5.74, p = 0.02, = 0.10; RT: F(1,54) = 5.03, p = 0.03, = 0.09) and task condition (accuracy: overall effect, F(3,162) = 28.50, p < 0.001, = 0.35 and linear contrast, F(1,54) = 60.04, p < 0.001, = 0.53; RT: overall effect, F(3,162) = 141.90, p < 0.001, = 0.72 and linear contrast, F(1,54) = 200.92, p < 0.001, = 0.79). Across all conditions, older adults had lower accuracy and
Discussion
Here, we analyzed resting state and task-based fMRI data to characterize brain network reconfiguration that supports executive control functioning (i.e., performance of an N-back task) in older adults. Recent studies have shown that older adults have a less-modular brain network organization in a resting state compared with young adults (Chan et al., 2014, Geerligs et al., 2014a, Onoda and Yamaguchi, 2013). However, the extent to which this organization reconfigures during a cognitive task in
Disclosure statement
The authors declare no competing financial interests.
Acknowledgements
This work was supported by the National Institutes of Health (grant number NS79698 to MD), a Natural Science and Engineering Council of Canada Discovery Grant to GRT, and the Department of Defense Air Force Office of Scientific Research (National Defense Science and Engineering Graduate Fellowship 32 CFR 168a to CLG).
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