Age-Related Differences in the Modulation of Small-World Brain Networks during a Go/NoGo Task

Abstract

Although inter-regional phase synchrony of neural oscillations has been proposed as a plausible mechanism for response control, little is known about the possible effects due to normal aging. We recorded multi-channel electroencephalography (EEG) from healthy younger and older adults in a Go/NoGo task, and examined the aging effects on synchronous brain networks with graph theoretical analysis. We found that in both age groups, brain networks in theta, alpha or beta band for either response execution (Go) or response inhibition (NoGo) condition showed prominent small-world property. Furthermore, small-world property of brain networks showed significant differences between different task conditions. Further analyses of node degree suggested that frontal-central theta band phase synchrony was enhanced during response inhibition, whereas during response execution, increased phase synchrony was observed in beta band over central-parietal regions. More interestingly, these task-related modulations on brain networks were well preserved and even more robust in older adults compared with younger adults. Taken together, our findings not only suggest that response control involves synchronous brain networks in functionally-distinct frequency bands, but also indicate an increase in the recruitment of brain network resources due to normal aging.

Keywords: aging; graph theory; induced activity; phase synchrony; response inhibition.

Figure 1

The schematic diagram for the brain network analysis. (A) Electroencephalography (EEG) epochs after preprocessing steps. (B) The event-related potentials (ERPs) were computed by averaging the same type of EEG epochs (Go, NoGo), which was reported in our previous study (Hong et al., 2014). (C) ERP activity was subtracted from EEG epochs. (D) Frequency-domain analysis of oscillatory power using Fourier analysis. (E) Phase synchronization (PS) index was computed for each pair of channels in each trial, and the connectivity matrix was averaged across the same type of EEG epochs (Baseline, Go, NoGo). (F) Different thresholds were applied on the connectivity matrix to construct brain networks, which were then analyzed using graph theoretical metrics.

Figure 2

Group-averaged task-related oscillatory power changes in different frequency bands for the younger group (A) and older group (B).

Figure 3

Group-averaged normalized clustering coefficient (γ), characteristic path length (λ) and small-worldness index (σ) of the brain networks for all task conditions (Baseline, Go, NoGo) in the younger group (A) and older group (B) under different network density levels. Error bars indicate the standard error of the mean (SEM).

Figure 4

Topographic maps for t-values of the node degree between different task conditions for the younger group (A) and older group (B) in different frequency bands. The results are illustrated under the network density level of 120 edges.

Figure 5

(A) Group-averaged node degree within the frontal-central (theta band) and central-parietal (beta band) regions of interest (ROIs). Error bars indicate SEM. (B) Group-averaged differences of connectivity strength between different task conditions. Only the connections with absolute differences greater than 0.05 are shown in the figure. The results are illustrated under the network density level of 120 edges.