Abstract
BackgroundIn view of age-related brain changes, identifying factors that are associated with healthy aging are of great interest. In the present study, we compared the functional brain network characteristics of three groups of healthy older participants aged 61–75 years who had a different cognitive and motor training history (multi-domain group: participants who had participated in a multi-domain training; visuomotor group: participants who had participated in a visuomotor training; control group: participants with no specific training history). The study’s basic idea was to examine whether these different training histories are associated with differences in behavioral performance as well as with task-related functional brain network characteristics. Based on a high-density electroencephalographic measurement one year after training, we calculated graph-theoretical measures representing the efficiency of functional brain networks.ResultsBehaviorally, the multi-domain group performed significantly better than the visuomotor and the control groups on a multi-domain task including an inhibition domain, a visuomotor domain, and a spatial navigation domain. In terms of the functional brain network features, the multi-domain group showed significantly higher functional connectivity in a network encompassing visual, motor, executive, and memory-associated brain areas in the theta frequency band compared to the visuomotor group. These brain areas corresponded to the multi-domain task demands. Furthermore, mean connectivity of this network correlated positively with performance across both the multi-domain and the visuomotor group. In addition, the multi-domain group showed significantly enhanced processing efficiency reflected by a higher mean weighted node degree (strength) of the network as compared to the visuomotor group.ConclusionsTaken together, our study shows expertise-dependent differences in task-related functional brain networks. These network differences were evident even a year after the acquisition of the different expertise levels. Hence, the current findings can foster understanding of how expertise is positively associated with brain functioning during aging.
Highlights
In view of age-related brain changes, identifying factors that are associated with healthy aging are of great interest
Graph theoretical analyses of functional connectivity measures originating from different neurophysiological recording techniques generally reveal networks characterized by a small-world topology in most human study samples [9, 11, 12]
Following the significant functional brain network difference in the theta band (F-test), we report pairwise comparisons between groups based on our hypotheses [a significant functional brain network was found for the t-test comparing the multi-domain vs. visuomotor group (2 components, thereof 1 significant: t = 3.6, p = .006, family-wise error rate correction (FWE)-corrected, 18 nodes, 20 edges)]
Summary
In view of age-related brain changes, identifying factors that are associated with healthy aging are of great interest. Based on a high-density electroencephalographic measurement one year after training, we calculated graph-theoretical measures representing the efficiency of functional brain networks. Graph theoretical analyses of functional connectivity measures originating from different neurophysiological recording techniques (e.g., electroencephalography: EEG; magnetencephalography: MEG; and resting state functional magnetic resonance imaging: rs-fMRI) generally reveal networks characterized by a small-world topology in most human study samples [9, 11, 12]. The two most important measures to characterize networks are the clustering coefficient and the characteristic path length [14]. The mean of the distance of all pairs of nodes is referred to as the characteristic path length, a measure of how efficiently a network is connected. Small-world networks designate networks with a high clustering coefficient and a short characteristic path length [9, 11, 13, 15]
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