Age-Related Alterations in EEG Network Connectivity in Healthy Aging

Abstract

Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

Hub structure in functional network of EEG signals supporting high cognitive functions in older individuals

“…Since BC can assess the global hub structure, it enables capturing the relationship with the extensive cognitive functions by global neural interaction. In comparison with the previous studies on healthy aging, this study demonstrated the effectiveness of BC in the evaluation of the global frequency-band specific topological characteristics for the first time; while the previous studies used the indices defined by the adjacent connectivity such as clustering coefficient and nodal centrality (Knyazev et al, 2015;Javaid et al, 2022).…”

“…For example, in AD, alterations in topological network characteristics, such as the loss of small-world characteristics, have been reported (Stam et al, 2007b ; de Haan et al, 2009 ). In addition, these alterations, typified by the clustering coefficient and nodal centrality, appear even in healthy aging individuals (Knyazev et al, 2015 ; Javaid et al, 2022 ). Of the different kinds of topological features, the brain region termed a “hub,” i.e., the most important brain region affecting the activity of the whole network, is an essential factor coordinating whole-brain interactions.…”

“…In particular, Engels et al ( 2015 ) showed that the alternation of BC was related to the stage of AD severity and demonstrated the effectiveness of usage of BC in the evaluation of pathological cognitive deficits. Knyazev et al ( 2015 ) and Javaid et al ( 2022 ) demonstrated that under healthy aging conditions the hub structure in the functional networks decreases with aging. However, Knyazev et al ( 2015 ) captured the hub structure by modularity based on the average among adjacent connectivity [called as node degree (ND)], instead of pathways among whole brain regions as in BC.…”

Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features

“…Such alterations in resting-state EEG connectivity in slow rhythms (delta and theta) has also been reported in childhood developmental disorders, such as autism spectrum disorders [83] and specific learning disorders [84]. On the contrary, healthy aging is marked by decreased slow frequency activity (band power) in the delta and theta bands during the resting state [85] as well as by reduced EEG network connectivity [86]. On the other hand, during the task performance, both delta and theta band oscillations predominantly contribute to the generation of P300 or P3 [87], a prominent event-related potential (ERP) component that is a marker of contextual neural processing, the amplitude of which is reduced abnormal in individuals with and/or at risk for AUD, who have shown reduced amplitudes [9].…”

Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder

“…Previous research has seldom focused on the EEG neural mechanism of different ages for GAD, and there is little precedent for GAD classification at different age levels with machine learning models. However, different age groups of GAD have been authenticated to play different symptoms ( Javaid et al, 2022 ). Therefore, it is necessary to expand the corresponding EEG studies to provide a scientific foundation for the clinical triage of GAD patients of different ages.…”