Alteration of Neural Network Activity With Aging Focusing on Temporal Complexity and Functional Connectivity Within Electroencephalography

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

Introduction

Maintaining high cognitive functions is desirable for "wellbeing" in old age and is particularly relevant to a super-aging society. According to their individual cognitive functions, optimal intervention for older individuals facilitates the maintenance of cognitive functions. Cognitive function is a result of whole-brain interactions. These interactions are reflected in several measures in graph theory analysis for the topological characteristics of functional connectivity. Betweenness centrality (BC), which can identify the "hub" node, i.e., the most important node affecting whole-brain network activity, may be appropriate for capturing whole-brain interactions. During the past decade, BC has been applied to capture changes in brain networks related to cognitive deficits arising from pathological conditions. In this study, we hypothesized that the hub structure of functional networks would reflect cognitive function, even in healthy elderly individuals.

Method

To test this hypothesis, based on the BC value of the functional connectivity obtained using the phase lag index from the electroencephalogram under the eyes closed resting state, we examined the relationship between the BC value and cognitive function measured using the Five Cognitive Functions test total score.

Results

We found a significant positive correlation of BC with cognitive functioning and a significant enhancement in the BC value of individuals with high cognitive functioning, particularly in the frontal theta network.

Discussion

The hub structure may reflect the sophisticated integration and transmission of information in whole-brain networks to support high-level cognitive function. Our findings may contribute to the development of biomarkers for assessing cognitive function, enabling optimal interventions for maintaining cognitive function in older individuals.

Enhanced temporal complexity of EEG signals in older individuals with high cognitive functions

Recent studies suggest that the maintenance of cognitive function in the later life of older people is an essential factor contributing to mental wellbeing and physical health. Particularly, the risk of depression, sleep disorders, and Alzheimer's disease significantly increases in patients with mild cognitive impairment. To develop early treatment and prevention strategies for cognitive decline, it is necessary to individually identify the current state of cognitive function since the progression of cognitive decline varies among individuals. Therefore, the development of biomarkers that allow easier measurement of cognitive function in older individuals is relevant for hyperaged societies. One of the methods used to estimate cognitive function focuses on the temporal complexity of electroencephalography (EEG) signals. The characteristics of temporal complexity depend on the time scale, which reflects the range of neuron functional interactions. To capture the dynamics, composed of multiple time scales, multiscale entropy (MSE) analysis is effective for comprehensively assessing the neural activity underlying cognitive function in the brain. Thus, we hypothesized that EEG complexity analysis could serve to assess a wide range of cognitive functions in older adults. To validate our hypothesis, we divided older participants into two groups based on their cognitive function test scores: a high cognitive function group and a low cognitive function group, and applied MSE analysis to the measured EEG data of all participants. The results of the repeated-measures analysis of covariance using age and sex as a covariate in the MSE profile showed a significant difference between the high and low cognitive function groups (F = 10.18, p = 0.003) and the interaction of the group × electrodes (F = 3.93, p = 0.002). Subsequently, the results of the post-hoct-test showed high complexity on a slower time scale in the frontal, parietal, and temporal lobes in the high cognitive function group. This high complexity on a slow time scale reflects the activation of long-distance neural interactions among various brain regions to achieve high cognitive functions. This finding could facilitate the development of a tool for diagnosis of cognitive decline in older individuals.