Characterization of young and old adult brains: An EEG functional connectivity analysis

Abstract

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 94% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and approximately two-thirds of connections involving occipital electrodes, showed decreased connectivity with older age.Just over half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age had significantly longer electrode-to-electrode distance than those that increased with older age.Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with ageing during a resting state.

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

“…There has been significant interest in studying task-based or resting-state brain's functional connectivity in healthy and patient subjects, which has resulted in new understanding about the brain function as well as the mechanisms underlying brainrelated disorders [117,118,119,120,121,122,123,124,125,17,126]. In BCIs, functional connectivity measures have been employed as features to discriminate various tasks [28,26,27,22,23].…”

Healthy aging and cognitive impairment alter EEG functional connectivity in distinct frequency bands

“…Many studies have reported changes in FC in different frequency bands with cognitive tasks, aging and with brain disorders (Ishii et al, 2017). For example, reduction in alpha (8-12 Hz) FC was reported with healthy aging in resting state EEG (Moezzi et al, 2019), while gamma (30-55 Hz) FC reduction was reported in autism (Safar et al, 2020), and in Alzheimer's Disease (AD) and schizophrenia (Uhlhaas and Singer, 2006).…”

NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis

“…Long-range synchrony between frontal and parietal cortices in the Beta band has been observed to dominate during top-down attentional processing (Buschman and Miller, 2007) and is thought to support the enhancement of task-relevant information (Antzoulatos and Miller, 2016). There is also some evidence that Beta band connectivity increases with aging (Moezzi et al, 2019; Vysata et al, 2014). The results did not yield support for previous observations of inter-hemispheric asymmetry reduction with age (Dolcos et al, 2002) in terms of increasing inter-hemispheric connectivity (Maurits et al, 2006).…”