Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis.

Autor: Moezzi B; Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia. Electronic address: bahar.moezzi@mymail.unisa.edu.au., Pratti LM; Not affiliated to an organization, Adelaide, Australia., Hordacre B; School of Health Sciences, University of South Australia, Australia., Graetz L; Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia., Berryman C; Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia., Lavrencic LM; Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia; Neuroscience Research of Australia, Australia., Ridding MC; Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia., Keage HAD; Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia., McDonnell MD; Computational Learning Systems Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia, Australia., Goldsworthy MR; Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia.
Jazyk: angličtina
Zdroj: Neuroscience [Neuroscience] 2019 Dec 01; Vol. 422, pp. 230-239.
DOI: 10.1016/j.neuroscience.2019.08.038
Abstrakt: 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 delta, 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 93% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in delta, theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and more than half of connections involving occipital electrodes, showed decreased connectivity with older age. Slightly less than half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age were not significantly different in 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 aging during a resting state.
(Copyright © 2019 IBRO. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: