ArticleBrain sciences2022
Age-Related Alterations in EEG Network Connectivity in Healthy Aging.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- Characteristics of electroencephalographic changes induced by different hypnotics in elderly patients: a narrative review.Korean journal of anesthesiology · 2026Review
- [Research progress in electroencephalogram-based brain age prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Neural transmission in the wired brain, new insights into an encoding-decoding-based neuronal communication model.Translational psychiatry · 2025Article
- Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling.NPJ Parkinson's disease · 2025Article
- Influence of individual's age on the characteristics of brain effective connectivity.GeroScience · 2025Article
- Network Segregation and Integration Changes in Healthy Aging: Evidence From EEG Subbands During the Visual Short-Term Memory Binding Task.The European journal of neuroscience · 2025Article
- Age-related differences in eye blink-related neural activity and functional connectivity during driving.Heliyon · 2025Article
- Structural inequality and temporal brain dynamics across diverse samples.Clinical and translational medicine · 2024Article
- Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.GeroScience · 2024Article
- Brain health in diverse settings: How age, demographics and cognition shape brain function.NeuroImage · 2024Article
- Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function.Human brain mapping · 2024Article
- Gershgorin circle theorem-based feature extraction for biomedical signal analysis.Frontiers in neuroinformatics · 2024Article
- Unique Brain Network Identification Number for Parkinson's and Healthy Individuals Using Structural MRI.Brain sciences · 2023Article
- Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features.Behavioral sciences (Basel, Switzerland) · 2023Article
- Hub structure in functional network of EEG signals supporting high cognitive functions in older individuals.Frontiers in aging neuroscience · 2023Article
- Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder.Frontiers in human neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.