Brain Network Analysis Reveals Age-Related Differences in Topological Reorganization During Vigilance Decline

To mitigate the economic losses and safety risks caused by reduced alertness of individuals in the context of an aging workforce, mental fatigue among the elderly is an issue worthy of in-depth exploration. Despite convergent studies on cognitive aging, the differential alterations in brain network topology between the elderly and young individuals during vigilance decline remain unclear. Here, a prolonged 30-min psychomotor vigilance task (PVT) was employed to induce mental fatigue, where both behavioral performance and electroencephalography (EEG) data were collected from healthy elderly (n =30) and young participants (n =40). Subsequently, EEG functional connectivity was constructed and the differences in network topological properties between the two groups were quantitatively evaluated based on global and nodal metrics. Both groups an exhibited age-independent significant decline in behavioral performance with time on task. Moreover, age-related dysconnectivity pattern was revealed over a wide frequency range ( $1-45$ Hz) in the elderly group, which further developed toward less optimal network architecture. Specifically, significant deficits in nodal efficiency were revealed in most of the brain regions, and the frontal area exhibited significant age-by-time interaction effect, which was attributed to a significant decline in the elderly group. Statistically significant correlation between behavioral and network metrics was also found. Overall, our results provide some of the first quantitative insights for revealing the neural mechanisms of age differences during mental fatigue, which may contribute to the rational arrangement of personnel in real-world scenarios with high alertness demands.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tnsre.2025.3598197
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Brain Network Analysis Reveals Age-Related Differences in Topological Reorganization During Vigilance Decline

Yi Sun, Yu Sun, Lingyun Gao, Mengru Xu et al.
1 citations
IEEE Transactions on Neural Systems and Rehabilitation Engineering
EEG and Brain-Computer Interfaces
1.43
article

Brain Network Analysis Reveals Age-Related Differences in Topological Reorganization During Vigilance Decline

Yi Sun, Yu Sun, Lingyun Gao, Mengru Xu, Rui Zhang, Gang Li
article en
1 citations

Abstract

To mitigate the economic losses and safety risks caused by reduced alertness of individuals in the context of an aging workforce, mental fatigue among the elderly is an issue worthy of in-depth exploration. Despite convergent studies on cognitive aging, the differential alterations in brain network topology between the elderly and young individuals during vigilance decline remain unclear. Here, a prolonged 30-min psychomotor vigilance task (PVT) was employed to induce mental fatigue, where both behavioral performance and electroencephalography (EEG) data were collected from healthy elderly (n =30) and young participants (n =40). Subsequently, EEG functional connectivity was constructed and the differences in network topological properties between the two groups were quantitatively evaluated based on global and nodal metrics. Both groups an exhibited age-independent significant decline in behavioral performance with time on task. Moreover, age-related dysconnectivity pattern was revealed over a wide frequency range ( $1-45$ Hz) in the elderly group, which further developed toward less optimal network architecture. Specifically, significant deficits in nodal efficiency were revealed in most of the brain regions, and the frontal area exhibited significant age-by-time interaction effect, which was attributed to a significant decline in the elderly group. Statistically significant correlation between behavioral and network metrics was also found. Overall, our results provide some of the first quantitative insights for revealing the neural mechanisms of age differences during mental fatigue, which may contribute to the rational arrangement of personnel in real-world scenarios with high alertness demands.

IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol. 33
Zhejiang Normal University (CN), Sir Run Run Shaw Hospital (CN), Zhengzhou University (CN), Zhejiang Lab (CN), Zhejiang University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Decent work and economic growth
Openalex Percentile: Top 17%
EEG and Brain-Computer Interfaces
1.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.