Age-related changes of whole-brain dynamics in spontaneous neuronal coactivations

Guofa Shou, Han Yuan, Yoon Hee Cha, John A. Sweeney, Lei Ding

Research output: Contribution to journalArticlepeer-review

Abstract

Human brains experience whole-brain anatomic and functional changes throughout the lifespan. Age-related whole-brain network changes have been studied with functional magnetic resonance imaging (fMRI) to determine their low-frequency spatial and temporal characteristics. However, little is known about age-related changes in whole-brain fast dynamics at the scale of neuronal events. The present study investigated age-related whole-brain dynamics in resting-state electroencephalography (EEG) signals from 73 healthy participants from 6 to 65 years old via characterizing transient neuronal coactivations at a resolution of tens of milliseconds. These uncovered transient patterns suggest fluctuating brain states at different energy levels of global activations. Our results indicate that with increasing age, shorter lifetimes and more occurrences were observed in the brain states that show the global high activations and more consecutive visits to the global highest-activation brain state. There were also reduced transitional steps during consecutive visits to the global lowest-activation brain state. These age-related effects suggest reduced stability and increased fluctuations when visiting high-energy brain states and with a bias toward staying low-energy brain states. These age-related whole-brain dynamics changes are further supported by changes observed in classic alpha and beta power, suggesting its promising applications in examining the effect of normal healthy brain aging, brain development, and brain disease.

Original languageEnglish (US)
Article number12140
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This work was supported in part by NSF RII Track-2 FEC 1539068, NSF RII Track-4 2132182, and NIH NIGMS 1P20GM135009. The authors are grateful to Matthew W. Mosconi, Jun Wang, Lauren Ethridge, Diamond Gleghorn, Benjamin C. Doudican, Yafen Chen, Yuxuan Chen, Chuang Li, and Junwei Ma for assistance in data collection.

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article

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