ArticleFrontiers in neuroscience2023
Tracking functional network connectivity dynamics in the elderly.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Study on dynamic functional connectivity and efficacy prediction of SAINT for rapid antidepressant effects and relief of suicidal ideation.Translational psychiatry · 2025Article
- Criticality and increased intrinsic neural timescales in stroke.NPJ systems biology and applications · 2025Article
- Mapping and modeling age-related changes in intrinsic neural timescales.Communications biology · 2025Article
- Effects of an 18-month meditation training on dynamic functional connectivity states in older adults: Secondary analyses from the Age-Well randomized controlled trial.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Dendrites contribute to the gradient of intrinsic timescales encompassing cortical and subcortical brain networks.Frontiers in cellular neuroscience · 2024Article
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Authors and funding
5 authors.
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Abstract
Introduction: Functional magnetic resonance imaging (fMRI) has shown that aging disturbs healthy brain organization and functional connectivity. However, how this age-induced alteration impacts dynamic brain function interaction has not yet been fully investigated. Dynamic function network connectivity (DFNC) analysis can produce a brain representation based on the time-varying network connectivity changes, which can be further used to study the brain aging mechanism for people at different age stages. Method: This presented investigation examined the dynamic functional connectivity representation and its relationship with brain age for people at an elderly stage as well as in early adulthood. Specifically, the resting-state fMRI data from the University of North Carolina cohort of 34 young adults and 28 elderly participants were fed into a DFNC analysis pipeline. This DFNC pipeline forms an integrated dynamic functional connectivity (FC) analysis framework, which consists of brain functional network parcellation, dynamic FC feature extraction, and FC dynamics examination. Results: The statistical analysis demonstrates that extensive dynamic connection changes in the elderly concerning the transient brain state and the method of functional interaction in the brain. In addition, various machine learning algorithms have been developed to verify the ability of dynamic FC features to distinguish the age stage. The fraction time of DFNC states has the highest performance, which can achieve a classification accuracy of over 88% by a decision tree. Discussion: The results proved there are dynamic FC alterations in the elderly, and the alteration was found to be correlated with mnemonic discrimination ability and could have an impact on the balance of functional integration and segregation.
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