Power shift and connectivity changes in healthy aging during resting-state EEG

Perinelli, Alessio; Assecondi, Sara; Tagliabue, Chiara F.; Mazza, Verônica de Azevedo

doi:10.1016/j.neuroimage.2022.119247

Cited by 24 publications

(19 citation statements)

References 110 publications

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“…The study reported no significant correlation of activity in gamma band within the occipital cortex with age. An EEG study on healthy aging (Perinelli et al, 2022) showed increase in gamma activity at the frontoparietal and left temporal areas. However, most of the studies in the literature on gamma are confined to the resting state condition, which is considered to have various shortcomings attributed to experimental settings, mental state of the subject, etc.…”

Section: Discussionmentioning

confidence: 99%

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Stimulus-induced gamma sources weaken but not shrink with healthy aging in human EEG

Kumar,

Sharma,

Ray

2024

Preprint

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Aging alters brain structure and function, and studying such changes may help understand the neural basis underlying aging and devise interventions to detect deviations from healthy progression. Electroencephalogram (EEG) offers an effective way to study healthy aging owing to its high temporal resolution and affordability. Recent studies have shown that narrow-band stimulus-induced gamma oscillations (20-70 Hz) in EEG weaken with healthy aging and onset of Alzheimer’s Disease while remaining highly reproducible for a given subject, thus hold promise as potential biomarkers. However, functional connectivity (FC) sometimes changes in a different way compared to sensor power with aging. This difference could be potentially addressed by studying how underlying gamma sources change with aging, since either a reduction in source power or a shrinkage of the sources (or both) could reduce the power in the sensors but may have different effects on other measures such as FC. We therefore reconstructed EEG gamma sources through a linear inverse method called eLORETA on a large (N=217) cohort of healthy elderly subjects (>50 years). We further characterized gamma distribution in cortical space as an exponential fall off from a seed voxel with maximal gamma source power, which could help delineate a reduction in magnitude versus shrinkage. We found significant reduction in magnitude but not shrinkage with healthy aging. Overall, our results shed light on changes in EEG gamma source distribution with healthy aging which could provide clues about underlying neural mechanisms.

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Section: Discussionmentioning

confidence: 99%

Section: Previous Studies On Changes In Source Gamma Power With Agementioning

confidence: 99%

Stimulus-induced gamma sources weaken but not shrink with healthy aging in human EEG

Kumar,

Sharma,

Ray

2024

Preprint

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“…The related study protocol was approved by the ethics committee at the medical faculty of the University of Leipzig, Germany (reference number 154/13-ff). Details of the set of subjects, the recording procedure, the preprocessing steps, and source reconstruction, are extensively described in two recent papers [ 61 , 62 ].…”

Section: Estimation Of Pe Variability On Experimental Time Seriesmentioning

confidence: 99%

Estimating Permutation Entropy Variability via Surrogate Time Series

Ricci

Perinelli

2022

Entropy

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In the last decade permutation entropy (PE) has become a popular tool to analyze the degree of randomness within a time series. In typical applications, changes in the dynamics of a source are inferred by observing changes of PE computed on different time series generated by that source. However, most works neglect the crucial question related to the statistical significance of these changes. The main reason probably lies in the difficulty of assessing, out of a single time series, not only the PE value, but also its uncertainty. In this paper we propose a method to overcome this issue by using generation of surrogate time series. The analysis conducted on both synthetic and experimental time series shows the reliability of the approach, which can be promptly implemented by means of widely available numerical tools. The method is computationally affordable for a broad range of users.

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“…These studies have been seminal in developing a deeper understanding of M/EEG measures widely applied in clinical and cognitive neurosciences. A common and early observation is that low-frequency power decreases and high-frequency power increases linearly with age [6][7][8][9][10] , with the most pronounced changes in frontal and temporal areas of the brain [2][3][4]11 . Studies on the brain's functional connectome are less frequent and consistent partly due to the various connectivity measures used, relying on either amplitude or phase information of the signals.…”

Section: Introductionmentioning

confidence: 99%

Extensive MEG time-series phenotyping unveils neural markers predictive of age

Stier,

Balestrieri,

Fehring

et al. 2024

Preprint

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Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography in 350 adults (18-88 years). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred novel time-series features proved superior. Notably, temporal autocorrelation emerged as the most robust predictor of age. Distinct patterns of autocorrelation within the visual and temporal cortex were most informative, offering a versatile measure of age-related signal changes for comprehensive health assessments based on brain activity.

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Power shift and connectivity changes in healthy aging during resting-state EEG

Cited by 24 publications

References 110 publications

Stimulus-induced gamma sources weaken but not shrink with healthy aging in human EEG

Stimulus-induced gamma sources weaken but not shrink with healthy aging in human EEG

Estimating Permutation Entropy Variability via Surrogate Time Series

Extensive MEG time-series phenotyping unveils neural markers predictive of age

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