Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities

Lopez, Kelsie; Monachino, A.D.; Vincent, Katherine M.; Peck, Fleming C.; Gabard-Durnam, Laurel

doi:10.1016/j.neuroimage.2023.120116

Cited by 13 publications

(9 citation statements)

References 232 publications

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“…3 ). These results are consistent with previous results reported by other authors where high reliability of EEG markers has been demonstrated 25 , 26 , 29 , 30 . The correlation r = 0.98 for alpha power over three years in the current study is consistent with the Pearson correlation r = 0.848 of alpha over 12 years 29 .…”

Section: Discussionsupporting

confidence: 94%

“…The stability of EEG theta, alpha, and beta bands’ power over two months has been reported higher than delta band power 25 , 26 . The temporal stability of EEG power markers has been demonstrated from minutes to twelve years 26 , 28 – 30 . Only a few data are available for the stability of non-linear markers 30 .…”

Section: Introductionmentioning

confidence: 99%

“…The temporal stability of EEG power markers has been demonstrated from minutes to twelve years 26 , 28 – 30 . Only a few data are available for the stability of non-linear markers 30 .…”

Section: Introductionmentioning

confidence: 99%

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Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Põld,

Päeske,

Hinrikus

et al. 2023

Sci Rep

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Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires’ scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.

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Section: Discussionsupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Põld,

Päeske,

Hinrikus

et al. 2023

Sci Rep

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“…This is consistent with recent work from Dede et al, 2023 that failed to find a single biomarker for ASD after extracting a high number of measures from resting-state EEG (in sensor space, including band-power and frequency measures) in a dataset of 776 individuals. There is large inter-individual variation in alpha-band rhythm features, but high internal consistency (Lopez et al, 2023), which hints at the fact that usage of longitudinal data (Gabard-Durnam et al, 2019) may be a promising approach to capture diagnosis-related information sufficiently. Our analysis suggests that it is challenging to use single-session resting-state oscillations for the construction of predictive biomarkers, but does not rule out that a more specific task-operationalization of deficits may be used to uncover disorder-specific physiology, e.g.…”

Section: Discussionmentioning

confidence: 99%

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Bender,

Voytek,

Schaworonkow

2023

Preprint

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In the human brain, the alpha rhythm in occipital cortex and the mu rhythm in sensorimotor cortex are among the most prominent rhythms, with both rhythms functionally implicated in gating modality-specific information. Separation of these rhythms is non-trivial due to the spatial mixing of these oscillations in sensor space. Using a computationally efficient processing pipeline, we show that alpha and mu rhythms can be separated from electroencephalography recordings performed on nearly 3000 children aged 5-18. Using the extracted time series for each rhythm, we characterized the waveform shape on a cycle-by-cycle basis and examined whether and how the waveform shape differs across development and in attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Alpha and mu rhythms both exhibit nonsinusoidal waveform shape that changes significantly across development. We found no differences in the resting-state features of these alpha-band rhythms for either ADHD or ASD in comparison to typically developing participants. While waveform shape is ignored by traditional Fourier spectral analyses, these nonsinusoidal properties may be informative for building more constrained generative models for different types of alpha-band rhythms. Our results highlight the difficulties in identifying targeted biomarkers for ADHD and ASD from resting-state alpha-band rhythms.

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“…More speci cally, of the six publications that we identi ed, four used the microstate analytic approach to examine event-related data (Bucsea et al, 2023;Gui et al, 2021;Maitre et al, 2020;Rupawala et al, 2023), one examined microstates during sleep (Khazaei et al, 2021), and one used microstate analysis to examine spontaneous EEG data collected from infants (i.e., 34, 6-10-month-olds) during video-watching (Brown & Gartstein, 2023). Unfortunately, none of this prior work investigated the reliability of microstate-related measures at this age, a critical step in understanding the potential use of microstates to study individual differences in behavior and development (Lopez et al, 2023). However, previous work has demonstrated the reliability of microstate analysis in adults and suggests it is likely present in younger age groups as well.…”

Section: Introductionmentioning

confidence: 99%

Microstate Analysis of Infant EEG: Tutorial and Reliability

Bagdasarov

Brunet

Michel

et al. 2023

Preprint

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Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy – a critical period of rapid brain development and brain plasticity – microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that all microstate-based measurements of brain organization and functioning except for transition probabilities were highly stable and reliable with as little as 2–3 minutes of video-watching resting-state data and provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

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Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities

Cited by 13 publications

References 232 publications

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Microstate Analysis of Infant EEG: Tutorial and Reliability

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