Cortical maturation from childhood to adolescence is reflected in resting state EEG signal complexity

Noordt, Stefon van; Willoughby, Teena

doi:10.1016/j.dcn.2021.100945

Cited by 27 publications

(21 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the most significant differences were found in frontal channels ( Figure 5 ). A similar result was also reported by Van Noordt et al [ 74 ], showing a main increase in EEG complexity located in fronto-central region. They suggest that the increase observed may reflect a maturational change in underlying brain networks.…”

Section: Discussionsupporting

confidence: 91%

“…Their results are consistent with the hypothesis that AD is characterised by a reduction of complexity [ 73 ], which has been suggested to reflect a decrease in non-linear neural dynamics caused by a loss of connectivity of local neural networks and/or neuronal death [ 12 ]. Moreover, MSE analysis has been applied to explore the changes in EEG complexity throughout aging, showing a prominent increase in EEG complexity from childhood to adolescence [ 74 ], and a lower EEG complexity in elderly subjects compared to young ones [ 50 ]. Thus, in view of the likely association between a reduced EEG complexity and the cognitive decline process, the increase observed in our study post-MI–NFT may lead to a possible alleviation of the effects of age-related cognitive decline.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Marcos-Martínez

Martínez-Cagigal

Santamaría-Vázquez

et al. 2021

Entropy

View full text Add to dashboard Cite

Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI–NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject’s scores from Luria tests performed before and after MI–NFT. We found that MI–NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI–NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI–NFT.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Marcos-Martínez

Martínez-Cagigal

Santamaría-Vázquez

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Several studies found a general increase in brain signal complexity with age in neurotypical populations. Indeed, studies have found neurodevelopmental effects on signal complexity from infancy through adolescence ( 34 , 51 , 52 ). This developmental increase was clearly observed in our control group across all regions of interest.…”

Section: Discussionmentioning

confidence: 99%

EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome

et al. 2021

View full text Add to dashboard Cite

Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied.Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5–28 years), and 7 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site.Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS.Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.

show abstract

“…McIntosh et al, 2014;A. R. McIntosh et al, 2008;Noordt and Willoughby, 2021) HFD (Smits et al, 2016;Zappasodi et al, 2015) Old age Decrease with normal aging MSE (A. McIntosh et al, 2014) HFD (Smits et al, 2016;Zappasodi et al, 2015) Increase with normal aging SampEn (Hogan et al, 2012) CD (Anokhin et al, 1996) Table 5 List of EEG complexity measures used in consciousness research.…”

Section: Discussionmentioning

confidence: 99%

Brain Entropy, Fractal Dimensions and Predictability: A Review of Complexity Measures for EEG in Healthy and Neuropsychiatric Populations

Lau¹,

Pham²,

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

While electroencephalography (EEG) signals are commonly examined using conventional linear methods, there has been an increasing trend towards the use of complexity analysis in quantifying neural activity. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of (1) predictability and (2) regularity. We then synthesize complexity findings across different areas of psychological science, namely in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

show abstract

Cortical maturation from childhood to adolescence is reflected in resting state EEG signal complexity

Cited by 27 publications

References 66 publications

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome

Brain Entropy, Fractal Dimensions and Predictability: A Review of Complexity Measures for EEG in Healthy and Neuropsychiatric Populations

Contact Info

Product

Resources

About