Brain oscillatory complexity across the life span

Fernández, Alberto; Zuluaga, Pilar; Abásolo, Daniel; Gómez, Carlos; Serra, Anahi; Méndez, María Andreina; Hornero, Roberto

doi:10.1016/j.clinph.2012.04.025

Cited by 48 publications

(78 citation statements)

References 66 publications

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“…Furthermore, the results in Figures 2 and 3 show that the anterior and central regions of the brain generally have higher nPE and nmPE values when compared to other regions of the brain. This was observed for all age groups, and is a result that is in accordance with that obtained by [4]. In the context of the characterisation of healthy ageing, this result suggests that these two brain regions have higher resting state activity than the other three brain regions, and thus have higher entropy than the other brain regions.…”

Section: Evaluation Of Differences Between Npe and Nmpesupporting

confidence: 79%

“…Various studies analysing healthy males and females [1][2][3][4][61][62][63][64][65] have successfully identified gender as an independent distinguishing characteristic between the gender groups. These studies have shown that significant differences exist between the genders from childhood, to adolescence and adulthood, i.e., throughout life.…”

Section: Gender Effectsmentioning

confidence: 99%

“…Statistical analyses were performed with 148 nPE and nmPE scores per subject. The 148 nPE and nmPE values were averaged into five regions similar to work done by Fernández et al [4] and Méndez et al [46]: anterior (a), central (c), left lateral (ll), right lateral (rl) and posterior (p). Figure 1 illustrates the location of the sensors in the neuroimaging device as well as the brain regions to which they are nearest.…”

Section: Meg Data Reduction and Analysismentioning

confidence: 99%

“…Different studies have shown however that gender also plays an important role in the development of the brain throughout life [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Shumbayawonda

Fernández

Hughes

et al. 2017

Entropy

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Abstract:The characterisation of healthy ageing of the brain could help create a fingerprint of normal ageing that might assist in the early diagnosis of neurodegenerative conditions. This study examined changes in resting state magnetoencephalogram (MEG) permutation entropy due to age and gender in a sample of 220 healthy participants (98 males and 122 females, ages ranging between 7 and 84). Entropy was quantified using normalised permutation entropy and modified permutation entropy, with an embedding dimension of 5 and a lag of 1 as the input parameters for both algorithms. Effects of age were observed over the five regions of the brain, i.e., anterior, central, posterior, and left and right lateral, with the anterior and central regions containing the highest permutation entropy. Statistically significant differences due to age were observed in the different brain regions for both genders, with the evolutions described using the fitting of polynomial regressions. Nevertheless, no significant differences between the genders were observed across all ages. These results suggest that the evolution of entropy in the background brain activity, quantified with permutation entropy algorithms, might be considered an alternative illustration of a 'nominal' physiological rhythm.

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Section: Evaluation Of Differences Between Npe and Nmpesupporting

confidence: 79%

Section: Gender Effectsmentioning

confidence: 99%

Section: Meg Data Reduction and Analysismentioning

confidence: 99%

“…Different studies have shown however that gender also plays an important role in the development of the brain throughout life [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Shumbayawonda

Fernández

Hughes

et al. 2017

Entropy

Self Cite

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“…Finally, three illustrative examples show how these tools can be applied to psychology. Keywords Algorithmic complexity · Randomness · Subjective probability · Coding theorem method Randomness and complexity are two concepts which are intimately related and are both central to numerous recent developments in various fields, including finance (Taufemback et al, 2011, Brandouy et al, 2012, linguistics (Gruber, 2010;Naranan, 2011), neuropsychology (Machado et al, 2010;Fernández et al, 2011Fernández et al, , 2012, psychiatry (Yang and Tsai, 2012;Takahashi 2013), genetics (Yagil, 2009;Ryabko et al 2013), sociology (Elzinga, 2010 and the behavioral sciences (Watanabe et al, 2003;Scafetta et al, 2009). In psychology, randomness and complexity have recently attracted interest, following the realization that they could shed light on a diversity of previously undeciphered behaviors and mental processes.…”

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confidence: 99%

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

et al. 2015

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Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed coding theorem method the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology. Keywords Algorithmic complexity · Randomness · Subjective probability · Coding theorem method Randomness and complexity are two concepts which are intimately related and are both central to numerous recent developments in various fields, including finance (Taufemback et al., 2011, Brandouy et al., 2012, linguistics (Gruber, 2010;Naranan, 2011), neuropsychology (Machado et al., 2010;Fernández et al., 2011Fernández et al., , 2012, psychiatry (Yang and Tsai, 2012;Takahashi 2013), genetics (Yagil, 2009;Ryabko et al. 2013), sociology (Elzinga, 2010 and the behavioral sciences (Watanabe et al., 2003;Scafetta et al., 2009). In psychology, randomness and complexity have recently attracted interest, following the realization that they could shed light on a diversity of previously undeciphered behaviors and mental processes. It has been found, for instance, that the subjective difficulty of a concept is directly related to its "boolean complexity", defined as the shortest logical description of a concept (Feldman, 2000(Feldman, , 2003(Feldman, , 2006. In the same vein, visual detection of shapes has been shown to be related to contour complexity (Wilder et al., 2011).More generally, perceptual organization itself has been described as based on simplicity or, equivalently, likelihood (Chater, 1996;Chater and Vitányi, 2003), in a model reconciling the complexity approach (perception is organized to minimize complexity) and a probability approach (perception is organized to maximize likelihood), very much in line with our view in this paper. Even the perception of similarity may be viewed through the lens of (conditional) complexity (Hahn et al., 2003).Randomness and complexity also play an important role in modern approaches to selecting the "best" among a set of candidate models (i.e., model selection; e.g., Myung et Behav Res (2016) 48:314-329 315 al., 2006Kellen et al., 2013), as discussed in more detail below in the section called "Relationship to complexity based model selection".Complexity can also shed light on short term memory storage and recall, more specifically, on the process underlying chunking. It is well known that the short term memory span lies b...

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Neuronal dynamics enable the functional differentiation of resting state networks in the human brain

Marino

Liu

Samogin

et al. 2018

Human Brain Mapping

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Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals.Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders. K E Y W O R D S electroencephalography, fractal dimension, neuronal oscillations, resting state networks

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Brain oscillatory complexity across the life span

Cited by 48 publications

References 66 publications

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

Neuronal dynamics enable the functional differentiation of resting state networks in the human brain

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