Wide Range Multiscale Entropy Changes through Development

Polizzotto, Nicola Riccardo; Takahashi, Tetsuya; Walker, Caroline; Cho, Raymond Y.

doi:10.3390/e18010012

Cited by 11 publications

(10 citation statements)

References 38 publications

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“…An absence of longitudinal data, or an adult cohort to examine whether signal complexity continues to change into adulthood, limits a more comprehensive assessment of maturational changes as they relate to EEG complexity. In addition, integrating measures of variability, irregularity, and spectral power will contribute to a deeper understanding of how EEG complexity at multiple temporal scales changes across development ( Courtiol et al, 2016 ; Kosciessa et al, 2020 ; Polizzotto et al, 2016 ). Future studies that longitudinally assess intra-individual complexity in resting and task-based EEG, coupled with comprehensive behavioural and neurocognitive assessment, will be especially valuable to our understanding about the functional significance of developmental changes in brain signal complexity.…”

Section: Limitations Conclusion Future Directionsmentioning

confidence: 99%

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

Noordt

Willoughby

2021

Developmental Cognitive Neuroscience

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Endogenous cortical fluctuations captured by electroencephalograms (EEGs) reflect activity in large-scale brain networks that exhibit dynamic patterns over multiple time scales. Developmental changes in the coordination and integration of brain function leads to greater complexity in population level neural dynamics. In this study we examined multiscale entropy, a measure of signal complexity, in resting-state EEGs in a large (N = 405) cross-sectional sample of children and adolescents (9–16 years). Our findings showed consistent age-dependent increases in EEG complexity that are distributed across multiple temporal scales and spatial regions. Developmental changes were most robust as the age gap between groups increased, particularly between late childhood and adolescence, and were most prominent over fronto-central scalp regions. These results suggest that the transition from late childhood to adolescence is characterized by age-dependent changes in the underlying complexity of endogenous brain networks.

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Section: Limitations Conclusion Future Directionsmentioning

confidence: 99%

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

Noordt

Willoughby

2021

Developmental Cognitive Neuroscience

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“…Neural variability measured via electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) shows strong links to both cognitive performance and cognitive development. Specifically, increased variability is associated with more accurate and stable perceptual, cognitive, and behavioral performance [6][7][8][9][10][11], and variability increases with development from infancy into adulthood [7][8][9][10][11][12][13][14][15][16][17][18]. These associations may occur because the addition of a moderate amount of random noise enhances and more accurately represents an underlying signal [2].…”

Section: Introductionmentioning

confidence: 99%

“…Both SD [ 11 , 54 – 56 ] and MSE [ 7 , 8 , 14 , 15 , 17 , 57 ] have been positively associated with developmental and behavioral outcomes. However, no study to date has directly compared the explanatory power of these two variability measures, nor considered a role for oxytocinergic system function as an underlying molecular mechanism capable of contributing to brain signal variability during social perception in humans.…”

Section: Introductionmentioning

confidence: 99%

Epigenetic tuning of brain signal entropy in emergent human social behavior

Puglia

Krol

Missana

et al. 2020

BMC Med

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Background How the brain develops accurate models of the external world and generates appropriate behavioral responses is a vital question of widespread multidisciplinary interest. It is increasingly understood that brain signal variability—posited to enhance perception, facilitate flexible cognitive representations, and improve behavioral outcomes—plays an important role in neural and cognitive development. The ability to perceive, interpret, and respond to complex and dynamic social information is particularly critical for the development of adaptive learning and behavior. Social perception relies on oxytocin-regulated neural networks that emerge early in development. Methods We tested the hypothesis that individual differences in the endogenous oxytocinergic system early in life may influence social behavioral outcomes by regulating variability in brain signaling during social perception. In study 1, 55 infants provided a saliva sample at 5 months of age for analysis of individual differences in the oxytocinergic system and underwent electroencephalography (EEG) while listening to human vocalizations at 8 months of age for the assessment of brain signal variability. Infant behavior was assessed via parental report. In study 2, 60 infants provided a saliva sample and underwent EEG while viewing faces and objects and listening to human speech and water sounds at 4 months of age. Infant behavior was assessed via parental report and eye tracking. Results We show in two independent infant samples that increased brain signal entropy during social perception is in part explained by an epigenetic modification to the oxytocin receptor gene (OXTR) and accounts for significant individual differences in social behavior in the first year of life. These results are measure-, context-, and modality-specific: entropy, not standard deviation, links OXTR methylation and infant behavior; entropy evoked during social perception specifically explains social behavior only; and only entropy evoked during social auditory perception predicts infant vocalization behavior. Conclusions Demonstrating these associations in infancy is critical for elucidating the neurobiological mechanisms accounting for individual differences in cognition and behavior relevant to neurodevelopmental disorders. Our results suggest that an epigenetic modification to the oxytocin receptor gene and brain signal entropy are useful indicators of social development and may hold potential diagnostic, therapeutic, and prognostic value.

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“…While many studies demonstrate change in MSE across development (Bosl et al, 2011;De Wel et al, 2017;Hasegawa et al, 2018;Kang et al, 2019;Lippé et al, 2009;McIntosh et al, 2008;Miskovic et al, 2016;Polizzotto et al, 2016;Szostakiwskyj et al, 2017;Zhang et al, 2009) or with developmental disorder (Begum et al, 2017;Chenxi et al, 2016;Eroğlu et al, 2020;Kang et al, 2018;Liu et al, 2017;Okazaki et al, 2015;Rezaeezadeh et al, 2020;Simon et al, 2017;Wadhera and Kakkar, 2020;Weng et al, 2017), the vastly differing preprocessing choices and widespread failure to adopt the critical MSE algorithm modification of scale-wise recalculation of the similarity criterion makes it challenging to compare results across studies and realize the role of entropy in neurodevelopment.…”

Section: Mse Across Development: An Overview Of Prior Pediatric Eeg Researchmentioning

confidence: 99%

The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

Puglia

Slobin

Williams

2021

Preprint

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It is increasingly understood that moment-to-moment brain signal variability – traditionally modeled out of analyses as mere “noise” – serves a valuable function role and captures properties of brain function related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) – a measure of signal irregularity across temporal scales – is an increasingly popular analytic technique in human neuroscience. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain’s moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in data preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized EEG preprocessing and entropy estimation pipeline that generates scale-wise entropy estimates that are reliable and capable of differentiating developmental stages and cognitive states. This novel pipeline – the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED. The dataset used herein to develop and validate the pipeline is available for download from https://openneuro.org/datasets/ds003710.

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Wide Range Multiscale Entropy Changes through Development

Cited by 11 publications

References 38 publications

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

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

Epigenetic tuning of brain signal entropy in emergent human social behavior

The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

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