Charting moment-to-moment brain signal variability from early to late childhood

Miskovic, Vladimir; Owens, Max; Kuntzelman, Karl; Gibb, Brandon E.

doi:10.1016/j.cortex.2016.07.006

Cited by 26 publications

(27 citation statements)

References 82 publications

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“…Developmental EEG studies have shown a spectral power shift to higher frequencies, decreasing theta/alpha ratio and increasing alpha peak frequency by age (Benninger et al, 1984;Clarke et al, 2001;Gasser et al, 1988;Miskovic et al, 2015). There are also developmental changes of the brain networks indexed by strengthening long-range alpha connectivity (Miskovic et al, 2015;Knyazev et al, 2017) and increasing EEG variability with age, proceeding from the posterior to anterior areas of the brain (Miskovic et al, 2016). Our and stimulus-driven ventral attention network (VAN) are activated during inhibitory and attentional tasks (Stevens et al, 2007;see also reviews by Corbetta et al, 2008;Parks and Madden, 2013;Vogel et al 2010).…”

Section: Discussionmentioning

confidence: 99%

Decreased occipital alpha oscillation in children who stutter during a visual Go/Nogo task

Piispala

Starck

Jansson-Verkasalo

et al. 2018

Clinical Neurophysiology

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Section: Discussionmentioning

confidence: 99%

Decreased occipital alpha oscillation in children who stutter during a visual Go/Nogo task

Piispala

Starck

Jansson-Verkasalo

et al. 2018

Clinical Neurophysiology

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“…The technology to measure brain entropy has advanced over the last five years resulting in a growing literature using a variety of brain imaging techniques applied to understand normal and abnormal brain function [ 16 – 18 ]. Brain entropy can be measured from fMRI data, which consists of a series of successive MRI brain images taken over time.…”

Section: Introductionmentioning

confidence: 99%

Brain entropy and human intelligence: A resting-state fMRI study

2018

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Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

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“…Kaur et al, 2019;Miskovic et al, 2016;Mizuno et al, 2010). We hope that our 863practical example of coarse-scale biases thus highlights the dangers of resulting mismatches 864and motivate the adoption of scale-varying parameters.…”

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confidence: 76%

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?

Kosciessa

Kloosterman

Garrett³

2019

Preprint

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AbstractMultiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its’ presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via “similarity bounds” biases coarse MSE scales – that are thought to reflect slow dynamics – by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales – presumed to indicate fast dynamics – is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.Author SummaryBrain signals exhibit a wealth of dynamic patterns that that are thought to reflect ongoing neural computations. Multiscale sample entropy (MSE) intends to describe the temporal irregularity of such patterns at multiple time scales of brain function. However, the notion of time scales may often be unintuitive. In particular, traditional implementations of MSE are sensitive to slow fluctuations at fine time scales, and fast dynamics at coarse time scales. This conceptual divergence is often overlooked and may lead to difficulties in establishing the unique contribution of MSE to effects of interest over more established spectral power. Using simulations and empirical data, we highlight these issues and provide evidence for their relevance for valid practical inferences. We further highlight that standard MSE and traditional spectral power are highly collinear in our example. Finally, our analyses indicate that spectral filtering can be used to estimate temporal signal irregularity at matching and intuitive time scales. To guide future studies, we make multiple recommendations based on our observations. We believe that following these suggestions may advance our understanding of the unique contributions of neural signal irregularity to neural and cognitive function across the lifespan.

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Charting moment-to-moment brain signal variability from early to late childhood

Cited by 26 publications

References 82 publications

Decreased occipital alpha oscillation in children who stutter during a visual Go/Nogo task

Decreased occipital alpha oscillation in children who stutter during a visual Go/Nogo task

Brain entropy and human intelligence: A resting-state fMRI study

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?

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