Association between individual EEG characteristics and the level of intelligence

Stankova, E. P.; Myshkin, I. Yu.

doi:10.3103/s0096392516040118

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…This finding would have been overlooked and missed by the spectral analysis method. Stankova et al found that the degree of CD and intelligence level are positively correlated [17], and they concluded that the magnitude of CD is a measure of intelligence. Recorded EEG data suggested that participants with highly oscillating EEG have higher intelligence test scores than those with more periodic oscillations.…”

Section: Cognitionmentioning

confidence: 99%

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

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(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

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Section: Cognitionmentioning

confidence: 99%

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

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“…However, acrossstudy findings on relations between individual differences in intelligence and intrinsic brain signal complexity are heterogeneous. Higher intelligence has been associated with higher complexity (Jaušovec and Jaušovec, 2003;Stankova and Myshkin, 2016;Thatcher et al, 2005), Dreszer et al (2020) found both positive and negative associations, and other studies could not find any significant relation (Anokhin et al, 1999;Ueno et al, 2015). Sample characteristics (Dreszer et al, 2020), varying complexity measures (Ferenets et al, 2006;Goldberger et al, 2002), and differences in the considered type of neural complexity, i.e., complexity that has been linked to local (short-range) vs. global (long-range) neural processing (Courtiol et al, 2016;Dreszer et al, 2020;Vakorin et al, 2011) were proposed as contributing to this heterogeneity.…”

Section: Introductionmentioning

confidence: 98%

Multimodal Brain Signal Complexity Predicts Human Intelligence

Thiele

Richter

Hilger

2022

Preprint

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Spontaneous brain activity builds the foundation for human cognitive processing during external demands. A huge number of neuroimaging studies identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, respective research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) in regard to their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ~ .20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.Significance StatementSpontaneous brain activity builds the foundation for intelligent processing - the ability of humans to adapt to various cognitive demands. Using resting-state EEG, we extracted multiple aspects of temporally highly resolved intrinsic brain dynamics to investigate their relationship with individual differences in intelligence. Single associations were of small effect sizes and varied critically across spatial and temporal scales. However, combining multiple measures in a multimodal cross-validated prediction model, allows to significantly predict individual intelligence scores in unseen participants. Our study adds to a growing body of research suggesting that observable associations between complex human traits and neural parameters might be rather small and proposes multimodal prediction approaches as promising tool to derive robust brain-behavior relations despite limited sample sizes.

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Section: Introductionmentioning

confidence: 99%

Multimodal Brain Signal Complexity Predicts Human Intelligence

Thiele

Richter²,

Hilger³

2023

eNeuro

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Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r∼ .20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication,N= 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.Significance StatementSpontaneous brain activity builds the foundation for intelligent processing - the ability of humans to adapt to various cognitive demands. Using resting-state EEG, we extracted multiple aspects of temporally highly resolved intrinsic brain dynamics to investigate their relationship with individual differences in intelligence. Single associations were of small effect sizes and varied critically across spatial and temporal scales. However, combining multiple measures in a multimodal cross-validated prediction model, allows to significantly predict individual intelligence scores in unseen participants. Our study adds to a growing body of research suggesting that observable associations between complex human traits and neural parameters might be rather small and proposes multimodal prediction approaches as promising tool to derive robust brain-behavior relations despite limited sample sizes.

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Association between individual EEG characteristics and the level of intelligence

Cited by 9 publications

References 11 publications

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Multimodal Brain Signal Complexity Predicts Human Intelligence

Multimodal Brain Signal Complexity Predicts Human Intelligence

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