Refined Multiscale Entropy Using Fuzzy Metrics: Validation and Application to Nociception Assessment

Valencia, J.; Bolaños, José; Vallverdú, Montserrat; Jensen, Erik Weber; Porta, Alberto; Gambús, Pedro L.

doi:10.3390/e21070706

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Remarkably, transformations increasing pattern similarity could be applied not only to fuzzy entropy [ 29 , 30 , 31 ] but also to refined fuzzy entropy [ 22 ] and distribution entropy [ 42 ] and, more generally, to any CE metric based on the assessment of the distance between current and reference patterns [ 8 , 9 , 10 , 11 , 43 ]. These transformations could be exploited in multiscale [ 44 , 45 ] and multivariate [ 46 , 47 ] analyses as well.…”

Section: Discussionmentioning

confidence: 99%

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

Porta

Valencia

Cairo

et al. 2020

Entropy

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It has been suggested that a viable strategy to improve complexity estimation based on the assessment of pattern similarity is to increase the pattern matching rate without enlarging the series length. We tested this hypothesis over short simulations of nonlinear deterministic and linear stochastic dynamics affected by various noise amounts. Several transformations featuring a different ability to increase the pattern matching rate were tested and compared to the usual strategy adopted in sample entropy (SampEn) computation. The approaches were applied to evaluate the complexity of short-term cardiac and vascular controls from the beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP) in 12 Parkinson disease patients and 12 age- and gender-matched healthy subjects at supine resting and during head-up tilt. Over simulations, the strategies estimated a larger complexity over nonlinear deterministic signals and a greater regularity over linear stochastic series or deterministic dynamics importantly contaminated by noise. Over short HP and SAP series the techniques did not produce any practical advantage, with an unvaried ability to discriminate groups and experimental conditions compared to the traditional SampEn. Procedures designed to artificially increase the number of matches are of no methodological and practical value when applied to assess complexity indexes.

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Section: Discussionmentioning

confidence: 99%

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

Porta

Valencia

Cairo

et al. 2020

Entropy

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“…Then, sample entropy is determined for each coarse-grained signal (scale). Sample entropy is computed similar to Fuzzy entropy (see above), with the differences that no baseline correction of the template vectors is performed before calculating the Chebyshev distances between them and that the similarity between two template vectors of lengths l = m (or l = m + 1 ) is determined in respect to the tolerance r (here r = 0.2 times the standard deviation of the signal) by: For mathematical insights and more detailed descriptions see Richman and Moorman (2000), and Valencia et al (2019).…”

Section: Methodsmentioning

confidence: 99%

“…For mathematical insights and more detailed descriptions see Richman and Moorman (2000), and Valencia et al (2019).…”

Section: Methodsmentioning

confidence: 99%

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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“…For mathematical insights and more detailed descriptions see Richman and Moorman (2000), and Valencia et al (2019).…”

Section: Intrinsic Brain Signal Complexitymentioning

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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Refined Multiscale Entropy Using Fuzzy Metrics: Validation and Application to Nociception Assessment

Cited by 5 publications

References 26 publications

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

Multimodal Brain Signal Complexity Predicts Human Intelligence

Multimodal Brain Signal Complexity Predicts Human Intelligence

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