The reliability and psychometric structure of Multi-Scale Entropy measured from EEG signals at rest and during face and object recognition tasks

Kaur, Yadwinder; Ouyang, Guang; Junge, Martin; Sommer, Werner; Liu, Mianxin; Zhou, Changsong; Hildebrandt, Andrea

doi:10.1016/j.jneumeth.2019.108343

Cited by 16 publications

(20 citation statements)

References 41 publications

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“…Here, we add to important work to establish the reliability and validity of MSE [63,115,116] and to suggest its validity as a measure of individual differences [116]. However, a limitation to these and the majority of other studies investigating EEG variability metrics to date is the focus on a limited number of manually selected metrics with minimal consideration of alternatives [117].…”

Section: Limitations and Future Directionsmentioning

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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Section: Limitations and Future Directionsmentioning

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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“…To avoid multiple testing, we integrated MSE values across several scales into single scores. This procedure has been previously proposed in the literature as an approach to handle such multiple scale measurements (see Takahashi et al, 2009;Kaur et al, 2019). Hence, we used the area under the curve (AUC) as an integrated entropy score per participant.…”

Section: Latent Mean and Individual Differences In Multiscale Entropymentioning

confidence: 99%

What Does Temporal Brain Signal Complexity Reveal About Verbal Creativity?

Kaur

Ouyang

Sommer

et al. 2020

Front. Behav. Neurosci.

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Recent empirical evidence reveals that creative idea generation builds upon an interplay of multiple neural networks. Measures of temporal complexity yield important information about the underlying mechanisms of these co-activated neural networks. A few neurophysiological studies investigated brain signal complexity (BSC) during the production of creative verbal associations and resting states, aiming to relate it with creative task performance. However, it is unknown whether the complexity of brain signals can distinguish between productions of typical and original verbal associations. In the present study, we investigated verbal creativity with multiscale entropy (MSE) of electroencephalography (EEG) signals, which quantifies complexity over multiple timescales, capturing unique dynamic features of neural networks. MSE was measured in verbal divergent thinking (DT) states while emphasizing on producing either typical verbal associations or original verbal associations. We hypothesized that MSE differentiates between brain states characterizing the production of typical and original associations and is a sensitive neural marker of individual differences in producing original associations. Results from a sample of N = 92 young adults revealed slightly higher average MSE for original as compared with typical association production in small and medium timescales at frontal electrodes and slightly higher average MSE for typical association production in higher timescales at parietal electrodes. However, measurement models failed to uncover specificity of individual differences as MSE in typical vs. original associations was perfectly correlated. Hence, individuals with higher MSE in original association condition also exhibit higher MSE during the production of typical associations. The difference between typical and original association MSE was not significantly associated with human-rated originality of the verbal associations. In sum, we conclude that MSE is a potential marker of creative verbal association states, but replications and extensions are needed, especially with respect to the brain-behavior relationships.

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“…To avoid multiple testing issues in statistical analyses, we integrated the MSE values across adjacent time scales, following the procedure used in a previous study (Kaur et al, 2019), and calculated areas under the curve (AUCs) of MSE as integrated measures at low (1-5), medium (6-11), and high (12-20) time scales, for each participant and electrode. These composite values of MSE were later used to obtain electrode-specific latent variables (see Section 2.4.1).…”

Section: Mse Calculationmentioning

confidence: 99%

“…1A). AUC was calculated according to the procedure proposed by Kaur et al (2019). The length of segments used for calculating MSE indicators was chosen on the basis of prior reliability analyses (see Supplementary Material S1).…”

Section: Discovering Rois For Genetic Associations Of Mse In Samplementioning

confidence: 99%

“…Moreover, we related APOE genotypes, MSE and cognitive ability estimates in an integrated statistical model. Going far beyond previous studies, we applied a latent variable approach to MSE (Kaur et al, 2019) as well as to cognitive performance assessments (Raykov, 2006). This psychometric approach is powerful because it integrates MSE across both time scales and spatial regions of interest, and controls for measurement error.…”

Section: Introductionmentioning

confidence: 99%

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Resting state brain signal complexity of young healthy adults reflects genetic risk for developing Alzheimer’s Disease

Kaur

Wilhelm

et al. 2020

Preprint

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The e4 allele of the APOE gene is strongly associated with impaired brain functionality and cognitive decline in humans at older age. It is controversial whether and how the APOE e4 allele is affecting brain activity among young healthy individuals and how such effects may contribute to individual differences in cognitive performance. Signal complexity is a critical aspect of brain activity that has been shown to be associated with brain function. In this study, we analyzed multiscale entropy (MSE) of EEG signals among young healthy adults as an indicator of brain signal complexity and investigated how MSE is predicted by APOE genotype groups. Furthermore, by means of structural equation modeling, we investigated whether MSE predicts fluid intelligence. Results indicate larger MSE in young healthy e4 carriers across all time scales. Moreover, better fluid intelligence (gf) is associated with smaller MSE at low time scales and larger MSE at higher scales. However, MSE does not account for better cognitive performance among APOE e4 carriers by mediating the APOE genotype effect on fluid intelligence. The present results shed further light on the neural mechanisms underlying gene-behavior association relevant for Alzheimer’s Disease risk.

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The reliability and psychometric structure of Multi-Scale Entropy measured from EEG signals at rest and during face and object recognition tasks

Cited by 16 publications

References 41 publications

Epigenetic tuning of brain signal entropy in emergent human social behavior

Epigenetic tuning of brain signal entropy in emergent human social behavior

What Does Temporal Brain Signal Complexity Reveal About Verbal Creativity?

Resting state brain signal complexity of young healthy adults reflects genetic risk for developing Alzheimer’s Disease

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