Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Vecchio, Fabrizio; Miraglia, Francesca; Pappalettera, Chiara; Orticoni, Alessandro; Alù, Francesca; Judica, Elda; Cotelli, Maria; Rossini, Paolo Maria

doi:10.3390/sym13112178

Cited by 23 publications

(16 citation statements)

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“…According to dynamic systems theory, the state space of the brain signal is widely enlarged during closed eyes resting states when in the absence of external stimulus input, and mental activity is self‐organized by underlying neural networks. This should have the consequence that EC resting states basically induce larger MSE than EO resting states (Vecchio et al, 2021). However, our results indicate an interaction effect with scale.…”

Section: Discussionmentioning

confidence: 99%

Resting‐state brain signal complexity discriminates young healthy APOE e4 carriers from non‐e4 carriers

Kaur

Wilhelm

et al. 2023

Eur J of Neuroscience

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It is well established that the e4 allele of the APOE gene is associated with impaired brain functionality and cognitive decline in humans at elder age. However, it is controversial whether and how the APOE e4 allele is associated with superior brain function among young healthy individuals, thus indicates a case of antagonistic pleiotropy of APOE e4 allele. Signal complexity is a critical aspect of brain activity that has been associated with brain function. In this study, the multiscale entropy (MSE) of resting-state EEG signals among a sample of young healthy adults (N = 260) as an indicator of brain signal complexity was investigated. It was of interest whether MSE differs across APOE genotype groups while age and education level were controlled for and whether the APOE genotype effect on MSE interacts with MSE time scale, as well as EEG recording condition. Results of linear mixed models indicate overall larger MSE in APOE e4 carriers. This genotype-dependent difference is larger at high as compared with low time scales. The interaction effect between APOE

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Section: Discussionmentioning

confidence: 99%

Resting‐state brain signal complexity discriminates young healthy APOE e4 carriers from non‐e4 carriers

Kaur

Wilhelm

et al. 2023

Eur J of Neuroscience

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“…The resting-state EEG (rsEEG) studies have produced inconsistent results regarding the scalp topography of entropy parameters. For example, the maximal complexity values were located over the posterior region (Alù et al, 2021;Gu et al, 2022) or the frontal, central and temporal areas (Vecchio et al, 2021). Racz et al (2019) found the highest permutation entropy measures of rsEEG activity at the electrodes corresponding to the somatomotor and dorsal attention networks.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multiscale Entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender- related differences

Lewandowska

Tołpa

Rogala

et al. 2023

Preprint

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Background The study aimed to determine the resting-state EEG (rsEEG) dynamics quantified using the multivariate Multiscale Entropy (mMSE), and the sex/gender (s/g) differences in the mMSE features. The rsEEG was acquired from 95 healthy adults. For each channel set the AUC, that represents the total complexity, the MaxSlope and AvgEnt referring to the entropy at the fine- and coarse-grained scales, respectively, were extracted. The difference in the entropy between the #9 and #4 timescale (DiffEnt) was also calculated. Results We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets and there were no s/g differences in the AUC. Conclusions Novel results of the present study are: 1) an identification of the mMSE features that capture entropy at the fine and the coarse timescales in the channel sets corresponding to the main resting-state networks; 2) an indication of the sex/gender differences in these features.

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“…29 Given these premises, it was considered reasonable to apply entropy measures for detecting the variability and complexity of EEG signals. The approximate entropy (ApEn) is one of the most widely used indices in examining brain activity 30 and many studies have confirmed its capability to detect brain alterations in physiological and pathological conditions. [31][32][33] Several studies [32][33][34][35] have revealed that entropy analysis of physiological signal dynamics can disclose information that might not be contained in average or spectral analysis.…”

Section: Introductionmentioning

confidence: 99%

Complexity analysis from EEG data in congestive heart failure: A study via approximate entropy

et al. 2023

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Aim Congestive heart failure (CHF) is a very complex clinical syndrome that may lead to ischemic cerebral hypoxia condition. The aim of the present study is to analyze the effects of CHF on brain activity through electroencephalographic (EEG) complexity measures, like approximate entropy (ApEn). Methods Twenty patients with CHF and 18 healthy elderly people were recruited. ApEn values were evaluated in the total spectrum (0.2–47 Hz) and main EEG frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–11 Hz), alpha 2 (11–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz), and gamma (30–45 Hz) to identify differences between CHF group and control. Moreover, a correlation analysis was performed between ApEn parameters and clinical data (i.e., B‐type natriuretic peptides (BNP), New York Heart Association (NYHA), and systolic blood pressure (SBP)) within the CHF group. Results Statistical topographic maps showed statistically significant differences between the two groups in the total spectrum and theta frequency band. Within the CHF group, significant negative correlations were found between total ApEn and BNP in O2 channel and between theta ApEn and NYHA scores in Fp1, Fp2, and Fz channels; instead, a significant positive correlation was found between theta ApEn and SBP in C3 channel and a nearly significant positive correlation was obtained between theta ApEn and SBP in F4 channel. Conclusion EEG abnormalities in CHF are very similar to those observed in cognitive‐impaired patients, suggesting analogies between the effects of neurodegeneration and brain chronic hypovolaemia due to heart disorder and underlying high brain sensitivity to CHF.

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Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Cited by 23 publications

References 50 publications

Resting‐state brain signal complexity discriminates young healthy APOE e4 carriers from non‐e4 carriers

Resting‐state brain signal complexity discriminates young healthy APOE e4 carriers from non‐e4 carriers

Multivariate Multiscale Entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender- related differences

Complexity analysis from EEG data in congestive heart failure: A study via approximate entropy

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