Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Abstract

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.

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

“…Other entropy indices, such as the Sample Entropy (SampEn) and the Normalized Corrected Shannon Entropy (NCSEn), have been shown to be higher in the EO than in the EC condition, the former in frontal, temporal, and parietal cerebral areas, and the latter over all the cortex, suggesting that entropy parameters are sensitive to increases during external stimuli processing [13,18]. One of the most used indices of the entropy in brain activity examination [19][20][21][22] is the approximate entropy (ApEn). It is known for the advantages related to its properties: it maintains a good reproducibility if used with the time series of at least 50 samples; it is mostly insensitive to noise; it is finite for composite, stochastic, and noisy deterministic processes [23]; and it reveals the underlying episodic behavior changes undetected by peak occurrences or amplitudes [22].…”

“…In the present analysis, the Matlab default values for m and r were utilized: so, m was equal to 2 and r to 0.2 * variance (x) [33][34][35], with x that corresponds to a 2 s long epoch of a specific channel. A low ApEn of the investigated signal is suggestive of a high similarity between data sequences [19]. The values of ApEn calculated on each single epoch were averaged to obtain a value for each channel [36].…”

“…The values of ApEn calculated on each single epoch were averaged to obtain a value for each channel [36]. The ApEn values computed in each channel were thus averaged over ten regions of interest (ROIs), five for each hemisphere (frontal, central, parietal, occipital, temporal) [19,20], in order to obtain a single value of entropy for each ROI, as shown in Figure 1. The midline channels were not considered.…”

BFF: Bayesian, Fiducial, and Frequentist Analysis of Cognitive Engagement among Cognitively Impaired Older Adults

“…Of the 28 participants, 14 participated in the Wave 2 study approximately 6 months later before in-person testing was halted due to the COVID-19 pandemic. Complex data collection procedures with intensive physiological assessments typically rely on relatively small sample sizes (e.g., [ 54 ]), and here we focus on the large number of within-person assessments. Participants received $50 for compensation at each of the two waves of the study.…”

Entropy in Brain Networks

“…They illustrate the potential and pertinence of this tool to gain further knowledge into brain functioning. This is the case of the study by Alù et al [ 4 ], in which they assessed the reproducibility and stability of approximate entropy across time, as well as hemispheric differences, in a longitudinal electroencephalographic (EEG) dataset. From a more theoretical point of view, Maren [ 5 ] addressed a closely related concept to entropy: enthalpy, which is used to characterize 2D image topographies using the 2D Cluster Variation Method (CVM).…”