Entropy and the Brain: An Overview

Abstract

Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results: the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks’ information processing. In so doing, the present overview identifies that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. Moreover, it realizes that the use of these measures for the study of the ageing brain resulted in significant insights on various ways that the process of ageing may affect the dynamics and information processing capacity of the brain. It further reveals that their utilization for analysis of the brain regional interactivity formed a bridge between the previous two research areas, thereby providing further evidence in support of their results. It concludes by highlighting some potential considerations that may help future research to refine the use of entropic measures for the study of brain complexity and its function. The present study helps realize that (despite their seemingly differing lines of inquiry) the study of consciousness, the ageing brain, and the brain networks’ information processing are highly interrelated. Specifically, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain’s capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

“…Particularly, it is known that the brain signal variability is indicative of its functioning. Specifically, this variability is generated from the interplay between single neurons and their neuronal circuits that allows the brain to self-organize itself in order to maximize the brain information capacity [ 31 ]. In turn, these findings explain the capacity of entropy of quantifying the brain’s information processing [ 32 , 33 , 34 ], given the direct correspondence between the variance and the amount of information.…”

“…In turn, these findings explain the capacity of entropy of quantifying the brain’s information processing [ 32 , 33 , 34 ], given the direct correspondence between the variance and the amount of information. This approach revealed promising results in the assessment of altered state of consciousness, brain aging, and quantification of the brain networks’ information processing [ 31 ]. The complexity of cerebral signals can be evaluated using different entropy metrics.…”

“…The conditional entropy (CondEn) quantifies the amount of information needed to describe the outcome of a variable from the value of another variable [ 36 ]. Complexity evaluation is widely employed in neuroimaging to assess physiology and pathology [ 31 ]. In AD patients, it was utilized to analyze EEG signals acquired during resting state [ 37 , 38 ] and to investigate cortical activation during the execution of cognitive tasks employing fNIRS [ 39 , 40 ].…”

Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms

“…Although the methods described above for detecting driver states have made remarkable progress, there is still room for improvement in the accuracy and robustness of detection systems based on EEG signals. An entropy-based method for measuring non-linear dynamic parameters of incidence of new information in a time series has been applied in many scientific fields such as assisted diagnosis of diseases [ 20 ], cognitive ability understanding [ 21 ], neuroimaging research [ 22 ] and quantification of brain function [ 23 ]. Inspired by this, we consider the EEG signal, especially in an actual driving environment, to have the characteristics of complexity, instability and non-linearity.…”

Information Processing in the Brain as Optimal Entropy Transport: A Theoretical Approach

“…In a subsequent work, we explore this question in more detail, but a plausible connection can be established with experimental and theoretical results via fMRI in which different measures of cost have been proposed. The brain as an informational system is the subject of active research (see for instance [ 6 , 7 , 8 ]). For example, “behavior analysis has adopted these tools [Information theory] as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes” [ 1 ].…”