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Abstract
Information-theoretic measures for quantifying multivariate statistical dependence have proven useful for the study of the unity and diversity of the human brain. Two such measures–integration, I(X), and interaction complexity, CI(X)–have been previously applied to electroencephalographic (EEG) signals recorded during ongoing wakeful brain states. Here, I(X) and CI(X) were computed for empirical and simulated visually-elicited alpha-range (8–13 Hz) EEG signals. Integration and complexity of evoked (stimulus-locked) and induced (non-stimulus-locked) EEG responses were assessed using nonparametric k-th nearest neighbor (KNN) entropy estimation, which is robust to the nonstationarity of stimulus-elicited EEG signals. KNN-based I(X) and CI(X) were also computed for the alpha-range EEG of ongoing wakeful brain states. I(X) and CI(X) patterns differentiated between induced and evoked EEG signals and replicated previous wakeful EEG findings obtained using Gaussian-based entropy estimators. Absolute levels of I(X) and CI(X) were related to absolute levels of alpha-range EEG power and phase synchronization, but stimulus-related changes in the information-theoretic and other EEG properties were independent. These findings support the hypothesis that visual perception and ongoing wakeful mental states emerge from complex, dynamical interaction among segregated and integrated brain networks operating near an optimal balance between order and disorder.
Highlights
IntroductionInformation-Theoretic Measures of Brain Integration and Complexity
EEG responses were assessed using nonparametric k-th nearest neighbor (KNN) entropy estimation, which is robust to the nonstationarity of stimulus-elicited EEG signals
Absolute levels of I(X) and confidence intervals (CIs) (X) were related to absolute levels of alpha-range EEG power and phase synchronization, but stimulus-related changes in the information-theoretic and other EEG properties were independent. These findings support the hypothesis that visual perception and ongoing wakeful mental states emerge from complex, dynamical interaction among segregated and integrated brain networks operating near an optimal balance between order and disorder
Summary
Information-Theoretic Measures of Brain Integration and Complexity. It is well-established that neurocognitive functioning is tied to the dynamical interaction of neuronal networks in the brain. The dynamic interplay of segregation and integration is highly complex, reflecting coordinated interactions among neural hierarchies that are neither fully regular nor fully random [4]. Multiple analytical metrics are available to estimate segregation, integration, and complexity in the brain [8,9,10] by directly characterizing “deviations from statistical independence among components of a neural system” [6]. Information-theoretic methods for quantifying statistical dependence among multiple variables have proven especially useful for this purpose; the application of such techniques to Entropy 2019, 21, 61; doi:10.3390/e21010061 www.mdpi.com/journal/entropy
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