Entropy and Multifractal-Multiscale Indices of Heart Rate Time Series to Evaluate Intricate Cognitive-Autonomic Interactions.

Pierre Bouny,Emma Touré Cuq,Veronique Deschodt-Arsac,Laurent M Arsac

doi:10.3390/e23060663

Abstract

Recent research has clarified the existence of a networked system involving a cortical and subcortical circuitry regulating both cognition and cardiac autonomic control, which is dynamically organized as a function of cognitive demand. The main interactions span multiple temporal and spatial scales and are extensively governed by nonlinear processes. Hence, entropy and (multi)fractality in heart period time series are suitable to capture emergent behavior of the cognitive-autonomic network coordination. This study investigated how entropy and multifractal-multiscale analyses could depict specific cognitive-autonomic architectures reflected in the heart rate dynamics when students performed selective inhibition tasks. The participants () completed cognitive interference (Stroop color and word task), action cancellation (stop-signal) and action restraint (go/no-go) tasks, compared to watching a neutral movie as baseline. Entropy and fractal markers (respectively, the refined composite multiscale entropy and multifractal-multiscale detrended fluctuation analysis) outperformed other time-domain and frequency-domain markers of the heart rate variability in distinguishing cognitive tasks. Crucially, the entropy increased selectively during cognitive interference and the multifractality increased during action cancellation. An interpretative hypothesis is that cognitive interference elicited a greater richness in interactive processes that form the central autonomic network while action cancellation, which is achieved via biasing a sensorimotor network, could lead to a scale-specific heightening of multifractal behavior.

Highlights

IntroductionThe main intuition is that large and flexible networked systems arise, re-organize themselves as a function of sensory inputs, mood and internal processing to cohere in an efficient executive control
A set of metrics including individual response time and accuracy served for quantifying cognitive tasks performance was obtained (Table 1)
The small variability in response time may rely on the instructions given to the participants in our conditions, encouraging accuracy rather than speed

Summary

Introduction

The main intuition is that large and flexible networked systems arise, re-organize themselves as a function of sensory inputs, mood and internal processing to cohere in an efficient executive control This way, rather than conceiving independent encapsulated entities, system theories plead for elaborated architectures of intricate networks where interdependencies, governed by nonlinear processes, are more important than the components themselves, to achieve efficient functions. To distinguish white noise from complex RR time series, each experimental series was shuffled (n = 50 shuffle surrogates) and RCMSE was calculated for each participant in each cognitive task. A distinct profile between experimental and surrogate series was a prerequisite for considering that the entropy in our RR time series is not the consequence of background noise (see Figure 10 in Results). For each situation, the number of participants exhibiting an RR series with Ei values below the 2.5th percentile was quantified (see Figure 11 in Results)

Methods

Results

Discussion

Conclusion