Entropy in Heart Rate Dynamics Reflects How HRV-Biofeedback Training Improves Neurovisceral Complexity during Stress-Cognition Interactions

Abstract

Despite considerable appeal, the growing appreciation of biosignals complexity reflects that system complexity needs additional support. A dynamically coordinated network of neurovisceral integration has been described that links prefrontal-subcortical inhibitory circuits to vagally-mediated heart rate variability. Chronic stress is known to alter network interactions by impairing amygdala functional connectivity. HRV-biofeedback training can counteract stress defects. We hypothesized the great value of an entropy-based approach of beat-to-beat biosignals to illustrate how HRVB training restores neurovisceral complexity, which should be reflected in signal complexity. In thirteen moderately-stressed participants, we obtained vagal tone markers and psychological indexes (state anxiety, cognitive workload, and Perceived Stress Scale) before and after five-weeks of daily HRVB training, at rest and during stressful cognitive tasking. Refined Composite Multiscale Entropy (RCMSE) was computed over short time scales as a marker of signal complexity. Heightened vagal tone at rest and during stressful tasking illustrates training benefits in the brain-to-heart circuitry. The entropy index reached the highest significance levels in both variance and ROC curves analyses. Restored vagal activity at rest correlated with gain in entropy. We conclude that HRVB training is efficient in restoring healthy neurovisceral complexity and stress defense, which is reflected in HRV signal complexity. The very mechanisms that are involved in system complexity remain to be elucidated, despite abundant literature existing on the role played by amygdala in brain interconnections.

Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial

“…Compared to single-scale entropy estimates, MSE has been able to accurately account for lower HRV observed in different cardiac abnormalities, such as atrial fibrillation where there are random outputs in HRV [83]. As MSE formulation does not allow for reliable computation of entropy in short time windows, new methods such as refined MSE (RMSE) [84] and refined composite MSE (RCMSE) [85] have been introduced, though few studies have tested them on HRV [86,87].…”

Physiological Resonance in Empathic Stress: Insights from Nonlinear Dynamics of Heart Rate Variability

“…Both top-down (brain-to-heart) and bottom-up (heart-to-brain) aspects have been examined. HRV biofeedback training [ 29 , 30 , 31 ], a routine stimulating the baroreflex by slow breathing, shows that large oscillations in HRV, in any circumstances, influence neural networks, especially in brain regions regulating stress and emotions [ 32 ]. Concomitant top-down and bottom-up interactions maintain/improve functional connectivity in brain networks, spanning several temporal scales, which is likely reflected in some particular markers able to reflect multiplicative interactions at several scales.…”

“…The computations of these particular nonlinear HRV marker has been recommended in recent works to explore mood and cognition [ 40 ]. They were also shown to reflect how stress interacts with cognition [ 31 , 42 , 43 ].…”

“…The aim of the present study was to focus on nonlinear HRV dynamics, able to reflect multiple interactions across temporal scales that take place through both top-down and bottom-up communication between the brain and the heart, to gain a better understanding of physiological resonance associated with empathic stress. On the basis of our previous works [ 31 , 42 , 43 ], we hypothesized that the complexity marker of entropy would reflect an alteration in heart–brain interactions in targets experiencing stress as well as in observers, thus characterizing both first-hand stress and second-hand stress (empathic stress). To go further, we also hypothesized that this marker could highlight a physiological resonance phenomenon in observers (physiological linkage with the targets).…”

Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

“…Finally, two contributions to this Special Issue investigate "brain–heart interactions". The paper by Deschodt-Arsac et al [ 15 ] demonstrates that five weeks of a biofeedback training able to reduce stress and anxiety increases the multiscale entropy of heart rate during a stressful cognitive task. The results support the hypothesis that the adopted biofeedback training restores a healthy response to stress consisting of an increased heart rate complexity through mechanisms of neurovisceral integration.…”