Electrodermal activity (EDA) and heart rate variability (HRV) offer opportunities to grasp critical manifestations of the nervous autonomic system using low-intrusive sensing tools. A key question relies on the capacity to adequately process EDA and HRV signals to extract cognitive load markers, a multifaceted construct with intricate neural networks functioning, where emotions interfere with cognition. Here, 34 participants (20 males, 19.2 ± 1.3 years) were exposed to two-back mental tasking and watching emotionally charged images while recording EDA and HRV. HRV signals were processed using variable frequency complex demodulation (VFCDM) and wavelet packet transform (WPT) to provide high- and low-frequency (HF and LF) markers. Three methods were used to extract EDA indices: VFCDM (EDA_TVSYMP), WPT (EDA_WPT), and convex-optimization (EDA_CVX). Cognitive load and emotion epochs were distinguished by significant differences in NASA-TLX scores, mental fatigue, and stress, on the one hand; and by EDA_CVX and, remarkably, EDA_TVSYMP and HF-HRV_VFCDM on the other hand. A linear mixed-effects model and stepwise backward selection procedure showed that these two markers were main predictors of the NASA-TLX score (cognitive load). The individual perception of cognitive load was finally discriminated by k-means clustering, showing three profiles of autonomic responses relying, respectively, on EDA_TVSYMP, HF-HRV_VFCDM, or a mix of these two markers. The existence of EDA-, HRV-, and EDA/HRV-derived profiles might explain why previous attempts that have predominantly employed a single biosignal often remained unconclusive in evaluating the perceived cognitive load, thereby demonstrating the added value of the present approach to monitor mental-related workload in human operators.

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.

In humans, physiological systems involved in maintaining stable conditions for health and well-being are complex, encompassing multiple interactions within and between system components. This complexity is mirrored in the temporal structure of the variability of output signals. Entropy has been recognized as a good marker of systems complexity, notably when calculated from heart rate and postural dynamics. A degraded entropy is generally associated with frailty, aging, impairments or diseases. In contrast, high entropy has been associated with the elevated capacity to adjust to an ever-changing environment, but the link is unknown between entropy and the capacity to cope with cognitive tasks in a healthy young to middle-aged population. Here, we addressed classic markers (time and frequency domains) and refined composite multiscale entropy (MSE) markers (after pre-processing) of heart rate and postural sway time series in 34 participants during quiet versus cognitive task conditions. Recordings lasted 10 min for heart rate and 51.2 s for upright standing, providing time series lengths of 500–600 and 2048 samples, respectively. The main finding was that entropy increased during cognitive tasks. This highlights the possible links between our entropy measures and the systems complexity that probably facilitates a control remodeling and a flexible adaptability in our healthy participants. We conclude that entropy is a reliable marker of neurophysiological complexity and adaptability in autonomic and somatic systems.

Fractal physiology demonstrated growing interest over the last decades among physiologists, neuroscientists, and clinicians. Many physiological systems coordinate themselves for reducing variability and maintain a steady state. When recorded over time, the output signal exhibits small fluctuations around a stable value. It is becoming increasingly clear that these fluctuations, in most free-running healthy systems, are not simply due to uncorrelated random errors and possess interesting properties, one of which is the property of fractal dynamics. Fractal dynamics model temporal processes in which similar patterns occur across multiple timescales of measurement. Smaller copies of a pattern are nested within larger copies of the pattern, a property termed scale invariance. It is an intriguing process that may deserve attention for implementing curricular development for students to reconsider homeostasis. Teaching fractal dynamics needs to make calculating resources available for students. The present paper offers a calculating resource that uses a basic formula and is executable in a simple spreadsheet. The spreadsheet allows computing detrended fluctuation analysis (DFA), the most frequently used method in the literature to quantify the fractal-scaling index of a physiological time series. DFA has been nicely described by the group at Harvard that designed it; the authors made the C language source available. Going further, it is suggested here that a guide to build DFA step by step in a spreadsheet has many advantages for teaching fractal physiology and beyond: 1) it promotes the DIY (do-it-yourself) in students and highlights scaling concepts; and 2) it makes DFA available for people not familiarized with executing code in C language.

Daily-life behaviors strongly rely on visuomotor integration, a complex sensorimotor process with obvious plasticity. Visual-perceptive and visual-cognitive functions are degraded by neurological disorders and brain damage, but are improved by vision training, e.g. in athletes. Hence, developing tools to evaluate/improve visuomotor abilities has found echo among psychologists, neurophysiologists, clinicians and sport professionals. Here we implemented the Dynavision visuomotor reaction task in virtual reality (VR) to get a flexible tool to place high demands on visual-perceptive and visual-cognitive processes, and explore individual abilities in visuomotor integration. First, we demonstrated high test–retest reliability for the task in VR among healthy physically-active students (n = 64, 32 females). Second, the capture of head movements thanks to the VR-headset sensors provided new and reliable information on individual visual-perceptual strategies, which added significant value to explore visuomotor phenotypes. A factor analysis of mixed data and hierarchical clustering on principal components points to head movements, video-games practice and ball-tracking sports as critical cues to draw visuomotor phenotypes among our participants. We conclude that the visuomotor task in VR is a reliable, flexible and promising tool. Since VR nowadays can serve e.g. to modulate multisensorial integration by creating visual interoceptive-exteroceptive conflicts, or placing specifically designed cognitive demand, much could be learned on complex integrated visuomotor processes through VR experiments. This offers new perspectives for post brain injury risk evaluation, rehabilitation programs and visual-cognitive training.

Because most humans live and work in populated environments, researchers recently took into account that people may not only experience first-hand stress, but also second-hand stress related to the ability to empathically share another person’s stress response. Recently, researchers have begun to more closely examine the existence of such empathic stress and highlighted the human propensity to physiologically resonate with the stress responses of others. As in case of first-hand stress, empathic stress could be deleterious for health if people experience exacerbated activation of hypothalamic–pituitary–adrenal and autonomic nervous systems. Thus, exploring empathic stress in an observer watching someone else experiencing stress is critical to gain a better understanding of physiological resonance and conduct strategies for health prevention. In the current study, we investigated the influence of empathic stress responses on heart rate variability (HRV) with a specific focus on nonlinear dynamics. Classic and nonlinear markers of HRV time series were computed in both targets and observers during a modified Trier social stress test (TSST). We capitalized on multiscale entropy, a reliable marker of complexity for depicting neurovisceral interactions (brain-to-heart and heart-to-brain) and their role in physiological resonance. State anxiety and affect were evaluated as well. While classic markers of HRV were not impacted by empathic stress, we showed that the complexity marker reflected the existence of empathic stress in observers. More specifically, a linear model highlighted a physiological resonance phenomenon. We conclude on the relevance of entropy in HRV dynamics, as a marker of complexity in neurovisceral interactions reflecting physiological resonance in empathic stress.