Directional couplings between the respiration and parasympathetic control of the heart rate during sleep and wakefulness in healthy subjects at different ages

Future of Sleep Medicine: Novel Insights on Sleep Regulation from Network Physiology (Part II)

The authors review recent progress in understanding fundamental aspects of physiologic regulation during wake and sleep based on modern data-driven, analytic, and computational approaches with focus on the complex dynamics of physiologic systems interactions, their coexisting and transient forms of coupling, and the role of network integration among physiologic systems in generating states and functions at the organism level. They underscore the importance of novel network-based integrative approaches and the network physiology framework to investigate the structure and dynamics of physiologic networks and to quantify emergent global states and behaviors in health and disease.

Decreased cardio-respiratory information transfer is associated with deterioration and a poor prognosis in critically ill patients with sepsis

Assessing illness severity in the intensive care unit (ICU) is crucial for early prediction of deterioration and prognosis. Traditional prognostic scores often treat organ systems separately, overlooking the body's interconnected nature. Network physiology offers a new approach to understanding these complex interactions. This study used the concept of transfer entropy (TE) to measure information flow between heart rate (HR), respiratory rate (RR), and capillary oxygen saturation ([Formula: see text]) in critically ill patients with sepsis, hypothesizing that TE between these signals would correlate with disease outcome. The retrospective cohort study utilized the Medical Information Mart for Intensive Care III Clinical Database, including patients who met Sepsis-3 criteria on admission and had 30 min of continuous HR, RR, and [Formula: see text] data. TE between the signals was calculated to create physiological network maps. Cox regression assessed the relationship between cardiorespiratory network indices and both deterioration [Sequential Organ Failure Assessment (SOFA) score increase of ≥2 points at 48 h] and 30-day mortality. Among 164 patients, higher information flow from [Formula: see text] to HR [TE ([Formula: see text] → HR)] and reciprocal flow between HR and RR [TE (RR → HR) and TE (HR → RR)] were linked to reduced mortality, independent of age, mechanical ventilation, SOFA score, and comorbidity. Reductions in TE (HR → RR), TE (RR → HR), TE ([Formula: see text] → RR), and TE ([Formula: see text] → HR) were associated with an increased risk of 48-h deterioration. After adjustment for potential confounders, only TE (HR → RR) and TE (RR → HR) remained statistically significant. The study confirmed that physiological network mapping using routine signals in patients with sepsis could indicate illness severity and that higher TE values were generally associated with improved outcomes.NEW & NOTEWORTHY This study adopts an integrative approach through physiological network analysis to investigate sepsis, with the goal of identifying differences in information transfer between physiological signals in sepsis survivors versus nonsurvivors. We found that greater information flow between heart rate, respiratory rate, and capillary oxygen saturation was associated with reduced mortality, independent of age, disease severity, and comorbidities. In addition, reduced information transfer was linked to an increased risk of 48-h deterioration in patients with sepsis.

The new science of sleep: From cells to large-scale societies

In the past 20 years, more remarkable revelations about sleep and its varied functions have arguably been made than in the previous 200. Building on this swell of recent findings, this essay provides a broad sampling of selected research highlights across genetic, molecular, cellular, and physiological systems within the body, networks within the brain, and large-scale social dynamics. Based on this raft of exciting new discoveries, we have come to realize that sleep, in this moment of its evolution, is very much polyfunctional (rather than monofunctional), yet polyfunctional for reasons we had never previously considered. Moreover, these new polyfunctional insights powerfully reaffirm sleep as a critical biological, and thus health-sustaining, requisite. Indeed, perhaps the only thing more impressive than the unanticipated nature of these newly emerging sleep functions is their striking divergence, from operations of molecular mechanisms inside cells to entire group societal dynamics.

Putting back respiration into respiratory sinus arrhythmia or high-frequency heart rate variability: Implications for interpretation, respiratory rhythmicity, and health

Research on respiratory sinus arrhythmia, or high-frequency heart rate variability (its frequency-domain equivalent), has been popular in psychology and the behavioral sciences for some time. It is typically interpreted as an indicator of cardiac vagal activity. However, as research has shown for decades, the respiratory pattern can influence the amplitude of these noninvasive measures substantially, without necessarily reflecting changes in tonic cardiac vagal activity. Although changes in respiration are systematically associated with experiential and behavioral states, this potential confound in the interpretation of RSA, or HF-HRV, is rarely considered. Interpretations of within-individual changes in these parameters are therefore only conclusive if undertaken relative to the breathing pattern. The interpretation of absolute levels of these parameters between individuals is additionally burdened with the problem of residual inspiratory cardiac vagal activity in humans. Furthermore, multiple demographic, anthropometric, life-style, health, and medication variables can act as relevant third variables that might explain associations of RSA or HF-HRV with experiential and behavioral variables. Because vagal activity measured by these parameters only represents the portion of cardiac vagal outflow that is modulated by the respiratory rhythm, alternative interpretations beyond cardiac vagal activity should be considered. Accumulating research shows that activity of multiple populations of neurons in the brain and the periphery, and with that organ activity and function, are modulated rhythmically by respiratory activity. Thus, observable health benefits ascribed to the cardiac vagal system through RSA or HF-HRV may actually reflect beneficial effects of respiratory modulation. Respiratory rhythmicity may ultimately provide the mechanism that integrates central, autonomic, and visceral activities.

Non-invasive monitoring of cardiac contractility: Trans-radial electrical bioimpedance velocimetry (TREV)

We describe methods and software resources for a bioimpedance measurement technique, 'trans-radial electrical bioimpedance velocimetry' (TREV) that allows for the non-invasive monitoring of relative cardiac contractility and stroke volume. After reviewing the relationship between the measurement and cardiac contractility, we describe the general recording methodology, which requires impedance measurements of the forearm. We provide open-source Jupyter-based software (operable on most computers) for deriving cardiac contractility from the impedance measurements. The software includes tools for removing variance associated with heart rate and respiration. We demonstrate the ability of this bioimpedance measurement for tracking beat-to-beat changes of contractility in a maximal grip force production task. Critically, the results demonstrate both a reactive increase in contractility with force production, and suggest there is a learned increase in contractility prior to grip onset, consistent with anticipatory allostatic autonomic regulation mediated by sympathetic inotropy. The method and software should be of broad utility for investigations of event-related cardiac dynamics in psychophysical studies.

Dynamic networks of cortico-muscular interactions in sleep and neurodegenerative disorders

The brain plays central role in regulating physiological systems, including the skeleto-muscular and locomotor system. Studies of cortico-muscular coordination have primarily focused on associations between movement tasks and dynamics of specific brain waves. However, the brain-muscle functional networks of synchronous coordination among brain waves and muscle activity rhythms that underlie locomotor control remain unknown. Here we address the following fundamental questions: what are the structure and dynamics of cortico-muscular networks; whether specific brain waves are main network mediators in locomotor control; how the hierarchical network organization relates to distinct physiological states under autonomic regulation such as wake, sleep, sleep stages; and how network dynamics are altered with neurodegenerative disorders. We study the interactions between all physiologically relevant brain waves across cortical locations with distinct rhythms in leg and chin muscle activity in healthy and Parkinson's disease (PD) subjects. Utilizing Network Physiology framework and time delay stability approach, we find that 1) each physiological state is characterized by a unique network of cortico-muscular interactions with specific hierarchical organization and profile of links strength; 2) particular brain waves play role as main mediators in cortico-muscular interactions during each state; 3) PD leads to muscle-specific breakdown of cortico-muscular networks, altering the sleep-stage stratification pattern in network connectivity and links strength. In healthy subjects cortico-muscular networks exhibit a pronounced stratification with stronger links during wake and light sleep, and weaker links during REM and deep sleep. In contrast, network interactions reorganize in PD with decline in connectivity and links strength during wake and non-REM sleep, and increase during REM, leading to markedly different stratification with gradual decline in network links strength from wake to REM, light and deep sleep. Further, we find that wake and sleep stages are characterized by specific links strength profiles, which are altered with PD, indicating disruption in the synchronous activity and network communication among brain waves and muscle rhythms. Our findings demonstrate the presence of previously unrecognized functional networks and basic principles of brain control of locomotion, with potential clinical implications for novel network-based biomarkers for early detection of Parkinson's and neurodegenerative disorders, movement, and sleep disorders.

Automatic sleep-stage classification of heart rate and actigraphy data using deep and transfer learning approaches

Manual sleep-stage scoring based on full-night polysomnography data recorded in a sleep lab has been the gold standard of clinical sleep medicine. This costly and time-consuming approach is unfit for long-term studies as well as assessment of sleep on a population level. With the vast amount of physiological data becoming available from wrist-worn devices, deep learning techniques provide an opportunity for fast and reliable automatic sleep-stage classification tasks. However, training a deep neural network requires large annotated sleep databases, which are not available for long-term epidemiological studies. In this paper, we introduce an end-to-end temporal convolutional neural network able to automatically score sleep stages from raw heartbeat RR interval (RRI) and wrist actigraphy data. Moreover, a transfer learning approach enables the training of the network on a large public database (Sleep Heart Health Study, SHHS) and its subsequent application to a much smaller database recorded by a wristband device. The transfer learning significantly shortens training time and improves sleep-scoring accuracy from 68.9% to 73.8% and inter-rater reliability (Cohen's kappa) from 0.51 to 0.59. We also found that for the SHHS database, automatic sleep-scoring accuracy using deep learning shows a logarithmic relationship with the training size. Although deep learning approaches for automatic sleep scoring are not yet comparable to the inter-rater reliability among sleep technicians, performance is expected to significantly improve in the near future when more large public databases become available. We anticipate those deep learning techniques, when combined with our transfer learning approach, will leverage automatic sleep scoring of physiological data from wearable devices and enable the investigation of sleep in large cohort studies.

Information-Theoretic Analysis of Cardio-Respiratory Interactions in Heart Failure Patients: Effects of Arrhythmias and Cardiac Resynchronization Therapy

The properties of cardio-respiratory coupling (CRC) are affected by various pathological conditions related to the cardiovascular and/or respiratory systems. In heart failure, one of the most common cardiac pathological conditions, the degree of CRC changes primarily depend on the type of heart-rhythm alterations. In this work, we investigated CRC in heart-failure patients, applying measures from information theory, i.e., Granger Causality (GC), Transfer Entropy (TE) and Cross Entropy (CE), to quantify the directed coupling and causality between cardiac (RR interval) and respiratory (Resp) time series. Patients were divided into three groups depending on their heart rhythm (sinus rhythm and presence of low/high number of ventricular extrasystoles) and were studied also after cardiac resynchronization therapy (CRT), distinguishing responders and non-responders to the therapy. The information-theoretic analysis of bidirectional cardio-respiratory interactions in HF patients revealed the strong effect of nonlinear components in the RR (high number of ventricular extrasystoles) and in the Resp time series (respiratory sinus arrhythmia) as well as in their causal interactions. We showed that GC as a linear model measure is not sensitive to both nonlinear components and only model free measures as TE and CE may quantify them. CRT responders mainly exhibit unchanged asymmetry in the TE values, with statistically significant dominance of the information flow from Resp to RR over the opposite flow from RR to Resp, before and after CRT. In non-responders this asymmetry was statistically significant only after CRT. Our results indicate that the success of CRT is related to corresponding information transfer between the cardiac and respiratory signal quantified at baseline measurements, which could contribute to a better selection of patients for this type of therapy.

Modifying functional brain networks in focal epilepsy by manual visceral-osteopathic stimulation of the vagus nerve at the abdomen

Non-invasive transcutaneous vagus nerve stimulation elicits similar therapeutic effects as invasive vagus nerve stimulation, offering a potential treatment alternative for a wide range of diseases, including epilepsy. Here, we present a novel, non-invasive stimulation of the vagus nerve, which is performed manually viscero-osteopathically on the abdomen (voVNS). We explore the impact of short-term voVNS on various local and global characteristics of EEG-derived, large-scale evolving functional brain networks from a group of 20 subjects with and without epilepsy. We observe differential voVNS-mediated alterations of these characteristics that can be interpreted as a reconfiguration and modification of networks and their stability and robustness properties. Clearly, future studies are necessary to assess the impact of such a non-pharmaceutical intervention on clinical decision-making in the treatment of epilepsy. However, our findings may add to the current discussion on the importance of the gut-brain axis in health and disease. Clinical Trial Registration: https://drks.de/search/en/trial/DRKS00029914, identifier DRKS00029914.

Analysis of Respiratory Sinus Arrhythmia and Directed Information Flow between Brain and Body Indicate Different Management Strategies of fMRI-Related Anxiety

Background: Respiratory sinus arrhythmia (RSA) denotes decrease of cardiac beat-to-beat intervals (RRI) during inspiration and RRI increase during expiration, but an inverse pattern (termed negative RSA) was also found in healthy humans with elevated anxiety. It was detected using wave-by-wave analysis of cardiorespiratory rhythms and was considered to reflect a strategy of anxiety management involving the activation of a neural pacemaker. Results were consistent with slow breathing, but contained uncertainty at normal breathing rates (0.2–0.4 Hz). Objectives and methods: We combined wave-by-wave analysis and directed information flow analysis to obtain information on anxiety management at higher breathing rates. We analyzed cardiorespiratory rhythms and blood oxygen level-dependent (BOLD) signals from the brainstem and cortex in 10 healthy fMRI participants with elevated anxiety. Results: Three subjects with slow respiratory, RRI, and neural BOLD oscillations showed 57 ± 26% negative RSA and significant anxiety reduction by 54 ± 9%. Six participants with breathing rate of ~0.3 Hz showed 41 ± 16% negative RSA and weaker anxiety reduction. They presented significant information flow from RRI to respiration and from the middle frontal cortex to the brainstem, which may result from respiration-entrained brain oscillations, indicating another anxiety management strategy. Conclusion: The two analytical approaches applied here indicate at least two different anxiety management strategies in healthy subjects.