Recent advances in physiological oscillations

Cardio-respiratory coupling and myocardial recovery in heart failure with reduced ejection fraction

INTRODUCTION

The interaction between the cardiovascular and respiratory systems in healthy subjects is determined by the autonomic nervous system and reflected in respiratory sinus arrhythmia. Recently, another pattern of cardio-respiratory coupling (CRC) has been proposed linking synchronization of heart and respiratory system. However, CRC has not been studied precisely in heart failure (HF) with reduced ejection fraction (EF) (HFrEF) according to the myocardial recovery.

METHODS

10-min resting electrocardiography measurements were performed in persistent HFrEF patients (n=40) who had a subsequent left ventricular EF (LVEF) of ≤ 40 %, HF with recovered EF patients (HFrecEF) (n=41) who had a subsequent LVEF of > 40 % and healthy controls (n=40). Respiratory frequency, respiratory rate, CRC index, time-domain, frequency-domain and nonlinear heart rate variability indices were obtained using standardized software-Kubios™. CRC index was defined as respiratory high-frequency peak minus heart rate variability high-frequency peak.

RESULTS

Respiratory rate was positively correlated with high-frequency (HF) peak (Hz) in both persistent HFrEF group (p<0.001) and HFrecEF group (p<0.001), while respiratory rate was negatively correlated with HF power (ms2) in the healthy controls (p<0.05). CRC index was lowest in the persistent HFrEF group followed by HFrecEF and was high in healthy controls (0.008 vs 0.012 vs 0.056 Hz, p=0.03).

CONCLUSION

CRC index was lowest in patients with impaired myocardial recovery, which indicates that cardio-respiratory synchrony is stronger in persistent HFrEF. This may represent a higher HF peak (Hz)/lower HF power (ms2) and abnormal sympathovagal balance in persistent HFrEF group compared to healthy controls. Further work is underway to tests this hypothesis and determine the utility of CRC index in HF phenotypes and its utility as a potential biomarker of response with neuromodulation.

Exploring metrics for the characterization of the cerebral autoregulation during head-up tilt and propofol general anesthesia

Techniques grounded on the simultaneous utilization of Tiecks' second order differential equations and spontaneous variability of mean arterial pressure (MAP) and mean cerebral blood flow velocity (MCBFV), recorded from middle cerebral arteries through a transcranial Doppler device, provide a characterization of cerebral autoregulation (CA) via the autoregulation index (ARI). These methods exploit two metrics for comparing the measured MCBFV series with the version predicted by Tiecks' model: normalized mean square prediction error (NMSPE) and normalized correlation ρ. The aim of this study is to assess the two metrics for ARI computation in 13 healthy subjects (age: 27 ± 8 yrs., 5 males) at rest in supine position (REST) and during 60° head-up tilt (HUT) and in 19 patients (age: 64 ± 8 yrs., all males), scheduled for coronary artery bypass grafting, before (PRE) and after (POST) propofol general anesthesia induction. Analyses were carried out over the original MAP and MCBFV pairs and surrogate unmatched couples built individually via time-shifting procedure. We found that: i) NMSPE and ρ metrics exhibited similar performances in passing individual surrogate test; ii) the two metrics could lead to different ARI estimates; iii) CA was not different during HUT or POST compared to baseline and this conclusion held regardless of the technique and metric for ARI estimation. Results suggest a limited impact of the sympathetic control on CA.

Beyond the Baroreflex: A New Measure of Autonomic Regulation Based on the Time-Frequency Assessment of Variability, Phase Coherence and Couplings

For decades the role of autonomic regulation and the baroreflex in the generation of the respiratory sinus arrhythmia (RSA) - modulation of heart rate by the frequency of breathing - has been under dispute. We hypothesized that by using autonomic blockers we can reveal which oscillations and their interactions are suppressed, elucidating their involvement in RSA as well as in cardiovascular regulation more generally. R-R intervals, end tidal CO2, finger arterial pressure, and muscle sympathetic nerve activity (MSNA) were measured simultaneously in 7 subjects during saline, atropine and propranolol infusion. The measurements were repeated during spontaneous and fixed-frequency breathing, and apnea. The power spectra, phase coherence and couplings were calculated to characterise the variability and interactions within the cardiovascular system. Atropine reduced R-R interval variability (p < 0.05) in all three breathing conditions, reduced MSNA power during apnea and removed much of the significant coherence and couplings. Propranolol had smaller effect on the power of oscillations and did not change the number of significant interactions. Most notably, atropine reduced R-R interval power in the 0.145-0.6 Hz interval during apnea, which supports the hypothesis that the RSA is modulated by a mechanism other than the baroreflex. Atropine also reduced or made negative the phase shift between the systolic and diastolic pressure, indicating the cessation of baroreflex-dependent blood pressure variability. This result suggests that coherent respiratory oscillations in the blood pressure can be used for the non-invasive assessment of autonomic regulation.

Linear and Nonlinear Directed Connectivity Analysis of the Cardio-Respiratory System in Type 1 Diabetes

In this study, we explored the possibility of developing non-invasive biomarkers for patients with type 1 diabetes (T1D) by quantifying the directional couplings between the cardiac, vascular, and respiratory systems, treating them as interconnected nodes in a network configuration. Towards this goal, we employed a linear directional connectivity measure, the directed transfer function (DTF), estimated by a linear multivariate autoregressive modelling of ECG, respiratory and skin perfusion signals, and a nonlinear method, the dynamical Bayesian inference (DBI) analysis of bivariate phase interactions. The physiological data were recorded concurrently for a relatively short time period (5 min) from 10 healthy control subjects and 10 T1D patients. We found that, in both control and T1D subjects, breathing had greater influence on the heart and perfusion with respect to the opposite coupling direction and that, by both employed methods of analysis, the causal influence of breathing on the heart was significantly decreased (p < 0.05) in T1D patients compared to the control group. These preliminary results, although obtained from a limited number of subjects, provide a strong indication for the usefulness of a network-based multi-modal analysis for the development of biomarkers of T1D-related complications from short-duration data, as well as their potential in the exploration of the pathophysiological mechanisms that underlie this devastating and very widespread disease.

Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer TRESP,SBP→RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration (USBP→RR), nonbaroreflex (URESP→RR) and baroreflex-mediated (RRESP,SBP→RR) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences (SRESP,SBP→RR), respectively. We find that fast (short time scale) HRV oscillations—respiratory sinus arrhythmia—originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT.