Areas distributed throughout the neuraxis, including the anterior insula, anterior cingulate cortex (ACC), amygdala, hypothalamus, periaqueductal gray matter, parabrachial nucleus, and several regions of the medulla, exert a beat-to-beat control on cardiac function. These areas are critically involved in emotional behavior, stress responses, and homeostatic reflexes and exert their influence on heart rate (HR) and cardiac contractility via the sympathetic and parasympathetic nervous systems. Over the past several years, advances in neuroanatomical, neurophysiologic, and functional imaging studies have provided insight into the central and peripheral mechanisms of neural control of cardiac function in humans. Whereas some issues, such as lateralization of this central control, remain unresolved, the adverse cardiac consequences of a wide variety of neurologic disorders emphasize the need to better understand the functional anatomy and neurochemical mechanisms of the neural control of the heart. Important examples are severe arrhythmias and myocardial injury in the setting of neurologic catastrophes and sudden unexplained death in epilepsy. This review will focus on some of the current experimental and clinical information related to these relevant issues for the neurologist.

Neural control of the heart: recent concepts and clinical correlations.

The understanding of heart rate variability (HRV) has increased parallel with the development of modern physiology. Discovered probably first in 1847 by Ludwig, clinical applications evolved in the second part of the 20th century. Today HRV is mostly used in cardiology and research settings. In general, heart rate variability can be measured over shorter (e.g., 5 to 10 minutes) or longer (12 or 24 hours) periods. Since 1996 most measurements and calculations are made according to the standard of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. As the first step, the series of times between successive R-peaks in the ECG are in milliseconds. It is crucial, however, to identify and remove extrasystoles and artifacts according to standard protocols. The series of QRS distances between successive heartbeats can be analyzed with simple or more sophisticated algorithms, beginning with standard deviation (SDNN) or by the square root of the mean of the sum of squares of differences between adjacent normal RR (rMSSD). Short-term HRV is frequently analyzed with the help of a nonparametric fast Fourier transformation quantifying the different frequency bands during the measurement period. In the last decades, various nonlinear algorithms have been presented, such as different entropy and fractal measures or wavelet analysis. Although most of them have a strong theoretical foundation, their clinical relevance is still debated.

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Hidden Signals-The History and Methods of Heart Rate Variability.

This protocol describes assays for assessing heart rate fluctuations in mice or tissue isolated from mice, including by using in vivo telemetry and in vitro electrophysiology of intact sinoatrial network preparations or isolated single sinoatrial node cells. The normal heartbeat slightly fluctuates around a mean value; this phenomenon is called physiological heart rate variability (HRV). It is well known that altered HRV is a risk factor for sudden cardiac death. The availability of genetic mouse models makes it possible to experimentally dissect the mechanism of pathological changes in HRV and its relation to sudden cardiac death. Here we provide a protocol that allows for a comprehensive multilevel analysis of heart rate (HR) fluctuations. The protocol comprises a set of techniques that include in vivo telemetry and in vitro electrophysiology of intact sinoatrial network preparations or isolated single sinoatrial node (SAN) cells. In vitro preparations can be completed within a few hours, with data acquisition within 1 d. In vivo telemetric ECG requires 1 h for surgery and several weeks for data acquisition and analysis. This protocol is of interest to researchers investigating cardiovascular physiology and the pathophysiology of sudden cardiac death.

Comprehensive multilevel in vivo and in vitro analysis of heart rate fluctuations in mice by ECG telemetry and electrophysiology

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Editorial: Heart Rate Variability, Health and Well-Being: A Systems Perspective

Background Autonomic arousal-responses to emotional stimuli change with age. Age-dependent autonomic responses to music-onset are undetermined. Objective To determine whether cardiovascular-autonomic responses to “relaxing” or “aggressive” music differ between young and older healthy listeners. Methods In ten young (22.8 ± 1.7 years) and 10 older volunteers (61.7 ± 7.7 years), we monitored respiration (RESP), RR-intervals (RRI), and systolic and diastolic blood pressure (BPsys, BPdia) during silence and 180 second presentations of two “relaxing” and two “aggressive” classical-music excerpts. Between both groups, we compared RESP, RRI, BPs, spectral-powers of mainly sympathetic low-frequency (LF: 0.04–0.15 Hz) and parasympathetic high-frequency (HF: 0.15–0.5 Hz) RRI-oscillations, RRI-LF/HF-ratios, RRI-total-powers (TP-RRI), and BP-LF-powers during 30 s of silence, 30 s of music-onset, and the remaining 150 s of music presentation (analysis-of-variance and post-hoc analysis; significance: p  Results During silence, both groups had similar RRI, LF/HF-ratios and LF-BPs; RESP, LF-RRI, HF-RRI, and TP-RRI were lower, but BPs were higher in older than younger participants. During music-onset, “relaxing” music decreased RRI in older and increased BPsys in younger participants, while “aggressive” music decreased RRI and increased BPsys, LF-RRI, LF/HF-ratios, and TP-RRI in older, but increased BPsys and RESP and decreased HF-RRI and TP-RRI in younger participants. Signals did not differ between groups during the last 150 s of music presentation. Conclusions During silence, autonomic modulation was lower – but showed sympathetic predominance – in older than younger persons. Responses to music-onset, particularly “aggressive” music, reflect more of an arousal- than an emotional-response to music valence, with age-specific shifts of sympathetic–parasympathetic balance mediated by parasympathetic withdrawal in younger and by sympathetic activation in older participants.

Music induces different cardiac autonomic arousal effects in young and older persons.

Heart rate and blood pressure are the most important vital signs in diagnosing disease. Both heart rate and blood pressure are characterized by a high degree of short term variability from moment to moment, medium term over the normal day and night as well as in the very long term over months to years. The study of new mathematical algorithms to evaluate the variability of these cardiovascular parameters has a high potential in the development of new methods for early detection of cardiovascular disease, to establish differential diagnosis with possible therapeutic consequences. The autonomic nervous system is a major player in the general adaptive reaction to stress and disease. The quantitative prediction of the autonomic interactions in multiple control loops pathways of cardiovascular system is directly applicable to clinical situations. Exploration of new multimodal analytical techniques for the variability of cardiovascular system may detect new approaches for deterministic parameter identification. A multimodal analysis of cardiovascular signals can be studied by evaluating their amplitudes, phases, time domain patterns, and sensitivity to imposed stimuli, i.e., drugs blocking the autonomic system. The causal effects, gains, and dynamic relationships may be studied through dynamical fuzzy logic models, such as the discrete-time model and discrete-event model. We expect an increase in accuracy of modeling and a better estimation of the heart rate and blood pressure time series, which could be of benefit for intelligent patient monitoring. We foresee that identifying quantitative mathematical biomarkers for autonomic nervous system will allow individual therapy adjustments to aim at the most favorable sympathetic-parasympathetic balance.

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Mathematical biomarkers for the autonomic regulation of cardiovascular system

Luminosity, which is the total amount of radiant energy emitted by an object, is one of the most critical quantities in astrophysics for characterizing stars. Equally important is the temporal evolution of a star’s luminosity because of its intimate connection with the stellar energy budget, large-scale convective motion, and heat storage in the stellar interior. The Sun’s luminosity and its variation have not been measured to date because current observations of the solar radiative output have been restricted to vantage points near the Earth. Here, we model the solar luminosity by extending a semiempirical total solar irradiance (TSI) model that uses solar-surface magnetism to reconstruct solar irradiance over the entire 4π solid angle around the Sun. This model was constrained by comparing its output to the irradiance in the Earth’s direction with the measured TSI. Comparing the solar luminosity to the TSI on timescales from days to solar cycles for cycles 23 and 24, we find poor agreement on short timescales (<solar rotation). This is not unexpected due to the Earth-centric viewing geometry and short-term irradiance dependence on surface features on the Earth-facing solar disk. On longer timescales, however, we find good agreement between the luminosity model and the TSI, which suggests that the extrapolation of luminosities to multicycle timescales based on TSI reconstructions may be possible. We show that the solar luminosity is not constant but varies in phase with the solar cycle. This variation has an amplitude of 0.14% from minimum to maximum for Solar Cycle 23. Considering the energetics in the solar convection zone, it is therefore obvious that a steady-state input from the radiative zone at the solar minimum level would lead to a gradual reduction in the energy content in the convection zone over multicentury timescales. We show that the luminosity at the base of the convection zone should be approximately 0.032% higher than that at the solar surface during solar minimum to maintain net energy equilibrium through the solar cycle. These different energy-input scenarios place constraints on the long-term evolution of the TSI and its impact on the solar forcing of climate variability. These results highlight the convection zone’s role as an energy reservoir on solar-cycle timescales and set constraints for dynamo models intending to understand the long-term evolution of the Sun and solar analogs.

Variability of the Sun’s Luminosity Places Constraints on the Thermal Equilibrium of the Convection Zone

Studies of the Sun and the Earth's atmosphere and climate consider solar variability and its constant monitoring an important driver in climate models. Solar irradiance is one of the main parameters that allow monitoring this variation, which can be studied in spectrum ranges or in its version that integrates all those ranges. Some physical and semi-empirical models were developed and made available and are very relevant for the reconstruction of irradiance in periods of data failure or absence in the collection. However, the solar irradiance prediction could benefit ionospheric and climate models through prior knowledge of irradiance values hours or days ahead, without the need to know or have available other parameters that would be necessary for their reconstruction. This paper presents a neural network based approach, which uses images of the solar photosphere to extract sunspot and active region information and thus generate inputs for recurrent neural networks to perform the irradiance prediction. Experiments were performed with two recurrent neural network architectures for short- and long-term predictions of total and spectral solar irradiance along three wavelengths. The results show good quality of prediction results for TSI and motivate individual effort in improving the prediction of each type of irradiance predicted in this work. The results obtained for SSI point out that photosphere images do not have the same influence on the prediction of all wavelengths tested, but encourage the bet on new spectral lines prediction. The quality closeness in neural networks and physical models results raise the possibility that prediction is an option to be considered in studies for which only reconstructed data are considered.

Amita Muralikrishna

Papers

Mathematical biomarkers for the autonomic regulation of cardiovascular system

Variability of the Sun’s Luminosity Places Constraints on the Thermal Equilibrium of the Convection Zone

Exploring possibilities for solar irradiance prediction from solar photosphere images using recurrent neural networks