Metrics of high cofluctuation and entropy to describe control of cardiac function in the stellate ganglion.

Stellate ganglia within the intrathoracic cardiac control system receive and integrate central, peripheral, and cardiopulmonary information to produce postganglionic cardiac sympathetic inputs. Pathological anatomical and structural remodeling occurs within the neurons of the stellate ganglion (SG) in the setting of heart failure (HF). A large proportion of SG neurons function as interneurons whose networking capabilities are largely unknown. Current therapies are limited to targeting sympathetic activity at the cardiac level or surgical interventions such as stellectomy, to treat HF. Future therapies that target the SG will require understanding of their networking capabilities to modify any pathological remodeling. We observe SG networking by examining cofluctuation and specificity of SG networked activity to cardiac cycle phases. We investigate network processing of cardiopulmonary transduction by SG neuronal populations in porcine with chronic pacing-induced HF and control subjects during extended in-vivo extracellular microelectrode recordings. We find that information processing and cardiac control in chronic HF by the SG, relative to controls, exhibits: (i) more frequent, short-lived, high magnitude cofluctuations, (ii) greater variation in neural specificity to cardiac cycles, and (iii) neural network activity and cardiac control linkage that depends on disease state and cofluctuation magnitude.

Studying Cardiac Neural Network Dynamics: Challenges and Opportunities for Scientific Computing.

Neural control of the heart involves continuous modulation of cardiac mechanical and electrical activity to meet the organism's demand for blood flow. The closed-loop control scheme consists of interconnected neural networks with central and peripheral components working cooperatively with each other. These components have evolved to cooperate control of various aspects of cardiac function, which produce measurable "functional" outputs such as heart rate and blood pressure. In this review, we will outline fundamental studies probing the cardiac neural control hierarchy. We will discuss how computational methods can guide improved experimental design and be used to probe how information is processed while closed-loop control is operational. These experimental designs generate large cardio-neural datasets that require sophisticated strategies for signal processing and time series analysis, while presenting the usual large-scale computational challenges surrounding data sharing and reproducibility. These challenges provide unique opportunities for the development and validation of novel techniques to enhance understanding of mechanisms of cardiac pathologies required for clinical implementation.

A novel metric linking stellate ganglion neuronal population dynamics to cardiopulmonary physiology.

Cardiopulmonary sympathetic control is exerted via stellate ganglia (SG); however, little is known about how neuronal firing patterns in the stellate ganglion relate to dynamic physiological function in the heart and lungs. We performed continuous extracellular recordings from SG neurons using multielectrode arrays in chloralose-anesthetized pigs (n = 6) for 8-9 h. Respiratory and left ventricular pressures (RP and LVP, respectively) and the electrocardiogram (ECG) were recorded concomitantly. Linkages between sampled spikes and LVP or RP were determined using a novel metric to evaluate specificity in neural activity for phases of the cardiac and pulmonary cycles during resting conditions and under various cardiopulmonary stressors. Firing frequency (mean 4.6 ± 1.2 Hz) varied spatially across the stellate ganglion, suggesting regional processing. The firing pattern of most neurons was synchronized with both cardiac (LVP) and pulmonary (RP) activity indicative of cardiopulmonary integration. Using the novel metric to determine cardiac phase specificity of neuronal activity, we found that spike density was highest during diastole and near-peak systole. This specificity was independent of the actual LVP or population firing frequency as revealed by perturbations to the LVP. The observed specificity was weaker for RP. Stellate ganglion neuronal populations exhibit cardiopulmonary integration and profound specificity toward the near-peak systolic phase of the cardiac cycle. This novel approach provides practically deployable tools to probe stellate ganglion function and its relationship to cardiopulmonary pathophysiology.NEW & NOTEWORTHY Activity of stellate ganglion neurons is often linking indirectly to cardiac function. Using novel approaches coupled with extended period of recordings in large animals, we link neuronal population dynamics to mechanical events occurring at near-peak systole. This metric can be deployed to probe stellate ganglion neuronal control of cardiopulmonary function in normal and disease states.

Closed-form approximation of symmetric thin-film multi-layer plasmonic dispersion equation solutions.

An original asymptotic method is developed and used to find closed-form approximations to the symmetric thin-film three- and multi-layer plasmonic dispersion equations. Closed-form analysis of three-layer metal-insulator-metal (MIM: "M" is metal and "I" is insulator) and IMI devices shows a complementary physics underpinning their properties. Analysis of multi-layer symmetric devices, considered for a seven-layer MIMIMIM example, uncovers a remarkable departure from the physics governing MIM and IMI features. Multi-layer propagation length and attenuation are determined by proximity, in the space of cladding thickness and wavelength, to singularities that exist in the limit of vanishing imaginary part of the cladding dielectric constant. Exploitation of this phenomenon will expand the development of a broader range of thin-film applications in optoelectronics.

Recurrent myocardial infarction: Mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control.

The cardiac nervous system continuously controls cardiac function whether or not pathology is present. While myocardial infarction typically has a major and catastrophic impact, population studies have shown that longer-term risk for recurrent myocardial infarction and the related potential for sudden cardiac death depends mainly upon standard atherosclerotic variables and autonomic nervous system maladaptations. Investigative neurocardiology has demonstrated that autonomic control of cardiac function includes local circuit neurons for networked control within the peripheral nervous system. The structural and adaptive characteristics of such networked interactions define the dynamics and a new normal for cardiac control that results in the aftermath of recurrent myocardial infarction and/or unstable angina that may or may not precipitate autonomic derangement. These features are explored here via a mathematical model of cardiac regulation. A main observation is that the control environment during pathology is an extrapolation to a setting outside prior experience. Although global bounds guarantee stability, the resulting closed-loop dynamics exhibited while the network adapts during pathology are aptly described as 'free-floating' in order to emphasize their dependence upon details of the network structure. The totality of the results provide a mechanistic reasoning that validates the clinical practice of reducing sympathetic efferent neuronal tone while aggressively targeting autonomic derangement in the treatment of ischemic heart disease.

Vagal nerve stimulation therapy: what is being stimulated?

Vagal nerve stimulation in cardiac therapy involves delivering electrical current to the vagal sympathetic complex in patients experiencing heart failure. The therapy has shown promise but the mechanisms by which any benefit accrues is not understood. In this paper we model the response to increased levels of stimulation of individual components of the vagal sympathetic complex as a differential activation of each component in the control of heart rate. The model provides insight beyond what is available in the animal experiment in as much as allowing the simultaneous assessment of neuronal activity throughout the cardiac neural axis. The results indicate that there is sensitivity of the neural network to low level subthreshold stimulation. This leads us to propose that the chronic effects of vagal nerve stimulation therapy lie within the indirect pathways that target intrinsic cardiac local circuit neurons because they have the capacity for plasticity.

Well-conditioned model predictive control.

Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also computationally demanding which makes them less suited for tight control of fast processes. In this paper, a new dynamic matrix control (DMC) algorithm is proposed that reduces inherent ill-conditioning by allowing the process prediction time step to exceed the control time step. The main feature, that stands in contrast with current DMC approaches, is that the original open-loop data are used to evaluate a "shifting factor" m in the controller matrix where m replaces the move suppression coefficient. The new control algorithm is practically demonstrated on a fast reacting process with better control being realized in comparison with DMC using move suppression. The algorithm also gives improved closed-loop responses for control simulations on a multivariable nonlinear process having variable dead-time, and on other models found in the literature. The shifting factor m is generic and can be effectively applied for any control horizon.