PHYSIOLOGY CASE STUDY

In this study— to advance towards the goals of personalized medicine, Aimdyn used a computational physiology approach to better understand how cardiovascular processes change when the baroreflex mechanism is challenged by resonance breathing.

This study has potential applications for clinical intervention and, if replicated in clinical samples, may be useful for matching persons to the more effective or targeted interventions.

The baroreflex is one of the body's homeostatic mechanisms that helps to maintain blood pressure at nearly constant levels.

Heart rate variability biofeedback intervention involves slow breathing at a rate of 6 breaths per minute (resonance breathing) to maximize respiratory and baroreflex effects on heart period oscillations.
This intervention has wide-ranging clinical benefits and is gaining empirical support as an adjunct therapy for biobehavioral disorders, including asthma and depression.

Yet, little is known about the system-level cardiovascular changes that occur during resonance breathing or the extent to which individuals differ in cardiovascular benefit. This study used a computational physiology approach to dynamically model the human cardiovascular system at rest and during resonance breathing. Noninvasive measurements of heart period, beat-to-beat systolic and diastolic blood pressure, and respiration period were obtained from 24 healthy young men and women.  

A model with respiration as input was parameterized to better understand how the cardiovascular processes that control variability in heart period and blood pressure change from rest to resonance breathing. The model includes mathematical descriptions of a pulsating heart, as well as the mechanics of blood flow and baroreflex activation. It consists of a system of delay differential equations and was adapted in this study to be applicable for repeated measurements on the same person under different conditions (resting state, resonance breathing).
The model includes more than 90 parameters and 21 states (pressures, flows, volumes, resistances, and elastances). Twenty-one delay differential equations reflect conservation of mass and balance of forces at arteries and veins, as well as delayed physiological responses to vagal and sympathetic neural activity. This allows for simulation of high-resolution blood pressure and heart period as a function of time. The parameters were estimated for each participant individually, at rest and during the breathing challenge, by finding the sensitive model parameters that provided the closest match between the model outputs and the experimental data collected during baseline and 6 breaths/min tasks.
By using GoSUM software, the global sensitivity analysis was performed for all subjects and all tasks in order to select the parameters for model optimization. All 80 nonconstant model parameters were assumed to be uncertain with a uniform distribution and a range of 10% from the initial value. A similar pattern of relative parameter importance was observed for all 24 participants for all tasks. The twelve parameters that were among the upper quartile of parameters across all tasks and that were conceptually relevant to baroreflex functioning were chosen as the optimization parameters. The cost optimization function used in model calibration corresponded to the difference between the experimental data and model outputs. A good match was observed between the data and model outputs (heart period, blood pressure, and corresponding power spectral densities).

Paired t-tests were conducted to examine whether statistically significant changes occurred within individuals in several important model parameters between the baseline tasks and resonance breathing task.

We found that splanchnic peripheral compliance, arterial receptor gain on sympathetic control of heart period, and minimum left ventricular elastane significantly increased during the resonance breathing task compared with both baselines.