Categorizing the Role of Respiration in Cardiovascular and Cerebrovascular Variability Interactions

Unanticipated evolution of cardio-respiratory interactions with cognitive load during a Go-NoGo shooting task in virtual reality

The cardiovascular system interacts continuously with the respiratory system to maintain the vital balance of oxygen and carbon dioxide in our body. The interplay between the sympathetic and parasympathetic branches of the autonomic nervous system regulates the aforesaid involuntary functions. This study analyzes the dynamics of the cardio-respiratory (CR) interactions using RR Intervals (RRI), Systolic Blood Pressure (SBP), and Respiration signals after first-order differencing to make them stationary. It investigates their variation with cognitive load induced by a virtual reality (VR) based Go-NoGo shooting task with low and high levels of task difficulty. We use Pearson's correlation-based linear and mutual information-based nonlinear measures of association to indicate the reduction in RRI-SBP and RRI-Respiration interactions with cognitive load. However, no linear correlation difference was observed in SBP-Respiration interactions with cognitive load, but their mutual information increased. A couple of open-loop autoregressive models with exogenous input (ARX) are estimated using RRI and SBP, and one closed-loop ARX model is estimated using RRI, SBP, and Respiration. The impulse responses (IRs) are derived for each input-output pair, and a reduction in the positive and negative peak amplitude of all the IRs is observed with cognitive load. Some novel parameters are derived by representing the IR as a double exponential curve with cosine modulation and show significant differences with cognitive load compared to other measures, especially for the IR between SBP and Respiration.

Impact of surgical aortic valve replacement and transcatheter aortic valve implantation on cardiovascular and cerebrovascular controls: A pilot study

Surgical aortic valve replacement (SAVR) and transcatheter aortic valve implantation (TAVI) are options in severe aortic valve stenosis (AVS). Cardiovascular (CV) and cerebrovascular (CBV) control markers, derived from variability of heart period, systolic arterial pressure, mean cerebral blood velocity and mean arterial pressure, were acquired in 19 AVS patients (age: 76.8 ± 3.1 yrs, eight males) scheduled for SAVR and in 19 AVS patients (age: 79.9 + 6.5 yrs, 11 males) scheduled for TAVI before (PRE) and after intervention (POST, <7 days). Left ventricular function was preserved in both groups. Patients were studied at supine resting (REST) and during active standing (STAND). We found that: (i) both SAVR and TAVI groups featured a weak pre-procedure CV control; (ii) TAVI ensured better CV control; (iii) cerebral autoregulation was working in PRE in both SAVR and TAVI groups; (iv) SAVR and TAVI had no impact on the CBV control; (v) regardless of group, CV and CBV control markers were not influenced by STAND in POST. Even though the post-procedure preservation of both CV and CBV controls in TAVI group might lead to privilege this procedure in patients at higher risk, the missing response to STAND suggests that this advantage could be insignificant.

Computational modeling for the quantitative assessment of cardiac autonomic response to orthostatic stress

Objective.The autonomic nervous system (ANS) plays a critical role in regulating not only cardiac functions but also various other physiological processes, such as respiratory rate, digestion, and metabolic activities. The ANS is divided into the sympathetic and parasympathetic nervous systems, each of which has distinct but complementary roles in maintaining homeostasis across multiple organ systems in response to internal and external stimuli. Early detection of ANS dysfunctions, such as imbalances between the sympathetic and parasympathetic branches or impairments in the autonomic regulation of bodily functions, is crucial for preventing or slowing the progression of cardiovascular diseases. These dysfunctions can manifest as irregularities in heart rate, blood pressure regulation, and other autonomic responses essential for maintaining cardiovascular health. Traditional methods for analyzing ANS activity, such as heart rate variability (HRV) analysis and muscle sympathetic nerve activity recording, have been in use for several decades. Despite their long history, these techniques face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit their effectiveness in personalized health assessments.Approach.This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments.Main results.The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males, age: 42 ± 18) and diabetic subjects (8 females and 6 males, age: 47 ± 14). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p < 0.05). Additionally, the data suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p < 0.05), characterized by increased responsiveness and intensity of the sympathetic nervous system to stimuli, i.e. fluctuations in blood pressure, leading to more pronounced changes in heart rate, these phenomena can be directly reflected by gain parameters and time response parameters of the model.Significance.The MMARCS model represents an innovative computational approach for quantifying ANS functionality. This model guarantees the accuracy of physiological modeling while reducing mathematical complexity, offering an easy-to-implement and widely applicable tool for clinical measurements of cardiovascular health, disease progression monitoring, and home health monitoring through wearable technology.

Integrative neuro-cardiovascular dynamics in response to test anxiety: A brain-heart axis study

Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neuro-cardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.

Quantification of dynamic cerebral autoregulation: welcome to the jungle!

PURPOSE

Patients with dysautonomia often experience symptoms such as dizziness, syncope, blurred vision and brain fog. Dynamic cerebral autoregulation, or the ability of the cerebrovasculature to react to transient changes in arterial blood pressure, could be associated with these symptoms.

METHODS

In this narrative review, we go beyond the classical view of cerebral autoregulation to discuss dynamic cerebral autoregulation, focusing on recent advances pitfalls and future directions.

RESULTS

Following some historical background, this narrative review provides a brief overview of the concept of cerebral autoregulation, with a focus on the quantification of dynamic cerebral autoregulation. We then discuss the main protocols and analytical approaches to assess dynamic cerebral autoregulation, including recent advances and important issues which need to be tackled.

CONCLUSION

The researcher or clinician new to this field needs an adequate comprehension of the toolbox they have to adequately assess, and interpret, the complex relationship between arterial blood pressure and cerebral blood flow in healthy individuals and clinical populations, including patients with autonomic disorders.

Concomitant evaluation of cardiovascular and cerebrovascular controls via Geweke spectral causality to assess the propensity to postural syncope

The evaluation of propensity to postural syncope necessitates the concomitant characterization of the cardiovascular and cerebrovascular controls and a method capable of disentangling closed loop relationships and decomposing causal links in the frequency domain. We applied Geweke spectral causality (GSC) to assess cardiovascular control from heart period and systolic arterial pressure variability and cerebrovascular regulation from mean arterial pressure and mean cerebral blood velocity variability in 13 control subjects and 13 individuals prone to develop orthostatic syncope. Analysis was made at rest in supine position and during head-up tilt at 60°, well before observing presyncope signs. Two different linear model structures were compared, namely bivariate autoregressive and bivariate dynamic adjustment classes. We found that (i) GSC markers did not depend on the model structure; (ii) the concomitant assessment of cardiovascular and cerebrovascular controls was useful for a deeper comprehension of postural disturbances; (iii) orthostatic syncope appeared to be favored by the loss of a coordinated behavior between the baroreflex feedback and mechanical feedforward pathway in the frequency band typical of the baroreflex functioning during the postural challenge, and by a weak cerebral autoregulation as revealed by the increased strength of the pressure-to-flow link in the respiratory band. GSC applied to spontaneous cardiovascular and cerebrovascular oscillations is a promising tool for describing and monitoring disturbances associated with posture modification.

On the Validity of Single Regression Strategy for Granger Causality Assessment in Cardiovascular and Cardiorespiratory Control Studies

Granger causality (GC) analysis is based on the comparison between prediction error variances computed over the full and restricted models after identifying the coefficients of appropriate vector regressions. GC markers can be computed via a double regression (DR) approach identifying two separate, independent models and a single regression (SR) strategy optimizing the description of the dynamics of the target over the full model and, then, reusing some parts of it in the restricted model. The present study compares the SR and DR strategies over heart period (HP), systolic arterial pressure (SAP) and respiration (R) beat-to-beat series collected during a graded orthostatic challenge induced by head-up tilt in 17 healthy individuals (age: 21-36 yrs; median: 29 yrs; 9 females and 8 males). We found that the DR approach was more powerful than the SR one in detecting the expected stronger involvement of the baroreflex during the challenge, while the expected weaker cardiorespiratory coupling was identified by both SR and DR strategies. The less powerful ability of the SR approach was the result of the greater variance of GC markers compared to the DR strategy. We conclude that, contrary to the suggestions present in literature, the SR approach is not necessarily associated with a smaller dispersion of GC markers. Moreover, we suggest that additional factors, such as the strategy utilized to build embedding spaces and metric utilized to compare prediction error variances, might play an important role in differentiating SR and DR approaches.

On the Different Abilities of Cross-Sample Entropy and K-Nearest-Neighbor Cross-Unpredictability in Assessing Dynamic Cardiorespiratory and Cerebrovascular Interactions

Nonlinear markers of coupling strength are often utilized to typify cardiorespiratory and cerebrovascular regulations. The computation of these indices requires techniques describing nonlinear interactions between respiration (R) and heart period (HP) and between mean arterial pressure (MAP) and mean cerebral blood velocity (MCBv). We compared two model-free methods for the assessment of dynamic HP-R and MCBv-MAP interactions, namely the cross-sample entropy (CSampEn) and k-nearest-neighbor cross-unpredictability (KNNCUP). Comparison was carried out first over simulations generated by linear and nonlinear unidirectional causal, bidirectional linear causal, and lag-zero linear noncausal models, and then over experimental data acquired from 19 subjects at supine rest during spontaneous breathing and controlled respiration at 10, 15, and 20 breaths·minute-1 as well as from 13 subjects at supine rest and during 60° head-up tilt. Linear markers were computed for comparison. We found that: (i) over simulations, CSampEn and KNNCUP exhibit different abilities in evaluating coupling strength; (ii) KNNCUP is more reliable than CSampEn when interactions occur according to a causal structure, while performances are similar in noncausal models; (iii) in healthy subjects, KNNCUP is more powerful in characterizing cardiorespiratory and cerebrovascular variability interactions than CSampEn and linear markers. We recommend KNNCUP for quantifying cardiorespiratory and cerebrovascular coupling.

Cerebral blood flow response to cardiorespiratory oscillations in healthy humans

Dynamic cerebral autoregulation (CA) characterizes the cerebral blood flow (CBF) response to abrupt changes in arterial blood pressure (ABP). CA operates at frequencies below 0.15 Hz. ABP regulation and probably CA are modified by autonomic nervous activity. We investigated the CBF response and CA dynamics to mild increase in sympathetic activity. Twelve healthy volunteers underwent oscillatory lower body negative pressure (oLBNP), which induced respiratory-related ABP oscillations at an average of 0.22 Hz. We recorded blood velocity in the internal carotid artery (ICA) by Doppler ultrasound and ABP. We quantified variability and peak wavelet power of ABP and ICA blood velocity by wavelet analysis at low frequency (LF, 0.05-0.15 Hz) and Mayer waves (0.08-0.12 Hz), respectively. CA was quantified by calculation of the wavelet synchronization gamma index for the pair ABP-ICA blood velocity in the LF and Mayer wave band. oLBNP increased ABP peak wavelet power at the Mayer wave frequency. At the Mayer wave, ABP peak wavelet power increased by >70 % from rest to oLBNP (p < 0.05), while ICA blood flow velocity peak wavelet power was unchanged, and gamma index increased (from 0.49 to 0.69, p < 0.05). At LF, variability in both ABP and ICA blood velocity and gamma index were unchanged from rest to oLBNP. Despite an increased gamma index at Mayer wave, ICA blood flow variability was unchanged during increased ABP variability. The increased synchronization during oLBNP did not cause less stable CBF or less active CA. Sympathetic activation seems to improve the mechanisms of CA.

Dynamic effects of cholinergic blockade upon cerebral blood flow autoregulation in healthy adults

Background: Cerebral flow autoregulation (CFA) is a homeostatic mechanism critical for survival. The autonomic nervous system (ANS) plays a key role in maintaining proper CFA function. More quantitative studies of how the ANS influences CFA are desirable. Objective: To discover and quantify the dynamic effects of cholinergic blockade upon CFA in response to changes of arterial blood pressure and blood CO2 tension in healthy adults. Methods: We analyzed time-series data of spontaneous beat-to-beat mean arterial blood pressure (ABP) and cerebral blood flow velocity in the middle cerebral arteries (CFV), as well as breath-to-breath end-tidal CO2 (CO2), collected in 9 adults before and after cholinergic blockade, in order to obtain subject-specific predictive input-output models of the dynamic effects of changes in ABP and CO2 (inputs) upon CFV (output). These models are defined in convolutional form using "kernel" functions (or, equivalently, Transfer Functions in the frequency domain) that are estimated via the robust method of Laguerre expansions. Results: Cholinergic blockade caused statistically significant changes in the obtained kernel estimates (and the corresponding Transfer Functions) that define the linear dynamics of the ABP-to-CFV and CO2-to-CFV causal relations. The kernel changes due to cholinergic blockade reflect the effects of the cholinergic mechanism and exhibited, in the frequency domain, resonant peaks at 0.22 Hz and 0.06 Hz for the ABP-to-CFV and CO2-to-CFV dynamics, respectively. Conclusion: Quantitative estimates of the dynamics of the cholinergic component in CFA are found as average changes of the ABP-to-CFV and CO2-to-CFV kernels, and corresponding Transfer Functions, before and after cholinergic blockade.

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.

Respiratory Sinus Arrhythmia is Mainly Driven by Central Feedforward Mechanisms in Healthy Humans

Heart rate variability (HRV) has prognostic and diagnostic potential, however, the mechanisms behind respiratory sinus arrhythmia (RSA), a main short-term HRV, are still not well understood. We investigated if the central feedforward mechanism or pulmonary stretch reflex contributed most to RSA in healthy humans. Ventilatory support reduces the centrally mediated respiratory effort but remains the inspiratory stretch of the pulmonary receptors. We aimed to quantify the difference in RSA between spontaneous breathing and ventilatory support. Nineteen healthy, young subjects underwent spontaneous breathing and non-invasive intermittent positive pressure ventilation (NIV) while we recorded heart rate (HR, from ECG), mean arterial pressure (MAP) and stroke volume (SV) estimated from the non-invasive finger arterial pressure curve, end-tidal CO2 (capnograph), and respiratory frequency (RF) with a stretch band. Variability was quantified by an integral between 0.15–0.4 Hz calculated from the power spectra. Median and 95% confidence intervals (95%CI) were calculated as Hodges–Lehmann’s one-sample estimator. Statistical difference was calculated by the Wilcoxon matched-pairs signed-rank test. RF and end-tidal CO2 were unchanged by NIV. NIV reduced HR by 2 bpm, while MAP and SV were unchanged in comparison to spontaneous breathing. Variability in both HR and SV was reduced by 60% and 75%, respectively, during NIV as compared to spontaneous breathing, but their interrelationship with respiration was maintained. NIV reduced RSA through a less central respiratory drive, and pulmonary stretch reflex contributed little to RSA. RSA is mainly driven by a central feedforward mechanism in healthy humans. Peripheral reflexes may contribute as modifiers of RSA.

Dynamic Temporal Relationship Between Autonomic Function and Cerebrovascular Reactivity in Moderate/Severe Traumatic Brain Injury

There has been little change in morbidity and mortality in traumatic brain injury (TBI) in the last 25 years. However, literature has emerged linking impaired cerebrovascular reactivity (a surrogate of cerebral autoregulation) with poor outcomes post-injury. Thus, cerebrovascular reactivity (derived through the pressure reactivity index; PRx) is emerging as an important continuous measure. Furthermore, recent literature indicates that autonomic dysfunction may drive impaired cerebrovascular reactivity in moderate/severe TBI. Thus, to improve our understanding of this association, we assessed the physiological relationship between PRx and the autonomic variables of heart rate variability (HRV), blood pressure variability (BPV), and baroreflex sensitivity (BRS) using time-series statistical methodologies. These methodologies include vector autoregressive integrative moving average (VARIMA) impulse response function analysis, Granger causality, and hierarchical clustering. Granger causality testing displayed inconclusive results, where PRx and the autonomic variables had varying bidirectional relationships. Evaluating the temporal profile of the impulse response function plots demonstrated that the autonomic variables of BRS, ratio of low/high frequency of HRV and very low frequency HRV all had a strong relation to PRx, indicating that the sympathetic autonomic response may be more closely linked to cerebrovascular reactivity, then other variables. Finally, BRS was consistently associated with PRx, possibly demonstrating a deeper relationship to PRx than other autonomic measures. Taken together, cerebrovascular reactivity and autonomic response are interlinked, with a bidirectional impact between cerebrovascular reactivity and circulatory autonomics. However, this work is exploratory and preliminary, with further study required to extract and confirm any underlying relationships.