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 ${\mathrm{b}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{h}\mathrm{s}·\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{u}\mathrm{t}\mathrm{e}}^{-1}$ 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.

Objective. Closed loop cardiovascular (CV) and cerebrovascular (CBV) variability interactions are assessed via transfer entropy (TE) from systolic arterial pressure (SAP) to heart period (HP) and vice versa and from mean arterial pressure (MAP) to mean cerebral blood velocity (MCBv) and vice versa. This analysis is exploited to assess the efficiency of baroreflex and cerebral autoregulation. This study aims at characterizing CV and CBV controls in postural orthostatic tachycardiac syndrome (POTS) subjects experiencing exaggerated sympathetic response during orthostatic challenge via unconditional TE and TE conditioned on respiratory activity ( R ). Approach. In 18 healthy controls (age: 28 ± 13 yrs; 5 males, 13 females) and 15 POTS individuals (age: 29 ± 11 yrs; 3 males, 12 females) we acquired beat-to-beat variability of HP, SAP, MAP and MCBv and two R signals, namely respiratory chest movement (RCM) and capnogram (CAP). Recordings were made at sitting rest and during active standing (STAND). TE was computed via vector autoregressive approach. Main results. We found that: (i) when assessing CV interactions, the increase of the TE from SAP to HP during STAND, indicating baroreflex activation, is detected solely when conditioning on RCM; (ii) when assessing CBV interactions, the impact of R on the TE computation is negligible; (iii) POTS shows baroreflex impairment during STAND; (iv) POTS exhibits a normal CBV response to STAND. Significance. TE is useful for detecting the impairment of specific regulatory mechanisms in POTS. Moreover, using different R signals highlights the sensitivity of CV and CBV controls to specific R aspects.

We tested the validity of the state space correspondence (SSC) strategy based on k-nearest neighbor cross-predictability (KNNCP) to assess the directionality of coupling in stochastic nonlinear bivariate autoregressive (NBAR) processes. The approach was applied to assess closed-loop cardiorespiratory interactions between heart period (HP) variability and respiration (R) during a controlled respiration (CR) protocol in 19 healthy humans (aged from 27 to 35 yrs, 11 females) and during active standing (STAND) in 25 athletes (aged from 20 to 40 yrs, all men) and 25 non-athletes (aged from 20 to 40 yrs, all men). Over simulated NBAR processes, we found that (i) the SSC approach can detect the correct causal relationship as the direction leads to better KNNCP from the past of the driver to the future state of the target and (ii) simulations suggest that the ability of the method is preserved in any condition of complexity of the interacting series. Over CR and STAND protocols, we found that (a) slowing the breathing rate increases the strength of the causal relationship in both temporal directions in a balanced modality; (b) STAND is more powerful in modulating the coupling strength on the pathway from HP to R; (c) regardless of protocol and experimental condition, the strength of the link from HP to R is stronger than that from R to HP; (d) significant causal relationships in both temporal directions are found regardless of the level of complexity of HP variability and R. The SSC strategy is useful to disentangle closed-loop cardiorespiratory interactions.

Temporal asymmetry is a peculiar aspect of heart period (HP) variability (HPV). HPV asymmetry (HPVA) is reduced with aging and pathology, but its origin is not fully elucidated. Given the impact of respiration on HPV resulting in the respiratory sinus arrhythmia (RSA) and the asymmetric shape of the respiratory pattern, a possible link between HPVA and RSA might be expected. In this study we tested the hypothesis that HPVA is significantly associated with RSA and asymmetry of the respiratory rhythm. We studied 42 middle-aged healthy (H) subjects, and 56 chronic heart failure (CHF) patients of whom 26 assigned to the New York Heart Association (NYHA) class II (CHF-II) and 30 to NYHA class III (CHF-III). Electrocardiogram and lung volume were monitored for 8 minutes during spontaneous breathing (SB) and controlled breathing (CB) at 15 breaths/minute. The ratio of inspiratory (INSP) to expiratory (EXP) phases, namely the I/E ratio, and RSA were calculated. HPVA was estimated as the percentage of negative HP variations, traditionally measured via the Porta’s index (PI). Departures of PI from 50% indicated HPVA and its significance was tested via surrogate data. We found that RSA increased during CB and I/E ratio was smaller than 1 in all groups and experimental conditions. In H subjects the PI was about 50% during SB and it increased significantly during CB. In both CHF-II and CHF-III groups the PI was about 50% during SB and remained unmodified during CB. The PI was uncorrelated with RSA and I/E ratio regardless of the experimental condition and group. Pooling together data of different experimental conditions did not affect conclusions. Therefore, we conclude that the HPVA cannot be explained by RSA and/or I/E ratio, thus representing a peculiar feature of the cardiac control that can be aroused in middle-aged H individuals via CB.

Baroreflex is commonly typified from heart period (HP) and systolic arterial pressure (SAP) spontaneous variations in the frequency domain mainly by estimating its sensitivity. However, an informative parameter linked to the rapidity of the HP response to SAP changes, such as the baroreflex bandwidth, remains unquantified. We propose a model-based parametric approach for estimating the baroreflex bandwidth from the impulse response function (IRF) of the HP-SAP transfer function (TF). The approach accounts explicitly for the action of mechanisms modifying HP regardless of SAP changes. The method was tested during graded baroreceptor unloading induced by head-up tilt (HUT) at 15°, 30°, 45°, 60°, and 75° (T15, T30, T45, T60, and T75) in 17 healthy individuals (age: 21–36 yr; 9 females and 8 males) and during baroreceptor loading obtained via head-down tilt (HDT) at −25° in 13 healthy men (age: 41–71 yr). The bandwidth was estimated as the decay constant of the monoexponential IRF fitting. The method was robust because the monoexponential fitting described adequately the HP dynamics following an impulse of SAP. We observed that 1) baroreflex bandwidth is reduced during graded HUT and this narrowing is accompanied by the reduction of the bandwidth of mechanisms that modify HP regardless of SAP changes and 2) baroreflex bandwidth is not affected by HDT but that of SAP-unrelated mechanisms becomes wider. This study provides a method for estimating a baroreflex feature that provides different information compared with the more usual baroreflex sensitivity while accounting explicitly for the action of mechanisms changing HP irrespective of SAP.

Objective. Aortic valve stenosis (AVS) induces left ventricular function adaptations and surgical aortic valve replacement (SAVR) restores blood flow profile across aortic valve. Modifications of cardiac hemodynamics induced by AVS and SAVR might alter cardiovascular (CV) and cerebrovascular (CBV) controls. The study aims at characterizing CV and CBV regulations one day before SAVR (PRE), within one week after SAVR (POST), and after a three-month follow-up (POST3) in 73 AVS patients (age: 63.9 ± 12.9 yrs; 48 males, 25 females) from spontaneous fluctuations of heart period (HP), systolic arterial pressure, mean arterial pressure and mean cerebral blood velocity. Approach. CV and CBV regulations were typified via a bivariate autoregressive approach computing traditional frequency domain markers and causal squared coherence (CK ²) from CV and CBV variabilities. Univariate time and frequency domain indexes were calculated as well. Analyses were carried out in frequency bands typical of CV and CBV controls at supine rest and during active standing. A surrogate method was exploited to check uncoupling condition. Main results. We found that: (i) CV regulation is impaired in AVS patients; (ii) CV regulation worsens in POST; (iii) CV regulation recovers in POST3 and CV response to active standing is even better than in PRE; (iv) CBV regulation is preserved in AVS patients; (v) SAVR does not affect CBV control; (vi) parameters of the CBV control in POST3 and PRE are similar. Significance. CK ² is particularly useful to characterize CV and CBV controls in AVS patients and to monitor of patient’s evolution after SAVR.