1,546 research outputs found

    A multivariate time-frequency method to characterize the influence of respiration over heart period and arterial pressure

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    Respiratory activity introduces oscillations both in arterial pressure and heart period, through mechanical and autonomic mechanisms. Respiration, arterial pressure, and heart period are, generally, non-stationary processes and the interactions between them are dynamic. In this study we present a methodology to robustly estimate the time course of cross spectral indices to characterize dynamic interactions between respiratory oscillations of heart period and blood pressure, as well as their interactions with respiratory activity. Time-frequency distributions belonging to Cohen's class are used to estimate time-frequency (TF) representations of coherence, partial coherence and phase difference. The characterization is based on the estimation of the time course of cross spectral indices estimated in specific TF regions around the respiratory frequency. We used this methodology to describe the interactions between respiration, heart period variability (HPV) and systolic arterial pressure variability (SAPV) during tilt table test with both spontaneous and controlled respiratory patterns. The effect of selective autonomic blockade was also studied. Results suggest the presence of common underling mechanisms of regulation between cardiovascular signals, whose interactions are time-varying. SAPV changes followed respiratory flow both in supine and standing positions and even after selective autonomic blockade. During head-up tilt, phase differences between respiration and SAPV increased. Phase differences between respiration and HPV were comparable to those between respiration and SAPV during supine position, and significantly increased during standing. As a result, respiratory oscillations in SAPV preceded respiratory oscillations in HPV during standing. Partial coherence was the most sensitive index to orthostatic stress. Phase difference estimates were consistent among spontaneous and controlled breathing patterns, whereas coherence was higher in spontaneous breathing. Parasympathetic blockade did not affect interactions between respiration and SAPV, reduced the coherence between SAPV and HPV and between respiration and HPV. Our results support the hypothesis that non-autonomic, possibly mechanically mediated, mechanisms also contributes to the respiratory oscillations in HPV. A small contribution of sympathetic activity on HPV-SAPV interactions around the respiratory frequency was also observed

    Internetwork and intranetwork communications during bursting dynamics: Applications to seizure prediction

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    We use a simple dynamical model of two interacting networks of integrate-and-fire neurons to explain a seemingly paradoxical result observed in epileptic patients indicating that the level of phase synchrony declines below normal levels during the state preceding seizures (preictal state). We model the transition from the seizure free interval (interictal state) to the seizure (ictal state) as a slow increase in the mean depolarization of neurons in a network corresponding to the epileptic focus. We show that the transition from the interictal to preictal and then to the ictal state may be divided into separate dynamical regimes: the formation of slow oscillatory activity due to resonance between the two interacting networks observed during the interictal period, structureless activity during the preictal period when the two networks have different properties, and bursting dynamics driven by the network corresponding to the epileptic focus. Based on this result, we hypothesize that the beginning of the preictal period marks the beginning of the transition of the epileptic network from normal activity toward seizing

    Assessment of quadratic nonlinear cardiorespiratory couplings during tilt table test by means of real wavelet biphase

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    In this paper a method for assessment of Quadratic Phase Coupling (QPC) between respiration and Heart Rate Variability (HRV) is presented. Methods: First, a method for QPC detection is proposed named Real Wavelet Biphase (RWB). Then, a method for QPC quantification is proposed based on the Normalized Wavelet Biamplitude (NWB). A simulation study has been conducted to test the reliability of RWB to identify QPC, even in the presence of constant delays between interacting oscillations, and to discriminate it from Quadratic Phase Uncoupling. Significant QPC was assessed based on surrogate data analysis. Then, quadratic cardiorespiratory couplings were studied during a tilt table test protocol of 17 young healthy subjects. Results: Simulation study showed that RWB is able to detect even weak QPC with delays in the range of 0 - 2 s, which are usual in the Autonomic Nervous System (ANS) control of heart rate. Results from the database revealed a significant reduction (p<0.05) of NWB between respiration and both low and high frequencies of HRV in head-up tilt position compared to early supine. Conclusion: The proposed technique detects and quantifies robustly QPC and is able to track the coupling between respiration and various HRV components during ANS changes. Significance: The proposed method can help to assess alternations of nonlinear cardiorespiratory interactions related to ANS dysfunction and physiological regulation of HRV in cardiovascular diseases

    Analysis and Modelling of Multimodal Interactions in Renal Autoregulation

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    By maintaining the volume and composition of the body fluids within narrow bounds and by producing a set of hormones that affect the blood vessels, the kidneys provide important long-term regulation of the blood pressure. Disturbances of kidney function can cause hypertension, a prevalent disease in modern societies. The kidneys protect their own function against short-term variations in the blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms: the tubuloglomerular feedback, which regulates the incoming blood flow in response to variations of the NaCl concentration of the tubular fluid near the terminal point of the loop of Henle (macula densa), and a myogenic mechanism by which the afferent arteriole regulates its diameter in response to variations in its transmural pressure. Experimentally, both of these mechanisms are found to produce oscillations. In the present study, analysis of experimental data of the tubular pressure and arteriole blood flow in combination with mechanism-based modelling has been used to answer the following questions: (i) How to reveal and characterize interactions between the two mechanisms of renal autoregulation? (ii) To what extend does nephron-to-nephron communication lead to cooperative behaviour? and (iii) How do intra- and inter-nephron interactions differ in normotensive and hypertensive rats? Analysis of experimental data revealed the presence of amplitude and frequency modulation, i.e. the regulation is provided not only by a change in the diameters of the active parts of the vessels, but also by an adjustment of the frequency of the myogenic oscillations. Interaction between the two mechanisms of renal autoregulation was found to be significantly stronger in spontaneously hypertensive rats than in normotensive rats. Synchronization phenomena in neighbouring nephrons were evaluated by measuring both frequency and phase entrainment. Statistical analysis showed that synchronization among mechanisms of renal autoregulation is reduced in hypertensive rats. With a probability exceeding 80%, normotensive rats demonstrated full entrainment in neighbouring nephrons where the oscillatory modes associated with two mechanisms of autoregulation were synchronized. Hypertensive rats displayed about half the probability of full synchronization and about twice the probability of partial synchronization, i.e. a state where neighbouring nephrons synchronize their slow tubuloglomerular feedback dynamics, while the fast myogenic dynamics remain desynchronized, or vice versa. Spontaneously hypertensive rats generally remained in synchrony for only 1/3 to 1/2 as long as the normotensive ones. Numerical simulations with a model of superficial nephrons connected via a flow mediated hemodynamic coupling and a vascular propagated coupling reproduced the experimentally observed patterns of behaviour. Lack of synchronization may be responsible for the development of irregular dynamics in the tubules of rats with experimental hypertension. The model has been extended by including deep nephrons for which it has not yet been possible to perform similar experimental measurements. Using available anatomical and physiological information we constructed a model of an nephron-vascular ensemble including superficial as well as deep nephrons with different length of loop of Henle. The computer simulation suggested that irregular dynamics of nephron ensemble increases at higher arterial pressures and values of the coupling strength. The model showed that, for physiologically reasonable parameter values, the deep nephrons do not synchronize with the superficial nephrons even though they are coupled via the same blood supply

    Brain circuits involved in self-paced motion: the influence of 0.1 Hz waves

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    The neural mechanisms behind human voluntary motion are not fully characterized yet, in spite of numerous research studies. Slow ( 0.1 Hz) brain oscillations are known to have a powerful modulatory effect on several cognitive and physiological phenomena, including free movement. This study is based on fMRI data acquired from 25 young, healthy subjects. The tasks were: rest, self-paced motion, motion paced by a periodic 0.1 Hz stimulus. The temporal resolution was finer than standard fMRI protocols (TR=871 ms). After preprocessing, the signal from brain regions of interest was extracted, and functional connectivity was computed between brain regions using wavelet phase coherence. Complementarily, effective connectivity was measured using Granger causality. The final output was Phase-Locking (PL) and Granger Causality (GC) matrices reflecting inter-regional phase coherence and causal interactions, respectively, around 0.1 Hz. Using the GraphVar toolbox, inter-task and inter-group comparisons were performed. In inter-task comparisons PL matrices showed encouraging results unlike GC matrices. Pairs of regions for which PL differs significantly between rest and self-paced movement were identified. These include mainly the Postcentral gyrus, Putamen, the Anterior Cingulum, the Precentral gyrus, the Calcarine, the Lingual and the Insula (all in the left hemisphere). Topological changes in the brain wiring were identified across the tasks by computing the node degree and global efficiency. Inter-group comparisons took into account the inter movement interval and the coupling between BOLD and heart rate beatto-beat interval signals and showed changes in brain activity depending on the regularity of movement intervals and specific connectivity patterns for neural BOLD oscillations, respectively. This methodological approach allowed to make a contribution towards the characterization of the functional connectivity of brain circuits related to voluntary motor behavior

    Symbolic Dynamic Analysis of Relations Between Cardiac and Breathing Cycles in Patients on Weaning Trials

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    Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure

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

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    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
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