Cross-correlation of instantaneous phase increments in pressure-flow fluctuations: applications to cerebral autoregulation

Network of muscle fibers activation facilitates inter-muscular coordination, adapts to fatigue and reflects muscle function

Fundamental movement patterns require continuous skeletal muscle coordination, where muscle fibers with different timing of activation synchronize their dynamics across muscles with distinct functions. It is unknown how muscle fibers integrate as a network to generate and fine tune movements. We investigate how distinct muscle fiber types synchronize across arm and chest muscles, and respond to fatigue during maximal push-up exercise. We uncover that a complex inter-muscular network of muscle fiber cross-frequency interactions underlies push-up movements. The network exhibits hierarchical organization (sub-networks/modules) with specific links strength stratification profile, reflecting distinct functions of muscles involved in push-up movements. We find network reorganization with fatigue where network modules follow distinct phase-space trajectories reflecting their functional role and adaptation to fatigue. Consistent with earlier observations for squat movements under same protocol, our findings point to general principles of inter-muscular coordination for fundamental movements, and open a new area of research, Network Physiology of Exercise.

Network Physiology of Exercise: Beyond Molecular and Omics Perspectives

Molecular Exercise Physiology and Omics approaches represent an important step toward synthesis and integration, the original essence of Physiology. Despite the significant progress they have introduced in Exercise Physiology (EP), some of their theoretical and methodological assumptions are still limiting the understanding of the complexity of sport-related phenomena. Based on general principles of biological evolution and supported by complex network science, this paper aims to contrast theoretical and methodological aspects of molecular and network-based approaches to EP. After explaining the main EP challenges and why sport-related phenomena cannot be understood if reduced to the molecular level, the paper proposes some methodological research advances related to the type of studied variables and measures, the data acquisition techniques, the type of data analysis and the assumed relations among physiological levels. Inspired by Network Physiology, Network Physiology of Exercise provides a new paradigm and formalism to quantify cross-communication among diverse systems across levels and time scales to improve our understanding of exercise-related phenomena and opens new horizons for exercise testing in health and disease.

Modeling Tumor Disease and Sepsis by Networks of Adaptively Coupled Phase Oscillators

In this study, we provide a dynamical systems perspective to the modelling of pathological states induced by tumors or infection. A unified disease model is established using the innate immune system as the reference point. We propose a two-layer network model for carcinogenesis and sepsis based upon the interaction of parenchymal cells and immune cells via cytokines, and the co-evolutionary dynamics of parenchymal, immune cells, and cytokines. Our aim is to show that the complex cellular cooperation between parenchyma and stroma (immune layer) in the physiological and pathological case can be qualitatively and functionally described by a simple paradigmatic model of phase oscillators. By this, we explain carcinogenesis, tumor progression, and sepsis by destabilization of the healthy homeostatic state (frequency synchronized), and emergence of a pathological state (desynchronized or multifrequency cluster). The coupled dynamics of parenchymal cells (metabolism) and nonspecific immune cells (reaction of innate immune system) are represented by nodes of a duplex layer. The cytokine interaction is modeled by adaptive coupling weights between the nodes representing the immune cells (with fast adaptation time scale) and the parenchymal cells (slow adaptation time scale) and between the pairs of parenchymal and immune cells in the duplex network (fixed bidirectional coupling). Thereby, carcinogenesis, organ dysfunction in sepsis, and recurrence risk can be described in a correct functional context.

Critical Parameters in Dynamic Network Modeling of Sepsis

In this work, we propose a dynamical systems perspective on the modeling of sepsis and its organ-damaging consequences. We develop a functional two-layer network model for sepsis based upon the interaction of parenchymal cells and immune cells via cytokines, and the coevolutionary dynamics of parenchymal, immune cells, and cytokines. By means of the simple paradigmatic model of phase oscillators in a two-layer system, we analyze the emergence of organ threatening interactions between the dysregulated immune system and the parenchyma. We demonstrate that the complex cellular cooperation between parenchyma and stroma (immune layer) either in the physiological or in the pathological case can be related to dynamical patterns of the network. In this way we explain sepsis by the dysregulation of the healthy homeostatic state (frequency synchronized) leading to a pathological state (desynchronized or multifrequency cluster) in the parenchyma. We provide insight into the complex stabilizing and destabilizing interplay of parenchyma and stroma by determining critical interaction parameters. The coupled dynamics of parenchymal cells (metabolism) and nonspecific immune cells (response of the innate immune system) is represented by nodes of a duplex layer. Cytokine interaction is modeled by adaptive coupling weights between nodes representing immune cells (with fast adaptation timescale) and parenchymal cells (slow adaptation timescale), and between pairs of parenchymal and immune cells in the duplex network (fixed bidirectional coupling). The proposed model allows for a functional description of organ dysfunction in sepsis and the recurrence risk in a plausible pathophysiological context.

Network Physiology of Exercise: Vision and Perspectives

The basic theoretical assumptions of Exercise Physiology and its research directions, strongly influenced by reductionism, may hamper the full potential of basic science investigations, and various practical applications to sports performance and exercise as medicine. The aim of this perspective and programmatic article is to: (i) revise the current paradigm of Exercise Physiology and related research on the basis of principles and empirical findings in the new emerging field of Network Physiology and Complex Systems Science; (ii) initiate a new area in Exercise and Sport Science, Network Physiology of Exercise (NPE), with focus on basic laws of interactions and principles of coordination and integration among diverse physiological systems across spatio-temporal scales (from the sub-cellular level to the entire organism), to understand how physiological states and functions emerge, and to improve the efficacy of exercise in health and sport performance; and (iii) to create a forum for developing new research methodologies applicable to the new NPE field, to infer and quantify nonlinear dynamic forms of coupling among diverse systems and establish basic principles of coordination and network organization of physiological systems. Here, we present a programmatic approach for future research directions and potential practical applications. By focusing on research efforts to improve the knowledge about nested dynamics of vertical network interactions, and particularly, the horizontal integration of key organ systems during exercise, NPE may enrich Basic Physiology and diverse fields like Exercise and Sports Physiology, Sports Medicine, Sports Rehabilitation, Sport Science or Training Science and improve the understanding of diverse exercise-related phenomena such as sports performance, fatigue, overtraining, or sport injuries.

Influence of Delayed Conductance on Neuronal Synchronization

In the brain, the excitation-inhibition balance prevents abnormal synchronous behavior. However, known synaptic conductance intensity can be insufficient to account for the undesired synchronization. Due to this fact, we consider time delay in excitatory and inhibitory conductances and study its effect on the neuronal synchronization. In this work, we build a neuronal network composed of adaptive integrate-and-fire neurons coupled by means of delayed conductances. We observe that the time delay in the excitatory and inhibitory conductivities can alter both the state of the collective behavior (synchronous or desynchronous) and its type (spike or burst). For the weak coupling regime, we find that synchronization appears associated with neurons behaving with extremes highest and lowest mean firing frequency, in contrast to when desynchronization is present when neurons do not exhibit extreme values for the firing frequency. Synchronization can also be characterized by neurons presenting either the highest or the lowest levels in the mean synaptic current. For the strong coupling, synchronous burst activities can occur for delays in the inhibitory conductivity. For approximately equal-length delays in the excitatory and inhibitory conductances, desynchronous spikes activities are identified for both weak and strong coupling regimes. Therefore, our results show that not only the conductance intensity, but also short delays in the inhibitory conductance are relevant to avoid abnormal neuronal synchronization.

A Brief Review of Chimera State in Empirical Brain Networks

Understanding the human brain and its functions has always been an interesting and challenging problem. Recently, a significant progress on this problem has been achieved on the aspect of chimera state where a coexistence of synchronized and unsynchronized states can be sustained in identical oscillators. This counterintuitive phenomenon is closely related to the unihemispheric sleep in some marine mammals and birds and has recently gotten a hot attention in neural systems, except the previous studies in non-neural systems such as phase oscillators. This review will briefly summarize the main results of chimera state in neuronal systems and pay special attention to the network of cerebral cortex, aiming to accelerate the study of chimera state in brain networks. Some outlooks are also discussed.

Cerebral haemodynamics with head position changes post-ischaemic stroke: A systematic review and meta-analysis

The effects of upright postures on the cerebral circulation early post-ischaemic stroke are not fully understood. We conducted a systematic review and meta-analysis to investigate the effects of head positioning on cerebral haemodynamics assessed by imaging methods post-ischaemic stroke. Of the 21 studies included (n = 529), 15 used transcranial Doppler. Others used near-infrared, diffuse correlation spectroscopy and nuclear medicine modalities. Most tested head positions between 0° and 45°. Seventeen studies reported changes in CBF parameters (increase at lying-flat or decrease at more upright) in the ischaemic hemisphere with position change. However, great variability was found and risk of bias was high in many studies. Pooled data of two studies ≤24 h (n = 28) showed a mean increase in cerebral blood flow (CBF) velocity of 8.5 cm/s in the ischaemic middle cerebral artery (95%CI,-2.2-19.3) from 30° to 0°. The increase found ≤48 h (n = 50) was of 2.3 cm/s (95%CI,-4.6-9.2), while ≤7 days (n = 38) was of 8.4 cm/s (95%CI, 1.8-15). Few very early studies (≤2 days) tested head positions greater than 30° and were unable to provide information about the response of acute stroke patients to upright postures (sitting, standing). These postures are part of current clinical practice and knowledge on their effects on cerebral haemodynamics is required.

Multivariate Analysis of Joint Motion Data by Kinect: Application to Parkinson's Disease

Analysis of joint motion data (AJMD) by Kinect, such as velocity, has been widely used in many research fields, many of which focused on how one joint moves with another, namely bivariate AJMD. However, these studies might not accurately reflect the motor symptoms in patients. The human body can be divided into six widely accepted parts (head, trunk and four limbs), which are interrelated and interact with each other. Therefore, in this study we attempted to investigate how the major joints of one body part move with the ones in another body part, namely multivariate AJMD. For method illustration, the motion data of sit-to-stand-to-sit for healthy participants and people with Parkinson disease (PD) were employed. Four types of multivariate AJMD were investigated by eigenspace-maximal-information-canonical-correlation-analysis, which obtained maximal- information-eigen-coefficients (MIECes), the parameters for quantifying the correlation between two sets of joints located in two different body parts. The results show that healthy participants have significantly higher MIECes than the PD patients (p-value < 0.0001). Furthermore, the area under the receiver operating characteristic curve value for the classification between healthy participants and PD patients reaches up to 1.00. In conclusion, we demonstrated the possibility of using multivariate AJMD for motion feature extraction, which may be helpful for medical research and engineering.

Coupling Between Leg Muscle Activation and EEG During Normal Walking, Intentional Stops, and Freezing of Gait in Parkinson's Disease

In this paper, we apply novel techniques for characterizing leg muscle activation patterns via electromyograms (EMGs) and for relating them to changes in electroencephalogram (EEG) activity during gait experiments. Specifically, we investigate changes of leg-muscle EMG amplitudes and EMG frequencies during walking, intentional stops, and unintended freezing-of-gait (FOG) episodes. FOG is a frequent paroxysmal gait disturbance occurring in many patients suffering from Parkinson's disease (PD). We find that EMG amplitudes and frequencies do not change significantly during FOG episodes with respect to walking, while drastic changes occur during intentional stops. Phase synchronization between EMG signals is most pronounced during walking in controls and reduced in PD patients. By analyzing cross-correlations between changes in EMG patterns and brain-wave amplitudes (from EEGs), we find an increase in EEG-EMG coupling at the beginning of stop and FOG episodes. Our results may help to better understand the enigmatic pathophysiology of FOG, to differentiate between FOG events and other gait disturbances, and ultimately to improve diagnostic procedures for patients suffering from PD.

Optimizing the detection of nonstationary signals by using recurrence analysis

Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology.

How stimulation frequency and intensity impact on the long-lasting effects of coordinated reset stimulation

Several brain diseases are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR) stimulation was computationally designed to specifically counteract abnormal neuronal synchronization processes by desynchronization. In the presence of spike-timing-dependent plasticity (STDP) this may lead to a decrease of synaptic excitatory weights and ultimately to an anti-kindling, i.e. unlearning of abnormal synaptic connectivity and abnormal neuronal synchrony. The long-lasting desynchronizing impact of CR stimulation has been verified in pre-clinical and clinical proof of concept studies. However, as yet it is unclear how to optimally choose the CR stimulation frequency, i.e. the repetition rate at which the CR stimuli are delivered. This work presents the first computational study on the dependence of the acute and long-term outcome on the CR stimulation frequency in neuronal networks with STDP. For this purpose, CR stimulation was applied with Rapidly Varying Sequences (RVS) as well as with Slowly Varying Sequences (SVS) in a wide range of stimulation frequencies and intensities. Our findings demonstrate that acute desynchronization, achieved during stimulation, does not necessarily lead to long-term desynchronization after cessation of stimulation. By comparing the long-term effects of the two different CR protocols, the RVS CR stimulation turned out to be more robust against variations of the stimulation frequency. However, SVS CR stimulation can obtain stronger anti-kindling effects. We revealed specific parameter ranges that are favorable for long-term desynchronization. For instance, RVS CR stimulation at weak intensities and with stimulation frequencies in the range of the neuronal firing rates turned out to be effective and robust, in particular, if no closed loop adaptation of stimulation parameters is (technically) available. From a clinical standpoint, this may be relevant in the context of both invasive as well as non-invasive CR stimulation.

Transcranial Doppler in autonomic testing: standards and clinical applications

When cerebral blood flow falls below a critical limit, syncope occurs and, if prolonged, ischemia leads to neuronal death. The cerebral circulation has its own complex finely tuned autoregulatory mechanisms to ensure blood supply to the brain can meet the high metabolic demands of the underlying neuronal tissue. This involves the interplay between myogenic and metabolic mechanisms, input from noradrenergic and cholinergic neurons, and the release of vasoactive substrates, including adenosine from astrocytes and nitric oxide from the endothelium. Transcranial Doppler (TCD) is a non-invasive technique that provides real-time measurements of cerebral blood flow velocity. TCD can be very useful in the work-up of a patient with recurrent syncope. Cerebral autoregulatory mechanisms help defend the brain against hypoperfusion when perfusion pressure falls on standing. Syncope occurs when hypotension is severe, and susceptibility increases with hyperventilation, hypocapnia, and cerebral vasoconstriction. Here we review clinical standards for the acquisition and analysis of TCD signals in the autonomic laboratory and the multiple methods available to assess cerebral autoregulation. We also describe the control of cerebral blood flow in autonomic disorders and functional syndromes.

Spurious cross-frequency amplitude-amplitude coupling in nonstationary, nonlinear signals

Recent studies of brain activities show that cross-frequency coupling (CFC) play an important role in memory and learning. Many measures have been proposed to investigate the CFC phenomenon, including the correlation between the amplitude envelopes of two brain waves at different frequencies - cross-frequency amplitude-amplitude coupling (AAC). In this short communication, we describe how nonstationary, nonlinear oscillatory signals may produce spurious cross-frequency AAC. Utilizing the empirical mode decomposition, we also propose a new method for assessment of AAC that can potentially reduce the effects of nonlinearity and nonstatonarity and, thus, help to avoid the detection of artificial AACs. We compare the performances of this new method and the traditional Fourier-based AAC method. We also discuss the strategies to identify potential spurious AACs.

Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions

Within the framework of 'Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.

Synchronization of time-delayed chemically coupled burst-spiking neurons with correlated noises

Synchronization of two time-delayed chemically coupled neurons with burst-spiking states is studied. Different from the previous study by N. Buric et al. (Phys. Rev. E 78, 036211 (2008)), it is found that exactly synchronous burst-spiking dynamics can occur for small coupling strengths and time delays. The results are confirmed by common time delays and non-equal time delays. When common noise is added to the two neurons, synchronization is enhanced as noise strength is increased. But the results are different for larger time delay and smaller time delay. When noises are correlated, it is found that only strong noises with large correlation coefficient can induce exact synchronization. Even one percent of independent noises can influence synchronization much.

Time-varying modeling of cerebral hemodynamics

The scientific and clinical importance of cerebral hemodynamics has generated considerable interest in their quantitative understanding via computational modeling. In particular, two aspects of cerebral hemodynamics, cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity (CVR), have attracted much attention because they are implicated in many important clinical conditions and pathologies (orthostatic intolerance, syncope, hypertension, stroke, vascular dementia, mild cognitive impairment, Alzheimer's disease, and other neurodegenerative diseases with cerebrovascular components). Both CFA and CVR are dynamic physiological processes by which cerebral blood flow is regulated in response to fluctuations in cerebral perfusion pressure and blood CO2 tension. Several modeling studies to date have analyzed beat-to-beat hemodynamic data in order to advance our quantitative understanding of CFA-CVR dynamics. A confounding factor in these studies is the fact that the dynamics of the CFA-CVR processes appear to vary with time (i.e., changes in cerebrovascular characteristics) due to neural, endocrine, and metabolic effects. This paper seeks to address this issue by tracking the changes in linear time-invariant models obtained from short successive segments of data from ten healthy human subjects. The results suggest that systemic variations exist but have stationary statistics and, therefore, the use of time-invariant modeling yields "time-averaged models" of physiological and clinical utility.

A nonlinear dynamic approach reveals a long-term stroke effect on cerebral blood flow regulation at multiple time scales

Cerebral autoregulation (CA) is an important vascular control mechanism responsible for relatively stable cerebral blood flow despite changes of systemic blood pressure (BP). Impaired CA may leave brain tissue unprotected against potentially harmful effects of BP fluctuations. It is generally accepted that CA is less effective or even inactive at frequencies >∼0.1 Hz. Without any physiological foundation, this concept is based on studies that quantified the coupling between BP and cerebral blood flow velocity (BFV) using transfer function analysis. This traditional analysis assumes stationary oscillations with constant amplitude and period, and may be unreliable or even invalid for analysis of nonstationary BP and BFV signals. In this study we propose a novel computational tool for CA assessment that is based on nonlinear dynamic theory without the assumption of stationary signals. Using this method, we studied BP and BFV recordings collected from 39 patients with chronic ischemic infarctions and 40 age-matched non-stroke subjects during baseline resting conditions. The active CA function in non-stroke subjects was associated with an advanced phase in BFV oscillations compared to BP oscillations at frequencies from ∼0.02 to 0.38 Hz. The phase shift was reduced in stroke patients even at > = 6 months after stroke, and the reduction was consistent at all tested frequencies and in both stroke and non-stroke hemispheres. These results provide strong evidence that CA may be active in a much wider frequency region than previously believed and that the altered multiscale CA in different vascular territories following stroke may have important clinical implications for post-stroke recovery. Moreover, the stroke effects on multiscale cerebral blood flow regulation could not be detected by transfer function analysis, suggesting that nonlinear approaches without the assumption of stationarity are more sensitive for the assessment of the coupling of nonstationary physiological signals.

Cerebral autoregulation is an important mechanism that regulates blood supply to brain tissue to match metabolic demands during daily activities. Impaired cerebral autoregulation increases the dependence of cerebral blood flow on systemic blood pressure, and is associated with fatal outcomes in patients after brain injury and acute ischemic stroke. Reliable and noninvasive assessment of cerebral autoregulation is still a major challenge in medical diagnostics and clinic studies, mainly because blood pressure and flow are intrinsically nonstationary (possessing complex oscillations/fluctuations with varying amplitude and frequency) while traditional methods for assessment of the pressure-flow dependence assume stationary signals. We propose a new computational technique that is based on nonlinear theories without the assumption of stationary signals. This technique allows us to detect the degradation of cerebral autoregulation in patients with mild ischemic stroke even at >6 months after the insult. The degradation was present in both stroke and non-stroke sides and was accompanied by an altered pressure-flow interaction over a wide range of frequencies from 0.02–0.38 Hz. Our results challenges the traditionally accepted functional region of autoregulation (<∼0.1 Hz). The observed long-term influences of stroke highlight the importance of reliable monitoring of cerebral blood flow regulation for the management and daily care of stroke patients.

Respiration drives phase synchronization between blood pressure and RR interval following loss of cardiovagal baroreflex during vasovagal syncope

Loss of the cardiovagal baroreflex (CVB), thoracic hypovolemia, and hyperpnea contribute to the nonlinear time-dependent hemodynamic instability of vasovagal syncope. We used a nonlinear phase synchronization index (PhSI) to describe the extent of coupling between cardiorespiratory parameters, systolic blood pressure (SBP) or arterial pressure (AP), RR interval (RR), and ventilation, and a directional index (DI) measuring the direction of coupling. We also examined phase differences directly. We hypothesized that AP-RR interval PhSI would be normal during early upright tilt, indicating intact CVB, but would progressively decrease as faint approached and CVB failed. Continuous measurements of AP, RR interval, respiratory plethysomography, and end-tidal CO2 were recorded supine and during 70-degree head-up tilt in 15 control subjects and 15 fainters. Data were evaluated during five distinct times: baseline, early tilt, late tilt, faint, and recovery. During late tilt to faint, fainters exhibited a biphasic change in SBP-RR interval PhSI. Initially in fainters during late tilt, SBP-RR interval PhSI decreased (fainters, from 0.65±0.04 to 0.24±0.03 vs. control subjects, from 0.51±0.03 to 0.48±0.03; P<0.01) but then increased at the time of faint (fainters=0.80±0.03 vs. control subjects=0.42±0.04; P<0.001) coinciding with a change in phase difference from positive to negative. Starting in late tilt and continuing through faint, fainters exhibited increasing phase coupling between respiration and AP PhSI (fainters=0.54±0.06 vs. control subjects=0.27±0.03; P<0.001) and between respiration and RR interval (fainters=0.54±0.05 vs. control subjects=0.37±0.04; P<0.01). DI indicated respiratory driven AP (fainters=0.84±0.04 vs. control subjects=0.39±0.09; P<0.01) and RR interval (fainters=0.73±0.10 vs. control subjects=0.23±0.11; P<0.001) in fainters. The initial drop in the SBP-RR interval PhSI and directional change of phase difference at late tilt indicates loss of cardiovagal baroreflex. The subsequent increase in SBP-RR interval PhSI is due to a respiratory synchronization and drive on both AP and RR interval. Cardiovagal baroreflex is lost before syncope and supplanted by respiratory reflexes, producing hypotension and bradycardia.

Nonlinear pressure-flow relationship is able to detect asymmetry of brain blood circulation associated with midline shift

Reliable and noninvasive assessment of cerebral blood flow regulation is a major challenge in acute care monitoring. This study assessed dynamics of flow regulation and its relationship to asymmetry of initial computed tomography (CT) scan using multimodal pressure flow (MMPF) analysis. Data of 27 patients (38 +/- 15 years old) with traumatic brain injury (TBI) were analyzed. Patients were selected from bigger cohort according to criteria of having midline shift on initial CT scan and intact skull (no craniotomy or bone flap). The MMPF analysis was used to extract the oscillations in cerebral perfusion pressure (CPP) and blood flow velocity (BFV) signals at frequency of artificial ventilation, and to calculate the instantaneous phase difference between CPP and BFV oscillations. Mean CPP-BFV phase difference was used to quantify pressure and flow relationship. The TBI subjects had smaller mean BP-BFV phase shifts (left, 8.7 +/- 9.6; right 10.2 +/- 8.3 MCAs, mean +/- SD) than values previously obtained in healthy subjects (left, 37.3 +/- 7.6 degrees; right, 38.0 +/- 8.9 degrees; p < 0.0001), suggesting impaired blood flow regulation after TBI. The difference in phase shift between CPP and BFV in the left and right side was strongly correlated to the midline shift (R = 0.78; p < 0.0001). These findings indicate that the MMPF method allows reliable assessment of alterations in pressure and flow relationship after TBI. Moreover, mean pressure-flow phase shift is sensitive to the displacement of midline of the brain, and may potentially serve as a marker of asymmetry of cerebral autoregulation.

Increased phase synchronization and decreased cerebral autoregulation during fainting in the young

Vasovagal syncope may be due to a transient cerebral hypoperfusion that accompanies frequency entrainment between arterial pressure (AP) and cerebral blood flow velocity (CBFV). We hypothesized that cerebral autoregulation fails during fainting; a phase synchronization index (PhSI) between AP and CBFV was used as a nonlinear, nonstationary, time-dependent measurement of cerebral autoregulation. Twelve healthy control subjects and twelve subjects with a history of vasovagal syncope underwent 10-min tilt table testing with the continuous measurement of AP, CBFV, heart rate (HR), end-tidal CO2 (ETCO2), and respiratory frequency. Time intervals were defined to compare physiologically equivalent periods in fainters and control subjects. A PhSI value of 0 corresponds to an absence of phase synchronization and efficient cerebral autoregulation, whereas a PhSI value of 1 corresponds to complete phase synchronization and inefficient cerebral autoregulation. During supine baseline conditions, both control and syncope groups demonstrated similar oscillatory changes in phase, with mean PhSI values of 0.58+/-0.04 and 0.54+/-0.02, respectively. Throughout tilt, control subjects demonstrated similar PhSI values compared with supine conditions. Approximately 2 min before fainting, syncopal subjects demonstrated a sharp decrease in PhSI (0.23+/-0.06), representing efficient cerebral autoregulation. Immediately after this period, PhSI increased sharply, suggesting inefficient cerebral autoregulation, and remained elevated at the time of faint (0.92+/-0.02) and during the early recovery period (0.79+/-0.04) immediately after the return to the supine position. Our data demonstrate rapid, biphasic changes in cerebral autoregulation, which are temporally related to vasovagal syncope. Thus, a sudden period of highly efficient cerebral autoregulation precedes the virtual loss of autoregulation, which continued during and after the faint.

Influence of paced maternal breathing on fetal-maternal heart rate coordination

Pregnant mothers often report a special awareness of and bonding with their unborn child. Little is known about this relationship although it may offer potential for the assessment of the fetal condition. Recently we found evidence of short epochs of fetal-maternal heart rate synchronization under uncontrolled conditions with spontaneous maternal breathing. Here, we examine whether the occurrence of such epochs can be influenced by maternal respiratory arrhythmia induced by paced breathing at several different rates (10, 12, 15, and 20 cycles per minute). To test for such weak and nonstationary synchronizations among the fetal-maternal subsystems, we apply a multivariate synchronization analysis technique and test statistics based on twin surrogates. We find a clear increase in synchronization epochs mostly at high maternal respiratory rates in the original but not in the surrogate data. On the other hand, fewer epochs are found at low respiratory rates both in original and surrogate data. The results suggest that the fetal cardiac system seems to possess the capability to adjust its rate of activation in response to external--i.e., maternal--stimulation. Hence, the pregnant mothers' special awareness to the unborn child may also be reflected by fetal-maternal interaction of cardiac activity. Our approach opens up the chance to examine this interaction between independent but closely linked physiological systems.

Nonlinear phase interaction between nonstationary signals: a comparison study of methods based on Hilbert-Huang and Fourier transforms

Phase interactions among signals of physical and physiological systems can provide useful information about the underlying control mechanisms of the systems. Physical and biological recordings are often noisy and exhibit nonstationarities that can affect the estimation of phase interactions. We systematically studied effects of nonstationarities on two phase analyses including (i) the widely used transfer function analysis (TFA) that is based on Fourier decomposition and (ii) the recently proposed multimodal pressure flow (MMPF) analysis that is based on Hilbert-Huang transform (HHT)-an advanced nonlinear decomposition algorithm. We considered three types of nonstationarities that are often presented in physical and physiological signals: (i) missing segments of data, (ii) linear and step-function trends embedded in data, and (iii) multiple chaotic oscillatory components at different frequencies in data. By generating two coupled oscillatory signals with an assigned phase shift, we quantify the change in the estimated phase shift after imposing artificial nonstationarities into the oscillatory signals. We found that all three types of nonstationarities affect the performances of the Fourier-based and the HHT-based phase analyses, introducing bias and random errors in the estimation of the phase shift between two oscillatory signals. We also provided examples of nonstationarities in real physiological data (cerebral blood flow and blood pressure) and showed how nonstationarities can complicate result interpretation. Furthermore, we propose certain strategies that can be implemented in the TFA and the MMPF methods to reduce the effects of nonstationarities, thus improving the performances of the two methods.

Continuous estimates of dynamic cerebral autoregulation: influence of non-invasive arterial blood pressure measurements

Temporal variability of parameters which describe dynamic cerebral autoregulation (CA), usually quantified by the short-term relationship between arterial blood pressure (BP) and cerebral blood flow velocity (CBFV), could result from continuous adjustments in physiological regulatory mechanisms or could be the result of artefacts in methods of measurement, such as the use of non-invasive measurements of BP in the finger. In 27 subjects (61+/-11 years old) undergoing coronary artery angioplasty, BP was continuously recorded at rest with the Finapres device and in the ascending aorta (Millar catheter, BP(AO)), together with bilateral transcranial Doppler ultrasound in the middle cerebral artery, surface ECG and transcutaneous CO(2). Dynamic CA was expressed by the autoregulation index (ARI), ranging from 0 (absence of CA) to 9 (best CA). Time-varying, continuous estimates of ARI (ARI(t)) were obtained with an autoregressive moving-average (ARMA) model applied to a 60 s sliding data window. No significant differences were observed in the accuracy and precision of ARI(t) between estimates derived from the Finapres and BP(AO). Highly significant correlations were obtained between ARI(t) estimates from the right and left middle cerebral artery (MCA) (Finapres r=0.60+/-0.20; BP(AO) r=0.56+/-0.22) and also between the ARI(t) estimates from the Finapres and BP(AO) (right MCA r=0.70+/-0.22; left MCA r=0.74+/-0.22). Surrogate data showed that ARI(t) was highly sensitive to the presence of noise in the CBFV signal, with both the bias and dispersion of estimates increasing for lower values of ARI(t). This effect could explain the sudden drops of ARI(t) to zero as reported previously. Simulated sudden changes in ARI(t) can be detected by the Finapres, but the bias and variability of estimates also increase for lower values of ARI. In summary, the Finapres does not distort time-varying estimates of dynamic CA obtained with a sliding window combined with an ARMA model, but further research is needed to confirm these findings in healthy subjects and to assess the influence of different physiological manoeuvres.

Altered Phase Interactions between Spontaneous Blood Pressure and Flow Fluctuations in Type 2 Diabetes Mellitus: Nonlinear Assessment of Cerebral Autoregulation

Cerebral autoregulation (CA) is an important mechanism that involves dilation and constriction in arterioles to maintain relatively s cerebral blood flow in response to changes of systemic blood pressure. Traditional assessments of CA focus on the changes of cerebral blood flow velocity in response to large blood pressure fluctuations induced by interventions. This approach is not feasible for patients with impaired autoregulation or cardiovascular regulation. Here we propose a newly developed technique-the multimodal pressure-flow (MMPF) analysis, which assesses CA by quantifying nonlinear phase interactions between spontaneous oscillations in blood pressure and flow velocity during resting conditions. We show that CA in healthy subjects can be characterized by specific phase shifts between spontaneous blood pressure and flow velocity oscillations, and the phase shifts are significantly reduced in diabetic subjects. Smaller phase shifts between oscillations in the two variables indicate more passive dependence of blood flow velocity on blood pressure, thus suggesting impaired cerebral autoregulation. Moreover, the reduction of the phase shifts in diabetes is observed not only in previously-recognized effective region of CA (<0.1Hz), but also over the higher frequency range from ~0.1 to 0.4Hz. These findings indicate that Type 2 diabetes alters cerebral blood flow regulation over a wide frequency range and that this alteration can be reliably assessed from spontaneous oscillations in blood pressure and blood flow velocity during resting conditions. We also show that the MMPF method has better performance than traditional approaches based on Fourier transform, and is more sui for the quantification of nonlinear phase interactions between nonstationary biological signals such as blood pressure and blood flow.

Multimodal Pressure Flow Analysis: Application of Hilbert Huang Transform in Cerebral Blood Flow Regulation

Quantification of nonlinear interactions between two nonstationary signals presents a computational challenge in different research fields, especially for assessments of physiological systems. Traditional approaches that are based on theories of stationary signals cannot resolve nonstationarity-related issues and, thus, cannot reliably assess nonlinear interactions in physiological systems. In this review we discuss a new technique "Multi-Modal Pressure Flow method (MMPF)" that utilizes Hilbert-Huang transformation to quantify dynamic cerebral autoregulation (CA) by studying interaction between nonstationary cerebral blood flow velocity (BFV) and blood pressure (BP). CA is an important mechanism responsible for controlling cerebral blood flow in responses to fluctuations in systemic BP within a few heart-beats. The influence of CA is traditionally assessed from the relationship between the well-pronounced systemic BP and BFV oscillations induced by clinical tests. Reliable noninvasive assessment of dynamic CA, however, remains a challenge in clinical and diagnostic medicine.In this brief review we: 1) present an overview of transfer function analysis (TFA) that is traditionally used to quantify CA; 2) describe the a MMPF method and its modifications; 3) introduce a newly developed automatic algorithm and engineering aspects of the improved MMPF method; and 4) review clinical applications of MMPF and its sensitivity for detection of CA abnormalities in clinical studies. The MMPF analysis decomposes complex nonstationary BP and BFV signals into multiple empirical modes adaptively so that the fluctuations caused by a specific physiologic process can be represented in a corresponding empirical mode. Using this technique, we recently showed that dynamic CA can be characterized by specific phase delays between the decomposed BP and BFV oscillations, and that the phase shifts are significantly reduced in hypertensive, diabetics and stroke subjects with impaired CA. In addition, the new technique enables reliable assessment of CA using both data collected during clinical test and spontaneous BP/BFV fluctuations during baseline resting conditions.

Spurious detection of phase synchronization in coupled nonlinear oscillators

Coupled nonlinear systems under certain conditions exhibit phase synchronization, which may change for different frequency bands or with the presence of additive system noise. In both cases, Fourier filtering is traditionally used to preprocess data. We investigate to what extent the phase synchronization of two coupled Rössler oscillators depends on (1) the broadness of their power spectrum, (2) the width of the bandpass filter, and (3) the level of added noise. We find that for identical coupling strengths, oscillators with broader power spectra exhibit weaker synchronization. Further, we find that within a broad bandwidth range, bandpass filtering reduces the effect of noise but can lead to a spurious increase in the degree of phase synchronization with narrowing bandwidth, even when the coupling between the two oscillators remains the same.