Frontiers in network physiology最新文献

The estimation of cardiorespiratory coupling (CRC) is attracting interest in sports physiology as an important tool to characterize cardiac neural regulation genuinely driven by respiration. When applied in sports medicine, cardiorespiratory coupling measurements can provide information on the effects of training, pre-competition stress, as well as cardiovascular adjustments during stressful stimuli. Furthermore, since the cardiorespiratory coupling is strongly affected by physical activity, the study of the cardiorespiratory coupling can guide the application of specific training methods to optimize the coupling between autonomic activity and heart with possible effects on performance. However, a consensus about the physiological mechanisms, as well as methodological gold standard methods to quantify the cardiorespiratory coupling, has not been reached yet, thus limiting its application in experimental settings. This review supports the relevance of assessing cardiorespiratory coupling in the sports medicine, examines the possible physiological mechanisms involved, and lists a series of methodological approaches. cardiorespiratory coupling strength seems to be increased in athletes when compared to sedentary subjects, in addition to being associated with positive physiological outcomes, such as a possible better interaction of neural subsystems to cope with stressful stimuli. Moreover, cardiorespiratory coupling seems to be influenced by specific training modalities, such as inspiratory muscle training. However, the impact of cardiorespiratory coupling on sports performance still needs to be better explored through ad hoc physical exercise tests and protocols. In addition, this review stresses that several bivariate and multivariate methods have been proposed to assess cardiorespiratory coupling, thus opening new possibilities in estimating cardiorespiratory interactions in athletes.

Introduction: Detrended Fluctuation Analysis (DFA) has been used to investigate self-similarity in center of pressure (CoP) time series. For fractional gaussian noise (fGn) signals, the analysis returns a scaling exponent, DFA-α, whose value characterizes the temporal correlations as persistent, random, or anti-persistent. In the study of postural control, DFA has revealed two time scaling regions, one at the short-term and one at the long-term scaling regions in the diffusion plots, suggesting different types of postural dynamics. Much attention has been given to the selection of minimum and maximum scales, but the choice of spacing (step size) between the window sizes at which the fluctuation function is evaluated may also affect the estimates of scaling exponents. The aim of this study is twofold. First, to determine whether DFA can reveal postural adjustments supporting performance of an upper limb task under variable demands. Second, to compare evenly-spaced DFA with two different step sizes, 0.5 and 1.0 in log₂ units, applied to CoP time series. Methods: We analyzed time series of anterior-posterior (AP) and medial-lateral (ML) CoP displacement from healthy participants performing a sequential upper limb task under variable demand. Results: DFA diffusion plots revealed two scaling regions in the AP and ML CoP time series. The short-term scaling region generally showed hyper-diffusive dynamics and long-term scaling revealed mildly persistent dynamics in the ML direction and random-like dynamics in the AP direction. There was a systematic tendency for higher estimates of DFA-α and lower estimates for crossover points for the 0.5-unit step size vs. 1.0-unit size. Discussion: Results provide evidence that DFA-α captures task-related differences between postural adjustments in the AP and ML directions. Results also showed that DFA-α estimates and crossover points are sensitive to step size. A step size of 0.5 led to less variable DFA-α for the long-term scaling region, higher estimation for the short-term scaling region, lower estimate for crossover points, and revealed anomalous estimates at the very short range that had implications for choice of minimum window size. We, therefore, recommend the use of 0.5 step size in evenly spaced DFAs for CoP time series similar to ours.

Introduction: Joint symbolic analysis (JSA) can be utilized to describe interactions between time series while accounting for time scales and nonlinear features. JSA is based on the computation of the rate of occurrence of joint patterns built after symbolization. Lagged JSA (LJSA) is obtained from the more classical JSA by introducing a delay/lead between patterns built over the two series and combined to form the joint scheme, thus monitoring coordinated patterns at different lags. Methods: In the present study, we applied LJSA for the assessment of cardiorespiratory coupling (CRC) from heart period (HP) variability and respiratory activity (R) in 19 healthy subjects (age: 27-35 years; 8 males, 11 females) during spontaneous breathing (SB) and controlled breathing (CB). The R rate of CB was selected to be indistinguishable from that of SB, namely, 15 breaths·minute^-1 (CB15), or slower than SB, namely, 10 breaths·minute^-1 (CB10), but in both cases, very rapid interactions between heart rate and R were known to be present. The ability of the LJSA approach to follow variations of the coupling strength was tested over a unidirectionally or bidirectionally coupled stochastic process and using surrogate data to test the null hypothesis of uncoupling. Results: We found that: i) the analysis of surrogate data proved that HP and R were significantly coupled in any experimental condition, and coupling was not more likely to occur at a specific time lag; ii) CB10 reduced CRC strength at the fastest time scales while increasing that at intermediate time scales, thus leaving the overall CRC strength unvaried; iii) despite exhibiting similar R rates and respiratory sinus arrhythmia, SB and CB15 induced different cardiorespiratory interactions; iv) no dominant temporal scheme was observed with relevant contributions of HP patterns either leading or lagging R. Discussion: LJSA is a useful methodology to explore HP-R dynamic interactions while accounting for time shifts and scales.

When potassium in the extracellular space separating neurons and glia reaches sufficient levels, neurons may fire spontaneous action potentials or even become inactivated due to membrane depolarisation, which, in turn, may lead to increased extracellular potassium levels. Under certain circumstances, this chain of events may trigger periodic bursts of neuronal activity. In the present study, reduced neuron-glia models are applied to explore the relationship between bursting behaviour and ion concentration dynamics. These reduced models are built based on a previously developed neuron-glia model, in which channel-mediated neuronal sodium and potassium currents are replaced by a function of neuronal sodium and extracellular potassium concentrations. Simulated dynamics of the resulting two reduced models display features that are qualitatively similar to those of the existing neuron-glia model. Bifurcation analyses of the reduced models show rich and interesting dynamics that include the existence of Hopf bifurcations between which the models exhibit slow ion concentration oscillations for a wide range of parameter values. The study demonstrates that even very simple models can provide insights of possible relevance to complex phenomena.

Astrocytic fine processes are the most minor structures of astrocytes but host much of the Ca²⁺ activity. These localized Ca²⁺ signals spatially restricted to microdomains are crucial for information processing and synaptic transmission. However, the mechanistic link between astrocytic nanoscale processes and microdomain Ca²⁺ activity remains hazily understood because of the technical difficulties in accessing this structurally unresolved region. In this study, we used computational models to disentangle the intricate relations of morphology and local Ca²⁺ dynamics involved in astrocytic fine processes. We aimed to answer: 1) how nano-morphology affects local Ca²⁺ activity and synaptic transmission, 2) and how fine processes affect Ca²⁺ activity of large process they connect. To address these issues, we undertook the following two computational modeling: 1) we integrated the in vivo astrocyte morphological data from a recent study performed with super-resolution microscopy that discriminates sub-compartments of various shapes, referred to as nodes and shafts to a classic IP₃R-mediated Ca²⁺ signaling framework describing the intracellular Ca²⁺ dynamics, 2) we proposed a node-based tripartite synapse model linking with astrocytic morphology to predict the effect of structural deficits of astrocytes on synaptic transmission. Extensive simulations provided us with several biological insights: 1) the width of nodes and shafts could strongly influence the spatiotemporal variability of Ca²⁺ signals properties but what indeed determined the Ca²⁺ activity was the width ratio between nodes and shafts, 2) the connectivity of nodes to larger processes markedly shaped the Ca²⁺ signal of the parent process rather than nodes morphology itself, 3) the morphological changes of astrocytic part might potentially induce the abnormality of synaptic transmission by affecting the level of glutamate at tripartite synapses. Taken together, this comprehensive model which integrated theoretical computation and in vivo morphological data highlights the role of the nanomorphology of astrocytes in signal transmission and its possible mechanisms related to pathological conditions.

Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid (fentanyl) or a nerve agent (VX), as well as for differentiating between the two. Here, we investigated how exposure to these different chemicals affects the interactions between ECG and respiration parameters as determined by Granger causality (GC). Features reflecting such interactions may provide additional information and improve models differentiating between chemical agents. Traditional respiration and ECG features, as well as GC features, were extracted from data of 120 guinea pigs exposed to VX (n = 61) or fentanyl (n = 59). Data were divided in a training set (n = 99) and a test set (n = 21). Minimum Redundancy Maximum Relevance (mRMR) and Support Vector Machine (SVM) algorithms were used to, respectively, perform feature selection and train a model to discriminate between the two chemicals. We found that ECG and respiration parameters are Granger-related under healthy conditions, and that exposure to fentanyl and VX affected these relationships in different ways. SVM models discriminated between chemicals with accuracy of 95% or higher on the test set. GC features did not improve the classification compared to traditional features. Respiration features (i.e., peak inspiratory and expiratory flow) were the most important to discriminate between different chemical's exposure. Our results indicate that it may be feasible to discriminate between chemical exposure when using traditional physiological respiration features from wearable sensors. Future research will examine whether GC features can contribute to robust detection and differentiation between chemicals when considering other factors, such as generalizing results across species.

The low-density lipoprotein related protein receptor 1 (LRP1), also known as CD91 or α-Macroglobulin-receptor, is a transmembrane receptor that interacts with more than 40 known ligands. It plays an important biological role as receptor of morphogens, extracellular matrix molecules, cytokines, proteases, protease inhibitors and pathogens. In the CNS, it has primarily been studied as a receptor and clearance agent of pathogenic factors such as Aβ-peptide and, lately, Tau protein that is relevant for tissue homeostasis and protection against neurodegenerative processes. Recently, it was found that LRP1 expresses the Lewis-X (Lex) carbohydrate motif and is expressed in the neural stem cell compartment. The removal of Lrp1 from the cortical radial glia compartment generates a strong phenotype with severe motor deficits, seizures and a reduced life span. The present review discusses approaches that have been taken to address the neurodevelopmental significance of LRP1 by creating novel, lineage-specific constitutive or conditional knockout mouse lines. Deficits in the stem cell compartment may be at the root of severe CNS pathologies.

Non-invasive transcutaneous vagus nerve stimulation elicits similar therapeutic effects as invasive vagus nerve stimulation, offering a potential treatment alternative for a wide range of diseases, including epilepsy. Here, we present a novel, non-invasive stimulation of the vagus nerve, which is performed manually viscero-osteopathically on the abdomen (voVNS). We explore the impact of short-term voVNS on various local and global characteristics of EEG-derived, large-scale evolving functional brain networks from a group of 20 subjects with and without epilepsy. We observe differential voVNS-mediated alterations of these characteristics that can be interpreted as a reconfiguration and modification of networks and their stability and robustness properties. Clearly, future studies are necessary to assess the impact of such a non-pharmaceutical intervention on clinical decision-making in the treatment of epilepsy. However, our findings may add to the current discussion on the importance of the gut-brain axis in health and disease. Clinical Trial Registration: https://drks.de/search/en/trial/DRKS00029914, identifier DRKS00029914.

The architecture of the air-blood barrier is effective in optimizing the gas exchange as long as it retains its specific feature of extreme thinness reflecting, in turn, a strict control on the extravascular water to be kept at minimum. Edemagenic conditions may perturb this equilibrium by increasing microvascular filtration; this characteristically occurs when cardiac output increases to balance the oxygen uptake with the oxygen requirement such as in exercise and hypoxia (either due to low ambient pressure or reflecting a pathological condition). In general, the lung is well equipped to counteract an increase in microvascular filtration rate. The loss of control on fluid balance is the consequence of disruption of the integrity of the macromolecular structure of lung tissue. This review, merging data from experimental approaches and evidence in humans, will explore how the heterogeneity in morphology, mechanical features and perfusion of the terminal respiratory units might impact on lung fluid balance and its control. Evidence is also provided that heterogeneities may be inborn and they could actually get worse as a consequence of a developing pathological process. Further, data are presented how in humans inter-individual heterogeneities in morphology of the terminal respiratory hinder the control of fluid balance and, in turn, hamper the efficiency of the oxygen diffusion-transport function.