Frontiers in network physiology最新文献

Traditional brain models have focused primarily on electrical signalling, offering valuable insights but often overlooking the crucial role of metabolism within the neurovascular unit. Existing metabolic models tend to be highly detailed and mass-based, relying on strict conservation laws that limit their applicability to the brain's thermodynamically open environment. In this study, we present a novel, phenomenological model of neuronal energy metabolism using a network of coupled Kuramoto oscillators. This nonautonomous phase dynamics framework captures complex, time-dependent interactions and allows for multiple synchronization states among metabolic processes. Our model captures key features consistent with healthy neurovascular dynamics, despite not being directly fitted to empirical data from resting-state brains and reveals how disruptions in metabolic synchrony may contribute to dementia-related pathology. By emphasizing the importance of metabolic coordination in the neurovascular unit, this work provides a versatile methodological foundation for future brain modelling efforts.

Introduction: Inspiratory muscle training (IMT) has been proposed as a non-pharmacological strategy capable of improving respiratory performance and modulating cardiovascular autonomic function. However, its effects on baroreflex sensitivity, heart rate variability, and cardiorespiratory interactions in healthy young adults remain insufficiently understood. Therefore, this study aimed to determine whether a 4-week IMT program, performed at moderate load, improves inspiratory muscle strength, cardiac autonomic modulation, spontaneous baroreflex sensitivity (BRS), and respiratory pattern in healthy individuals.

Methods: Twenty-two healthy young men were randomly assigned to an experimental group (60% of maximal inspiratory pressure, MIP) or a placebo group (10% of MIP). Before and after the intervention, participants underwent pulmonary function testing and assessments of inspiratory muscle performance, as well as hemodynamic, autonomic, and respiratory recordings during spontaneous and controlled breathing. Heart rate variability (HRV), blood pressure variability, and BRS (α-LF) were assessed during respiratory sinus arrhythmia (RSA), and responses to the Valsalva maneuver were also evaluated.

Results: IMT significantly increased MIP by approximately 26% and enhanced peak inspiratory flow, without changes in pulmonary volumes. Vagal indices of HRV increased after training (rMSSD and HF; p ≤ 0.05), indicating enhanced parasympathetic modulation. IMT also modified the respiratory pattern, reducing the Ti/Ttot ratio and increasing expiratory time (p = 0.04). No significant changes were observed in blood pressure variability or BRS. RSA analysis demonstrated a reduction in inspiratory heart rate, and the Valsalva maneuver revealed attenuation of heart rate overshoot in phase IV.

Discussion: In conclusion, a 4-week IMT program in healthy young adults improves inspiratory muscle performance, enhances vagally mediated HRV, and promotes adjustments in respiratory pattern, without altering spontaneous baroreflex sensitivity. These findings suggest that the autonomic benefits of IMT on cardiac vagal modulation are predominantly mediated by respiratory mechanisms.

We present a robust method to assess pulsatile motion of larger cranial blood vessels in the human brain from high spatiotemporal-resolution real-time magnetic resonance (MR) imaging data. Together with percentile-based thresholding in combination with a border-detection algorithm and other empirical selection criteria, we are able to extract area time series from the pulsatile motion of blood vessels. In a proof of concept, we apply our method to the left and right vertebral arteries in a cohort of healthy subjects and extract heart and breathing rates from their pulsatile motion. Comparison to mean physiological reference values measured simultaneously with a photoplethysmogram and a breathing belt shows no differences within the scope of the measurement accuracy. Intra-subject differences for breathing rates detected in the left and right vertebral artery are high but not significant. Our findings suggest that the proposed method is suitable for assessing arterial pulsations in larger cranial vessels driven by heart or breathing rates, as part of the complex physiological network of heart-brain interactions.

Introduction: Conventional models treat cardiovascular and neuromuscular adaptations as independent, which can hide interference between endurance and power. We investigated whether cardiac remodeling is associated with peak explosive power when adaptation is considered as an integrated system.

Methods: Nineteen male Super League soccer players completed two-dimensional echocardiography to quantify left ventricular mass index (LVMI) and performed a fifteen-repetition vertical jump test. We adjusted variables for body size and training years, then estimated a partial-correlation network with a Gaussian graphical model and ran sensitivity and subgroup checks.

Results: The developed network was sparse and stable. A selective inverse association linked LVMI with maximal jump height (partial correlation -0.41), supported by a complementary Bayesian analysis (Bayes factor 5.70). Neuromuscular variables formed a tight positive cluster, and LVMI did not show negative coupling with other jump metrics, indicating a specific rather than global trade-off.

Discussion: In elite players, a cardiac phenotype consistent with endurance support coincided with constrained peak explosive output when the system was analyzed as a whole. An interdependent network view clarifies interference patterns and points to targeted monitoring and periodization strategies for high-performance sport.

Epileptic seizures and interictal discharges exhibit robust circadian and multidien rhythms, yet the interaction between these biological cycles and anti-seizure medication (ASM) pharmacology remains poorly understood. Here, we present a dynamical network model that integrates rhythmic fluctuations in cortical excitability with pharmacokinetic properties of common ASMs to explore how treatment timing influences efficacy. The framework embeds a slow, rhythm-generating process directly within the governing equations, allowing seizure-like dynamics to emerge endogenously. We simulated ASMs with a range of distinct half-lives under single-daily and twice-daily dosing schedules. For the short half-life ASM, efficacy depended strongly on the phase of administration, with doses delivered approximately 6 h before the peak in seizure likelihood achieving up to 20% greater reduction in epileptiform discharges than suboptimal phases. In contrast, phase dependence was minimal for slower half-life drugs due to their slower elimination and flatter concentration profiles. These findings suggest that short half-life ASMs could benefit most from chronotherapeutic timing. Our framework unifies seizure dynamics, biological rhythms, and ASM pharmacology within a single model, offering a mechanistic tool to explore patient-specific optimization of treatment timing. This work establishes a foundation for translating chronotherapy into epilepsy care and provides a conceptual bridge between computational neuroscience and clinical pharmacology.

Structural balance theory, widely used in social network research, has recently been applied to brain network studies to explore how higher-order interactions relate to neural function and dysfunction. The theory is founded on the core assumption that balanced triads, representing internally consistent relationships, are intrinsically stable, while imbalanced triads, which introduce structural tension, are unstable and tend to reconfigure toward balance. Despite its promising application, these foundational assumptions have not been empirically validated in the brain. Here, we address this gap using resting-state fMRI data from the Human Connectome Project to analyze the temporal dynamics of triadic configurations. We defined two metrics: triad lifetime, the duration a triad persists, and absolute peak energy, the maximum triadic interaction strength during that time. Balanced triads showed significantly longer lifetimes and higher peak energy than imbalanced ones, consistent with their theorized stability. Imbalanced triads were more transient and weaker, reflecting structural conflict. Comparison with surrogate null models confirmed that these patterns were not random, but reflected meaningful higher-order neural organization. The joint distribution of lifetime and energy revealed two clusters of triads aligning with strong, not weak, structural balance theory. Additionally, specific transition patterns between triadic configurations, combined with lifetime profiles, shaped the non-uniform prevalence of triadic states in brain networks. Our findings provide empirical validation of structural balance theory in brain networks and introduce dynamic measures for characterizing triadic brain interactions, together offering a framework for studying the dynamics of higher-order interactions and the stability of brain networks in health and disease.

Purpose: To determine the frequency band-related local functional connectivity (BRLFC) of the seizure onset area (SOA) and areas removed from it, and the relationship between BRLFC and outcome of epilepsy surgery.

Methods: This study was conducted on 14 unselected adult patients with focal epilepsy undergoing icEEG monitoring for surgery. Intracranial EEG (icEEG) electrode contacts were located from post-implantation CT and MR images and registered to the MRI of a common brain to allow interpretation of results from all patients in the same space. Two 1 h icEEG epochs, recorded during wake and removed in time from seizure occurrence, were studied. One of these epochs was when the subject was on anti-seizure medications (ASMs), while the second was after ASM taper. Coherence was estimated for all pairs of electrode contacts ipsilateral to the SOA in delta, theta, alpha, beta, gamma and a high frequency band. The BRLFC of each electrode contact was estimated as the average band-related coherence between it and all electrode contacts within a spatial window.

Key findings: BRLFC in the SOA and peri-SOA, for selected frequency bands, was greater in patients with excellent outcome after surgery in comparison to those with poor outcome. A graded relationship was observed between BRLFC and distance to the SOA of patients with excellent outcome to surgery such that contacts with the greatest connectivity were closer to the SOA and those with the lowest connectivity were several cm from the SOA. This relationship between distance to the SOA and connectivity was present primarily in the alpha, beta, gamma and high frequency bands and the BRLFC was greatest in the peri-SOA, within a distance of 5 cm from the SOA. This relationship was stable between on-ASMs and off-ASMs epochs.

Significance: There is stable altered BRLFC in the SOA and peri-SOA expressed in the background icEEG of patients with focal epilepsy. This altered BRLFC may be a network marker of medically intractable focal epilepsy which is related to outcome of epilepsy surgery.

Introduction: Monitoring sleep-disordered breathing typically requires many sensors, including pneumoflow masks, measuring nasal and oral airflow, and esophageal pressure catheters. While these tools provide detailed information about airflow, effort, and respiratory mechanics, they can be uncomfortable, invasive, and less feasible for long-term, home-based, or large-scale sleep studies. In contrast, respiratory inductance plethysmography (RIP) belts offer a non-invasive and well-tolerated alternative.

Methods: In this study, we introduce four models that estimate key physiological signals from either RIP-belt data or pneumoflow mask data. Specifically, we present a heart rate model based on the RIP-belt signal, a nasal pneumoflow model estimating airflow from the RIP-belt signal, and two esophageal pressure models - one based on the RIP-belt signal, and the other one based on pneumoflow mask data. Data from 55 participants with varying degrees of sleep-disordered breathing were analyzed.

Results: When fitted to each participant individually, the heart rate model as well as the nasal pneumoflow model achieved a mean Pearson correlation of 0.60. The esophageal pressure model, using RIP-belt data, yielded a mean Pearson correlation of 0.65, while the model using pneumoflow mask data yielded a mean Pearson correlation of 0.52.

Discussion: Although these models do not replace gold-standard instruments, they provide physiologically interpretable estimates from non-invasive inputs and demonstrate potential for scalable, lower-burden sleep monitoring, and highlight the potential of considering physiological interconnectedness to extract desired information. Future work will focus on further validation and clinical diagnostic utility.

While the role of topology is established in active matter systems, its importance in cardiac electrophysiology, particularly concerning common arrhythmias, warrants further emphasis. Atrial fibrillation (AF), atrial flutter (AFL), and focal atrial tachycardia (FAT) are the most prevalent arrhythmias impacting human health. This article proposes a framework conceptualizing these atrial rhythm disturbances through the lens of topological states and phase transitions, drawing inspiration from the Kosterlitz-Thouless (KT) transition. Central to this framework is the hypothesis that distinct arrhythmia patterns emerge as discrete topological states constrained by the fundamental requirement that the net topological charge (associated with electrical phase singularities or vortices) must sum to zero across the atrial tissue. Within this constrained topological perspective, AF, characterised by disorganised activity, is likened to the KT unbound vortex state, dominated by disorder with repetitive vortex regeneration and an exponential decay in spatial correlation. In contrast, AFL, with its organized regularity, resembles the KT bound vortex state, where vortex-antivortex pairs result in ordered activity. Finally, FAT and Sinus Rhythm are characterized as topologically vortex-free states exhibiting ordered planar wave conduction. Importantly, while the resulting topological states show clear analogies, the specific biophysical mechanisms driving vortex defect formation, interaction, and unbinding in cardiac tissue likely differ significantly from the thermal free-energy considerations governing the classic KT transition. This viewpoint frames the transition between arrhythmias as a change in the topological organization of atrial electrical activity, governed by charge conservation principles and cardiac-specific dynamics. This perspective may offer novel diagnostic and therapeutic avenues applicable to human cardiac mapping procedures.

The Haken-Kelso-Bunz paradigm of motor coordination has instigated experimental research on pattern formation with a focus on body movement in intra- as well as interpersonal contexts. The current research on interpersonal synchrony in psychology can be seen to generalize on this initial synergetic approach. A large body of evidence has been aggregated to date showing that synchrony is a common signature of social systems as studied in psychotherapy research, in social psychology and in the dynamics of large groups. Interestingly, such synchronization processes occur spontaneously, generally outside the awareness of the individuals involved in them. Novel qualities arise due to interpersonal synchrony, which is reminiscent of self-organization as conceived by Haken's Synergetics. The degree of synchrony of physiological and behavioral processes was often found associated with cognitive and emotional variables and is thus considered an important aspect of 'embodied cognition'. Therefore, synchrony additionally points to circular causality in mind-body relations and throws a light on the synergetic slaving principle in psychology.