Hypoxic-ischemic encephalopathy (HIE) remains a critical challenge in neonatal intensive care, requiring precise bedside monitoring to mitigate long-term neurological morbidity. While individual assessment of cerebral and cardiovascular activity is standard, the dynamic interaction between these systems provides untapped diagnostic value. This pilot study investigates neuro-cardiac coupling using synchronized long-term EEG and ECG recordings, focusing primarily on a core subset of 61 neonates classified by clinical HIE severity (Sarnat Stage 1 vs. Stage 2). We employed cross-modal analysis to evaluate the relationship between the amplitude-integrated EEG (aEEG) envelope and heart rate variability (HRV) metrics. Our results demonstrate that neuro-cardiac interaction is significantly modulated by HIE severity and electrocortical background patterns. Neonates with healthy continuous normal voltage (CNV) patterns exhibited robust neuro-cardiac synchronization (median Spearman’s r=0.22), which was significantly reduced by approximately 60% in patients with abnormal EEG backgrounds (median Spearman’s r=0.09, p=0.015). To ensure robust translation, machine learning models were evaluated across diverse parameter grids. While isolated hyperparameter configurations yielded peak performance metrics–such as an overall maximum accuracy of 90.22% using combined cardiocycle and spectral ECG features with XGBoost–the feature space demonstrated strong, un-biased baseline stability, yielding reliable average accuracies across window lengths (e.g., 77.27% mean F1-score for EEG-based Logistic Regression and 72.73% mean F1 for ECG-based XGBoost). These findings suggest that multi-modal coupling metrics offer enhanced physiological insight for automated HIE grading in a pilot setting.
The relationship between the mother and fetus is increasingly recognized not as a simple interaction between two separate entities but as a unified, adaptive, and multiscale physiological network. This scoping review synthesizes current research across psychological, physiological, immunological, genetic, and cellular domains to map the “communicatome” that orchestrates pregnancy and programs lifelong health. Centrally, we examine the coupling of maternal and fetal heart rate variability (HRV), a paradigmatic manifestation of network physiology in which maternal autonomic oscillations are mirrored in umbilical venous and ductus venosus blood flow to modulate fetal neurodevelopment. We conclude that the decoupling of this physiological network—detectable through integrated biosignal analysis—serves as a critical early-warning marker for pregnancy pathologies, including pre-eclampsia (PE), fetal growth restriction (FGR), and preterm birth (PTB). Furthermore, the review highlights the active nature of fetal–maternal interactions, in which fetal trophoblasts “educate” the maternal immune system to ensure semi-allogeneic tolerance, and microchimeric cells establish a biological legacy that persists for decades postpartum. Psychologically, prenatal attachment is shown to be a transgenerational process in which the quality of current relationship care mediates the transmission of ancestral parenting patterns. By framing these interactions through the Developmental Origins of Health and Disease, we conclude that the decoupling of this physiological network—detectable through integrated biosignal analysis—serves as a critical early-warning marker for pregnancy pathologies and neurodevelopmental vulnerabilities.
Introduction:Combat-related stress is associated with autonomic nervous system (ANS) dysregulation-sympathetic predominance, reduced vagal tone, and decreased heart rate variability (HRV)-linked to increased cardiovascular risk. Non-pharmacological interventions targeting autonomic regulation show promise, but controlled studies in combat-exposed populations remain limited. We assessed whether adding a structured breathing-meditation protocol, the Technique of Mental Relaxation (TMR), to standard rehabilitation is associated with changes in HRV and electrocardiogram (ECG)-derived indices of autonomic regulation in Ukrainian servicemen. Methods:A controlled, non-randomized study screened 84 male servicemen; 77 were allocated to a TMR group (n = 37; TMR plus standard treatment) or a control group (n = 40; standard treatment alone). Paired baseline and post-treatment 5-min supine recordings, obtained over a 14-day rehabilitation period, were available for 74 participants (TMR n = 35, control n = 39); a civilian reference group (n = 89) provided baseline comparison. Primary HRV outcomes were SDNN, RMSSD, the Baevsky Stress Index, and heart rate; ECG analysis added repolarization intervals, with Hedges' g. Results:At baseline, both military groups showed impaired autonomic regulation versus civilians (all p < 0.0001). After intervention, the TMR group improved (SDNN 23→30 ms, p < 0.001; RMSSD 12→19 ms, p < 0.001; Stress Index 422→253, p < 0.001), whereas controls deteriorated; post-treatment between-group differences were significant for all primary outcomes (p ≤ 0.008). Conclusion:Heart-rate-adjusted analyses attenuated part of this difference, the Stress Index remaining most robust. Given the non-randomized design, these findings warrant caution and support randomized trials, with HRV as an objective monitoring tool.
BackgroundOptic nerve sheath diameter (ONSD) measured by ultrasound provides a non-invasive surrogate of intracranial pressure. Although lower body negative pressure (LBNP) is widely used to simulate central hypovolemia, the dose-response relationship between graded LBNP and ONSD, and possible sex differences therein, are incompletely characterized.MethodsForty-four healthy young adults (22 female, 22 male) underwent graded LBNP (−10 to −40 mmHg) in the supine position; valid ONSD data were available in 37 participants (18 female, 19 male) who constituted the analytical sample. ONSD was measured from transbulbar B-mode ultrasound images 3 mm posterior to the globe, with three representative frames averaged per condition. Relative changes from individual supine baseline were analyzed using a linear mixed-effects model with state, sex, and their interaction as fixed effects.ResultsA progressive and statistically significant reduction in ONSD was observed at every LBNP level in both sexes. Female participants showed reductions of approximately −4.4% (LBNP10, p = 0.001), −9.1% (LBNP20, p < 0.001), −13.2% (LBNP30, p < 0.001) and −15.9% (LBNP40, p < 0.001) relative to supine baseline. Male-specific contrasts showed comparable reductions (LBNP10: −3.0%, p = 0.022; LBNP20: −7.9%, p < 0.001; LBNP30: −10.1%, p < 0.001; LBNP40: −13.4%, p < 0.001). The main effect of sex was not significant (p = 1.00), and no state × sex interaction reached significance (all p > 0.1). ONSD returned to baseline during recovery (female: p = 0.173; male: p = 0.059).ConclusionGraded LBNP induces a progressive, dose-dependent reduction in ONSD that is detectable already at mild LBNP (−10 mmHg) and reaches approximately −15% at −40 mmHg. No statistically significant sex-related differences in the ONSD response to graded LBNP were detected in the present study. These findings support the use of transbulbar ONSD ultrasound as a sensitive non-invasive marker consistent with intracranial-pressure reductions during simulated central hypovolemia. By linking a systemic cardiovascular perturbation to a cerebro-ocular pressure readout, the study offers a network-physiology view of central-hypovolemia responses relevant to spaceflight and orthostatic-stress research.
Human spaceflight exposes individuals to prolonged, multifactorial stressors, including microgravity, radiation, isolation, confinement, altered light-dark cycles, and operational demands, that affect biological, cognitive, psychological, behavioral, and social domains. These stressors may contribute to body deconditioning, dysregulated stress responses, sleep disruption, increased pain vulnerability, impaired cognition, mood disturbances, and interpersonal conflict. As human space exploration moves toward longer missions beyond low Earth orbit, effective countermeasures will require small, portable, autonomous, low-power technologies capable of continuously monitoring interconnected systems and delivering personalized interventions in real-time. In this perspective, we propose that the consequences of spaceflight are best understood through a network physiology framework, in which stress regulation, sleep, pain, cognition, mood, social interaction, and other physiological functions are viewed as dynamically coupled components of an integrated system. Within this framework, disturbances in one domain may propagate to other domains, reducing resilience and increasing vulnerability to multisystem dysfunction. We discuss how advances in multimodal wearable and habitat-integrated sensing, combined with AI-based analysis, enable continuous monitoring in space-relevant environments. Body-worn and ambient sensors can capture neural, autonomic, cardiovascular, thermal, and behavioral signals, enabling longitudinal assessment of system-level adaptation. Integrating these signals with AI-based analysis may help identify deviations from adaptive network states, derive markers of multisystem resilience, and guide personalized countermeasures. We further discuss the potential of AI-guided, closed-loop, non-pharmacological interventions to restore physiological balance and maintain performance during long-duration missions. Beyond spaceflight, this framework may also inform precision health approaches to multisystem dysfunction on Earth.
Electrical remodelling is often seen as a key determinant in the development of atrial fibrillation. From a computational electrical modelling point of view, remodelling is usually incorporated by a global (within a specific atrial region) change of model parameters, e.g., ionic conductances and calcium handling properties. Given the spatially heterogeneous nature of cardiac tissue, one may expect that remodelling develops in a non-uniform manner. Computational models sometimes reflect this when fibrotic regions are considered; however, in the case of spatially heterogeneous electrical remodelling the literature is much sparser. Here we demonstrate that a spatially heterogeneous mixture of remodelled and non-remodelled regions can cause a destabilizing effect in simulated tissue paced at a sufficiently high frequency, indicating a potential mechanism for the initiation of fibrillatory behaviour. In particular, we use an established model of human atrial action potential with parameters based on sinus rhythm and chronic atrial fibrillation, and we distribute these two possible states according to realisations of the 2-dimensional Ising model. We show that there is a window of pacing frequencies where turbulent propagation behaviour (via the break up of incoming wavefronts) is sustained, and that a long turbulent transient can be sustained when adapting to fast pacing from a lower pacing frequency. These results might help us think about the mechanisms embodied in the aphorism ‘atrial fibrillation begets atrial fibrillation’, namely, that partially remodelled tissue could emerge in an intermediate stage of disease progression, promoting the conditions for the tissue to progress to becoming globally remodelled.
BackgroundThis companion empirical study established fundamental heterogeneity in pre-arrhythmic dynamical pathways across nine PhysioNet cohorts (N > 1,500 records): 93%–98% of events do not follow the canonical suppress-to-late-rise trajectory, Phase V (scaling manifold collapse, R2 < 0.93) is absent in healthy subjects and increases monotonically with disease severity, and healthy dynamics are predominantly supercritical (CHI = 2 (α1 − α2) = +0.334). Although these empirical markers characterize the dynamical state, they lacked formal theoretical grounding. The present study addresses this gap by constructing and validating a minimal stochastic dynamical model of cardiovascular criticality.MethodsA coupled Langevin system, grounded in Haken’s synergetic order-parameter formalism, is reduced to dψ/dt = A (R2)ψ − Bψ + ε(ψ)η(t) via adiabatic elimination. Parameters estimated by Fokker–Planck KL-divergence minimization, cross-cohort regression, and Kramers calibration. Euler–Maruyama and Heun/Stratonovich simulations (N = 50,000; SEED = 42) provide large-noise ground-truth benchmarks.ResultsRc = 0.991, a0 = 20.5, D = 0.06 ± 0.02. A five-condition P_self benchmark (Kramers additive/EM sim/Kramers multiplicative/Heun sim/empirical: 0.42|0.49|0.65|0.50|0.70 for Suppress; 0.50|0.41|0.63|0.43|0.58 for Late-rise) establishes that multiplicative Kramers overestimates simulation in the large-noise regime, quantifying the boundary conditions of Kramers-based approximations. Individual-level estimation (78 patients and 135 mr records) yields ICC = 0.37–0.41, demonstrating real between-patient heterogeneity with dominant within-patient non-stationarity. This ICC range is comparable to that of published values for DFA α1 (ICC ≈0.40–0.60; Penttilä et al., 2001; Aubert et al., 2003) and reflects the expected property of a dynamical rather than trait marker: CHI measures the current phase of the regulatory system, which varies within individuals across states, activity levels, and time of day. Low ICC is, therefore, not a limitation of CHI as a dynamic biomarker but a consequence of measuring a quantity that changes with the physiological context—analogous to blood pressure ICC being lower than body height ICC. High test–retest reliability would indicate that CHI is insensitive to physiological dynamics, which would contradict its role as an order parameter. CHI follows Student’s-t (ν = 12.4; ΔAIC = 74.4 vs. Gaussian). Cross-cohort validation supports P1 (Spearman ρ = 0.867, p = 0.001, N = 10; extended to N = 13 with nsrdb and Fantasia external cohorts, ρ = 0.989; p < 0.0001) and P3 (β = 1.255 [BCa 95%CI: 0.69–2.89], permutation p = 0.014, directionally consistent with pitchfork prediction; exploratory at N = 6). Within-patient temporal prediction (N = 78; 7,555 rolling windows; Cox model) yields HR_CHI = 0.61 (p < 0.001) and HR_PV = 2.31 (p = 0.001); temporal receiver operating characteristic (ROC) area under the curve (AUC) increases from 0.71 to 0.86 as VT/VF onset approaches; Harrell’s C = 0.78 with CHI trend ΔCHI included.ConclusionThis study provides the first formal dynamical framework for the ECSoC empirical markers, deriving the pitchfork bifurcation structure of cardiovascular criticality from the first principles approach and validating it against cross-cohort data and stochastic simulations. The model is structurally validated across analytical, simulation, and empirical domains, with quantitative constraints in the large-noise regime delineating the boundary conditions for Kramers-based approximations. Six falsifiable predictions (P1–P6) define the roadmap for prospective quantitative validation.
Interpersonal synchrony, the temporal correspondence of repeated behavioral, physiological, or neural measures between individuals, has been analyzed using a variety of different methods. However, the analysis method needs to be carefully chosen to ensure an adequate interpretation of the results. Here, we suggest using cross-wavelet power for interpersonal synchrony analysis due to several advantages. We demonstrate the proposed method using the example of heart rate synchrony. In cross-wavelet power analysis, synchrony is determined per frequency band and time point, allowing for both fine-grained frequency and temporal resolution. We argue that applying this approach to analyze synchrony in heart rate data provides additional information about underlying processes that may influence overall heart rate alignment between individuals. We describe the principles of cross-wavelet power analysis and compare the method to the frequently used cross-correlational approach using simulated and real data. Cross-correlation is a linear measure of the similarity between time series, which includes the quantification of leader-follower relationships. We illustrate different implications for which data series are considered to be synchronous and describe the advantages and drawbacks of using cross-wavelet analysis across various possible synchronization scenarios. The main advantage of cross-wavelet power is its high time- and frequency resolution, whereas cross-correlation is more suitable when researchers aim to differentiate between synchrony types. Finally, we provide recommendations for implementing cross-wavelet power analysis, including R code to facilitate the application to one’s own data.
Refractory epilepsy remains a significant clinical challenge, affecting over a third of patients with epilepsy and drastically increasing their risk of sudden unexpected death in epilepsy (SUDEP). Furthermore, psychiatric comorbidities - such as depression and anxiety disorders - are highly prevalent among patients with epilepsy. As neuromodulatory therapeutic approaches evolve, it is paramount that novel targets for stimulation are explored that may concomitantly reduce seizure burden, ameliorate psychiatric comorbidities, and/or reduce SUDEP risk. Here, we review physiological factors that are thought to contribute to SUDEP and which may reduce risk of SUDEP if addressed therapeutically, then we describe how the bed nucleus of the stria terminalis (BNST) represents a forebrain limbic region that concurrently influences many of these physiological processes. We conclude by synthesizing recent findings regarding the BNST in patients and preclinical models of epilepsy and propose that this evidence positions the BNST as a promising extra-thalamic target for therapeutic neurostimulation in patients with refractory epilepsy.
IntroductionInteroception, the perception and interpretation of internal bodily signals, is closely linked to autonomic regulation and emotional functioning. Heart rate variability biofeedback (HRVBF) has been proposed to influence interoceptive processes through modulation of vagally mediated cardiovascular dynamics. The present study investigated physiological correlates of interoception and examined whether an 8-week HRVBF intervention was associated with changes in autonomic regulation and interoceptive processing in healthy adults.MethodsTwenty-five participants completed an 8-week HRVBF training. Resting autonomic function was assessed before and after the intervention using cardiovascular and respiratory indices, including heart rate, heart rate variability, baroreflex sensitivity, respiratory sinus arrhythmia (RSA), respiration rate, and spectral HRV measures. Interoceptive accuracy was assessed using the heartbeat counting task, and interoceptive awareness was assessed using the Multidimensional Assessment of Interoceptive Awareness (MAIA).ResultsAt baseline, lower respiration rate and heart rate were associated with higher scores on several MAIA subscales. The HRVBF intervention was associated with significant multivariate changes in autonomic function, including decreased respiration rate, increased RSA, and elevated baroreflex sensitivity. Interoceptive accuracy improved modestly following the intervention. Interoceptive awareness also showed a significant multivariate change, with largest increases observed in the subscale Self-Regulation.ConclusionIn conclusion, HRVBF was associated with modulation of resting autonomic regulation and improvements in selected dimensions of interoceptive processing, particularly subjective self-regulatory awareness. Associations between physiological and interoceptive changes were modest, suggesting further research is needed to clarify the relationship between autonomic regulation and interoceptive function.
Introduction:Energy landscapes provide a useful lens for understanding multistability and state transitions in self-organizing systems, but systematic characterization of the energy landscapes for coupled oscillator networks remains less well developed. Here, we study a frustrated Stuart-Landau oscillator network on a 2D toroidal lattice with competing local coupling and global frustration and characterize how its macroscopic states and noise-driven transitions reorganize as the frustration strength is varied. Method:We combine a mode-based, phase-decoupled approximation with homotopy continuation and track representative families of equilibria from the approximation to the full model. In singular cases where the leading XY-Hamiltonian phase interaction yields non-isolated critical points, we impose a second-order phase equilibrium condition and solve it in a constrained-solvability sense to ensure that continuation is well posed. Results:Numerical continuation shows partial selectivity of this homotopy, in which lower-energy initializations tend to continue to similarly lower-energy, lower-index equilibria in the full system. Across frustration regimes, continuation shows how approximate equilibria split and reorder by energy and index; at intermediate frustration, extensive runs reveal multistability between an isolated global minimum and multiple 1D troughs contained within the edges of a multigraph. Guided by the resulting landscape-level picture, simulations demonstrate distinct noise-dependent synergetic phenomena, including noise-induced synchronization, noise-induced desynchronization, and persistent switching among coexisting macrostates. Stationary transition kinetics exhibit both Arrhenius-Kramers-like (activated) and diffusion-limited scaling with respect to noise. Discussion:These results support a coupled-oscillator-based framework for analyzing the energy landscape of multivariate time series, with potential applications in neuroscience, physiology, and beyond.
Network physiology is a growing and expanding field analysing the exchange of information across body systems. The main goal is to understand and represent the body in a complex network with the hopes that these tools can bring real clinical value. These concepts may seem foreign to most clinicians, but we intuitively understand this in day-to-day practice. The hypoxic patient with asthma presenting with tachycardia or the patient with sepsis presenting with high temperature, low blood pressure and reduced oxygen saturations. Across several disease states, network-based analysis has shown that the loss of healthy variability is a marker of physiological strain. In hypoxic settings alterations in oxygen saturation variability and its relationship with other physiological parameters provides information about an individual’s tolerance and adaptivity. In respiratory conditions such as Chronic Obstructive Pulmonary DiseaseOPD, changes in oxygen-saturation variability can help distinguish stable periods from early exacerbations, offering opportunities for earlier intervention in remote-monitoring settings. In parallel, studies of heart-rate variability (HRV) and heart-rate complexity show similarly promising correlations. These findings illustrate a recurring pattern: disease shifts the body from a flexible, adaptive physiological state to one that is more regular and less responsive, a change that can be captured through continuous-signal analysis and network physiology. Looking ahead, expanding access to continuous monitoring through wearables, both in hospital and at home, creates an opportunity to integrate these insights into everyday clinical practice. Network-physiology metrics could enhance risk stratification, support early warning systems, and help personalise treatment decisions and support precision medicine. “Digital twins” form part of this future in clinical applications. This overview aims to highlight the current impact and uses for network physiology. This will be a jargon-free entry point into the methods, emphasizing practical interpretation, limitations, and realistic pathways for integrating network physiology into everyday care and future research.
IntroductionVariability in cardiovascular parameters reflects complex interactions among regulatory mechanisms. Arterial stiffness, a key determinant of heart–vessel interaction and cardiac performance, is commonly assessed using pulse wave velocity, though its dependence on blood pressure limits interpretability. The Cardio-Ankle Vascular Index (CAVI), a widely used non-invasive measure of arterial stiffness, and its refined form CAVI0, aim to address this limitation. However, evidence suggests that these measurements may still be affected by factors such as vascular smooth muscle tone and heart rate. This study investigated the short-term effects of physiological stressors, including body position changes (considering hydrostatic pressure) and cognitive load on CAVI0, to better understand the influence of confounding factors on arterial stiffness assessment.MethodsTwenty-three healthy adults (age 22.3 ± 2.4 years, 16 male) were recruited. CAVI0 was measured using the VaSera VS-1500, with adjustments for blood pressure and hydrostatic effects (CAVI0ADJ) during three randomized tilt sequences (10° head-down tilt (HDT), 20° head-up tilt (HUT), and 40° HUT) and a mental arithmetic (MA) task inducing cognitive load. Continuous ECG, volume clamp photoplethysmography, and impedance cardiography recordings were used to derive heart rate, blood pressure, cardiac output, and systemic vascular resistance.ResultsCAVI0ADJ increased progressively, showing significant elevations at 20° and 40° HUT versus supine and 10° HDT. Brachial mean blood pressure did not change across postural positions, whereas heart rate and systemic vascular resistance increased during upright postures. Changes in CAVI0ADJ positively correlated with heart rate (p < 0.001) and systemic vascular resistance (p = 0.040), but not with brachial mean blood pressure. During the MA task, brachial mean blood pressure, heart rate, and systemic vascular resistance all increased significantly, whereas CAVI0 remained unchanged.ConclusionOur study shows that CAVI0 is not influenced by acute blood pressure changes caused by moderate mental stress pointing towards its robustness against blood pressure alterations. During head-up tilt, CAVI0 is influenced not only by hydrostatic pressure but also by heart rate and vasomotion, highlighting the importance of considering body position and autonomic responses when assessing arterial stiffness as an indicator of structural changes in arterial wall associated with atherosclerotic process.
Network physiology addresses global physiological states that arise self-organized through the interactions among the individual components of the human organism and is therefore intimately linked to Synergetics with its central concepts of order parameters, enslavement, and circular causality. Explanations of self-organization phenomena invoking these concepts exhibit a distinctive explanatory power and scientific fruitfulness across disciplines, in part due to their appeal to downward causation. Downward causation is not only actively discussed in philosophy and philosophy of science, particularly in debates on reductionism and emergence, but is also attracting considerable interest in scientific research on complex systems. However, at least when understood in ontological terms, it remains a philosophically delicate and contested notion.
As the most extreme course of an infectious disease, sepsis poses a serious health threat, with a high mortality rate and frequent long-term consequences for survivors. Despite its enormous burden on global healthcare and ongoing research efforts, early sepsis onset prediction remains challenging due to the complex nature of its pathophysiology. Current approaches face a fundamental trade-off: data-driven machine learning models achieve strong performance but lack interpretability, while biologically inspired models provide mechanistic insights but have limited clinical validation. In this study, we propose the Latent Dynamics Model, a hybrid machine learning approach that integrates a functional model of coupled oscillators representing organ- and immune-cell populations and their interactions. Here, the model parameters encode physiological conditions and allow for an interpretable differentiation between healthy and pathological states. By projecting high-dimensional patient data into the low-dimensional parameter space of the functional model, machine-learned trajectories through this space allow the prediction of critical organ system states and simultaneously offer interpretability beyond plain risk estimates. The proposed method is trained and evaluated on real intensive care patients, achieving competitive AUROC/AUPRC performance on a retrospective MIMIC-IV cohort. Additional qualitative analysis reveals that the learned trajectories exhibit clinically plausible patterns of deterioration, recovery, and stability. We demonstrate that a physiological network model can be embedded within a deep learning architecture without compromising predictive performance while providing an interpretable latent structure for sepsis onset prediction.
Spinal cord injury (SCI) disrupts locomotion by affecting multiple physiological domains, including spinal conduction, reflex excitability, gait kinematics, and behavioral performance. Because recovery may be reflected not only in individual readouts but also in the organization of relationships among these readouts, we assessed post-SCI rehabilitation using a multi-domain correlation-network approach. Adult female Wistar rats underwent mild thoracic contusion SCI and were assigned to an untreated (SCI) or a treadmill-trained (SCI + TMT) group. Locomotor recovery was evaluated using treadmill testing, open-field walking, and the ladder rung test. Video recordings were processed using DeepLabCut/ALMA-derived kinematic variables and were combined with behavioral scores, electrophysiological measures, and morphometric assessment. Group differences in kinematic parameters were evaluated using Mann-Whitney U tests with Benjamini-Hochberg false-discovery-rate correction, and correlation-network graphs were constructed from Spearman associations between selected electrophysiological and kinematic indicators. At 28 days post-injury (dpi), treadmill rehabilitation was associated with partial normalization of open-field stride-time metrics: median stride time recovered by 83%-84% toward the intact baseline for hind paws and by 81% for knee points relative to the SCI-intact difference, while stride-time variability normalized by 92%-100% for knee points. The hindlimb-to-forelimb stride-time ratio was closer to the intact value in SCI + TMT than in untreated SCI animals (1.03 vs. 1.25; intact: 1.00). Correlation-network analysis after multiple-comparison correction identified a limited set of associations involving bilateral H-wave amplitude, contralateral H-wave latency-hindlimb gait timing, and hindlimb/knee stride-interval indicators in the trained group. These findings suggest that treadmill rehabilitation after SCI is reflected not only in individual locomotor metrics but also in the organization of cross-domain statistical associations.
Introduction As the global population ages, declines in sensory, cognitive, and motor functions increase fall risk and compromise postural stability. Despite growing research, systematic reviews examining visual attention and postural stability—particularly through eye-tracking—remain scarce. Grounded in a network physiology perspective, this review aimed to (1) describe bibliometric characteristics, (2) thematize study purposes and outcomes, (3) map conceptual linkages through keyword network analysis, and (4) generate insights into the relationship between visual attention and postural stability in older adults during physical exercise interventions.MethodsFollowing a five-stage integrative review process using Covidence, 15 studies were included in the final analysis.ResultsBibliometric findings show that publications were multi-authored (93.33%), appeared in health-related journals (100%), peaked in 2022 (20.00%), and originated predominantly from the Americas (46.67%). Most studies focused on diagnosis (26.67%), involved healthy participants (46.67%), and used small samples (Md = 30; IQR = 28) in laboratory or clinical settings. Thematic analysis yielded five domains—Performance, Program, Process, Product, and Person (5Ps)—while keyword network analysis identified seven clusters: Vision, Virtual, Vulnerability, Visual-Motor, Velocity, Vestibular, and Vergence, collectively emphasizing a multifaceted, systems-oriented approach to fall prevention. Among visual attention metrics, fixation (21.15%) and saccades (19.23%) were most frequently assessed, and both were consistently associated with posture, balance, gait, and stability outcomes. Advanced metrics such as heat maps, pupil dilation, dwell time, and re-fixation remain underutilized.DiscussionOverall, this review establishes visual attention as a central and modifiable determinant of postural stability, with gaze-based, technology-assisted, multicomponent approaches warranting priority in fall prevention assessment and intervention.Systematic Review Registrationhttps://osf.io/bmsw6/overview.
Cervical cancer remains a major global health burden, yet current screening tools lack precision in identifying which women with high-risk human papillomavirus (HPV) infection will progress to high-grade lesions or cancer. Within a network-physiology framework, cervical carcinogenesis is viewed as emerging from dynamic interactions between viral dynamics, host immunity, vaginal ecology, vaccination status and behaviour rather than from isolated risk factors. This perspective review examines artificial intelligence (AI) approaches for cervical cancer prediction and evaluates the emerging role of the vaginal microbiome as a complementary biomarker within these interconnected physiological networks. The review synthesises evidence linking non-Lactobacillus-dominated or Lactobacillus iners-rich vaginal communities with increased HPV persistence and cervical intraepithelial neoplasia, contrasted with protective Lactobacillus crispatus-dominant communities, and outlines how these ecological signatures could be combined with HPV genotype and clinical factors in multi-modal models. A structured narrative synthesis of published AI tools demonstrates that current prognostic, diagnostic and screening algorithms rely mainly on demographic, clinical or imaging variables, with no validated models yet integrating vaginal microbiome profiles into cervical cancer risk calculators. The manuscript proposes a technical framework for microbiome-enabled modelling, covering feature engineering from community state types, algorithm selection, handling of high-dimensional omics data, and staged validation in NHS-relevant populations. Finally, it outlines a translational pathway for embedding microbiome-informed risk models into cervical screening using self-collected tampon sampling, AI-driven triage and digital decision support, and identifies key unmet needs, including longitudinal multi-omic cohorts, international consortia and robust bias auditing.
The aim of this study is to discuss the adoption of the popular natural visibility graph method (NVGM) to create complex networks and explore the condition, ignored by early work on this subject, where the dynamical system under analysis is characterized by a deviation from the ordinary ergodic condition, as a consequence of turbulent events (crucial events) with an inverse power law index μ ranging from 1 to ∞. In the long-time limit for 2<μ<3, the non-ergodic signal becomes virtually indistinguishable from the fractional Gaussian noise (FGN), hypothesized from the NVGM theory. To identify a genuine FGN, the method of statistical analysis, known as diffusion entropy analysis (DEA), with stripes is used. The adoption of DEA with stripes shows that the scaling δ is significantly reduced by meditation. The adoption of NVGM has the effect of generating a homogeneous network. This analysis yields a significant difference between sick and healthy patients. In the case of healthy patients, the adoption of stripes leaves the value of the scaling virtually unchanged, while for sick patients, the adoption of stripes yields a significantly lower value of δ, suggesting that their heartbeats host a FGN contribution. To explain the influence of meditation on δ, it is necessary to make the popular linear response theory by Kubo and his quantum mechanical prescription compatible with ergodicity breaking, using a master equation approach (MEA). The non-Markov MEA hosts ergodicity-breaking crucial events, leading to establish a connection with the literature on macroscopic effects of quantum mechanics. About the paradoxical effect of meditation-induced scaling reduction, it is suggested that more attention should be devoted to the statistical analysis of physiological processes after meditation.
BackgroundCardiac pacemakers exemplify how minimal external reference signals can restore functional stability in dysregulated biological systems without correcting the underlying etiology. This epistemological model suggests broader applications for understanding physiological support mechanisms beyond the heart.ObjectiveThis Perspective proposes geometric pacing as a general physiological principle whereby externally provided rhythmic, spatial, or proprioceptive references support self-organization in systems characterized by regulatory instability rather than structural pathology.FrameworkDrawing on coordination dynamics, autonomic regulation, polyvagal perspectives, and evidence from cueing interventions in neurological rehabilitation, the article develops a conceptual framework applicable to aging, persistent physical symptoms, and pre-pathological states marked by fatigue, autonomic lability, reduced adaptive reserve, and inefficient multisystem coordination. The argument is explicitly situated within Network Physiology by treating stabilization as an emergent property of interactions across autonomic, sensorimotor, interoceptive, and postural subsystems operating across multiple temporal scales.ConclusionGeometric pacing is proposed as a rate-modifying, non-specific support mechanism that may reduce regulatory uncertainty and energetic cost by constraining unstable system dynamics toward more coherent and efficient states. Rather than targeting isolated organs, the framework addresses organism-level coordination and cross-system integration. The article deliberately avoids proposing specific devices or protocols, instead articulating a principle intended to invite interdisciplinary investigation within the broader field of Network Physiology while preserving flexibility for future clinical, environmental, rehabilitative, or technological implementations.
