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        <title>Frontiers in Network Physiology | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/network-physiology</link>
        <description>RSS Feed for Frontiers in Network Physiology | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-04T22:11:11.735+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2026.1774273</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2026.1774273</link>
        <title><![CDATA[Metastability induced by non-reciprocal adaptive couplings in Kuramoto models]]></title>
        <pubdate>2026-03-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sayantan Nag Chowdhury</author><author>Hildegard Meyer-Ortmanns</author>
        <description><![CDATA[Non-reciprocal couplings are frequently found in systems out-of-equilibrium such as neuronal networks. Via bifurcation analysis and numerical integration we consider generalized Kuramoto models with non-reciprocal adaptive couplings. The non-reciprocity refers to the type of couplings according to Hebbian or anti-Hebbian rules and to different time scales on which the couplings evolve. The main effect of this specific combination of deterministic dynamics is an induced metastability of anti-phase synchronized clusters of oscillators. The time series exhibit random features but arise from deterministic dynamics. We analyze the metastability as a function of the system parameters, in particular of the size and the network connectivity. Metastable switching is typical for neuronal networks and a characteristic of brain dynamics. The mechanism behind the observed sudden changes in the order parameters is individual oscillators which change their cluster affiliation from time to time, providing “weak ties” between clusters of synchronized oscillators, where an individual oscillator may represent an entire brain area. This mechanism provides an alternative way of inducing metastability in the oscillatory system to switching events as result of heteroclinic dynamics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2026.1677209</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2026.1677209</link>
        <title><![CDATA[Assessing effects of vibroacoustic stimulation compared to a guided mindfulness meditation using the biosignal of human speech]]></title>
        <pubdate>2026-03-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Charlotte Fooks</author><author>Oliver Niebuhr</author>
        <description><![CDATA[IntroductionHigh stress and low wellbeing pose severe individual, societal and economic threats, and there is a pressing demand for non-invasive stress reduction tools. This exploratory pilot study assessed the efficacy of speech prosody as a biosignal for stress elicitation, when comparing relaxation outcomes of two interventions with a control group.MethodThirty participants were divided into three treatment groups; (1) guided mindfulness meditation (2) vibroacoustic intervention (3) control. All participants read aloud a text before and after one 20-min treatment. The sixty readings were assessed using a multi-parametric acoustic-prosodic analysis, and within-speaker differences were compared between the initial and final reading.ResultsResults show groups (1) and (2) spoke with a breathier vocal quality in the second reading, while group (3) speech was tenser and at a lower, less variable loudness.DiscussionResults demonstrate speech prosody is a sensitive biomarker for treatment-effect classification and evaluation. Practical limitations and future research perspectives are discussed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2026.1720336</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2026.1720336</link>
        <title><![CDATA[Modelling brain metabolism with interacting nonautonomous phase oscillators]]></title>
        <pubdate>2026-02-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Samuel J. K. Barnes</author><author>Anaí Echeverría</author><author>Joshua Hawley</author><author>Yevhen F. Suprunenko</author><author>Aneta Stefanovska</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2026.1761610</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2026.1761610</link>
        <title><![CDATA[Effects of inspiratory muscle training on cardiorespiratory network physiology: evidence from cardiac autonomic modulation, respiratory sinus arrhythmia, and baroreflex sensitivity analysis]]></title>
        <pubdate>2026-02-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Thiago Rodrigues Gonçalves</author><author>Selena Cristina Henriques Fontes</author><author>Michele Vaz Canena</author><author>Deysiane Peres da Silva Clemente de Oliveira</author><author>Pedro Paulo da Silva Soares</author><author>Gabriel Dias Rodrigues</author>
        <description><![CDATA[IntroductionInspiratory 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.MethodsTwenty-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.ResultsIMT 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.DiscussionIn 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.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2026.1701638</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2026.1701638</link>
        <title><![CDATA[Detection and characterization of physiological network interactions in pulsatile motion of cranial blood vessels using real-time MRI]]></title>
        <pubdate>2026-02-16T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Thorge von der Ohe</author><author>Vitali Telezki</author><author>Sabine Hofer</author><author>Peter Dechent</author><author>Martin Uecker</author><author>Mathias Bähr</author><author>Stefan Luther</author><author>Ulrich Parlitz</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2026.1741770</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2026.1741770</link>
        <title><![CDATA[Beyond hypertrophy: a network physiology perspective on the cardio-neuromuscular trade-off in elite soccer]]></title>
        <pubdate>2026-02-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zacharias Papadakis</author><author>Nikolaos Koutlianos</author><author>Vassilios Panoutsakopoulos</author><author>Evangelia Kouidi</author>
        <description><![CDATA[IntroductionConventional 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.MethodsNineteen 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.ResultsThe 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.DiscussionIn 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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1728848</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1728848</link>
        <title><![CDATA[Optimising anti-seizure medication timing using a dynamic network model of seizure rhythms]]></title>
        <pubdate>2026-01-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jake Ahern</author><author>Udaya Seneviratne</author><author>Wendyl D’Souza</author><author>Mark J. Cook</author><author>John R. Terry</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1681597</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1681597</link>
        <title><![CDATA[Empirical evidence for structural balance theory in functional brain networks]]></title>
        <pubdate>2026-01-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Majid Saberi</author><author>Abolfazl HaqiqiFar</author><author>AmirHussein Abdolalizadeh</author><author>Bratislav Misic</author><author>Ali Khatibi</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1441949</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1441949</link>
        <title><![CDATA[Spatial and spectral structure of local functional connectivity of the background intracranial EEG in patients with focal epilepsy]]></title>
        <pubdate>2026-01-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hitten P. Zaveri</author><author>Steven M. Pincus</author><author>Irina I. Goncharova</author><author>Reshma Munbodh</author><author>Lawrence J. Hirsch</author><author>Robert B. Duckrow</author><author>Dennis D. Spencer</author>
        <description><![CDATA[PurposeTo 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.MethodsThis 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 findingsBRLFC 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.SignificanceThere 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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1739213</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1739213</link>
        <title><![CDATA[Applications of synergetics in psychology: interpersonal synchrony in social systems]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Wolfgang Tschacher</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1710567</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1710567</link>
        <title><![CDATA[A topological hypothesis for atrial fibrilllation, atrial flutter and focal atrial tachycardia: comparison and contrast with Kosterlitz-Thouless physics]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Anand Narayan Ganesan</author><author>Pawel Kuklik</author><author>Stanley Nattel</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1625947</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1625947</link>
        <title><![CDATA[Comfortable sleep monitoring: using physiological process interconnectedness during sleep for novel software sensors]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anna Bavarsad</author><author>Elias August</author><author>Erna Sif Arnardóttir</author>
        <description><![CDATA[IntroductionMonitoring 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.MethodsIn 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.ResultsWhen 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.DiscussionAlthough 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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1687132</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1687132</link>
        <title><![CDATA[Quantifying coupling and causality in dynamic bivariate systems: a unified framework for time-domain, spectral, and information-theoretic analysis]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Laura Sparacino</author><author>Helder Pinto</author><author>Chiara Barà</author><author>Yuri Antonacci</author><author>Riccardo Pernice</author><author>Ana Paula Rocha</author><author>Luca Faes</author>
        <description><![CDATA[Understanding the underlying dynamics of complex real-world systems, such as neurophysiological and climate systems, requires quantifying the functional interactions between the system units under different scenarios. This tutorial paper offers a comprehensive description to time, frequency and information-theoretic domain measures for assessing the interdependence between pairs of time series describing the dynamical activities of physical systems, supporting flexible and robust analyses of statistical dependencies and directional relationships. Classical time and frequency domain correlation-based measures, as well as directional approaches derived from the notion of Granger causality, are introduced and discussed, along with information-theoretic measures of symmetrical and directional coupling. Both linear model-based and non-linear model-free estimation approaches are thoroughly described, the latter including binning, permutation, and nearest-neighbour estimators. Special emphasis is placed on the description of a unified framework that establishes a connection between causal and symmetric, as well as spectral and information-theoretic measures. This framework enables the frequency-specific representation of information-theoretic metrics, allowing for a detailed investigation of oscillatory components in bivariate systems. The practical computation of the interaction measures is favoured by presenting a software toolbox and two exemplary applications to cardiovascular and climate data. By bridging theoretical concepts with practical tools, this work enables researchers to effectively investigate a wide range of dynamical behaviours in various real-world scenarios in Network Physiology and beyond.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1578562</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1578562</link>
        <title><![CDATA[Evaluation of deep learning tools in medical diagnosis and treatment of cancer: research analysis of clinical and randomized clinical trials]]></title>
        <pubdate>2026-01-05T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Rawad Hodeify</author>
        <description><![CDATA[Artificial Intelligence and machine learning tools have brought a revolution in the healthcare sector. This has allowed healthcare providers, patients, and public to be at pole position -amidst the key consideration and barriers-to attain precision and personalized medicine. Deep Learning (DL) is a branch of machine learning and AI that has become transformative for healthcare and biomedicine, providing the ability to analyze large, complicated data, capture abstract patterns, and present fast and accurate predictions. DL models are based on complex neural networks that emulate biological neural networks. In this paper, our goal is to evaluate DL algorithms in clinical trials stratified per cancer type and present future perspectives on the most promising DL approaches. We systematically reviewed articles on deep learning in cancer diagnostics in studies published in the Pubmed database. The searched literature included two types of articles, clinical trials, and randomized controlled trials. The deep learning algorithms used in the targeted literature are reviewed, and then we evaluated the performance of the algorithms used in disease prediction and prognosis. We aim to highlight the promising DL approaches reported per cancer type. Finally, we present current limitations and potential recommendations in large-scale implementation of deep learning and AI in cancer care.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1658470</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1658470</link>
        <title><![CDATA[Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection]]></title>
        <pubdate>2025-12-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abdul Zahir Baratpur</author><author>Hamed Vahdat-Nejad</author><author>Emrah Arslan</author><author>Javad Hassannataj Joloudari</author><author>Silvia Gaftandzhieva</author>
        <description><![CDATA[IntroductionCardiovascular diseases, particularly Coronary Artery Disease (CAD), remain a leading cause of mortality worldwide. Invasive angiography, while accurate, is costly and risky. This study proposes a non-invasive, interpretable CAD prediction framework using the Z-Alizadeh Sani dataset.MethodsA hybrid decision tree–AdaBoost method is employed to select 30 clinically relevant features. To prevent data leakage, SMOTE oversampling is applied exclusively within each training fold of a 10-fold cross-validation pipeline. The Support Vector Machine (SVM) model is optimized using Bayesian hyperparameter tuning and compared against Sea Lion Optimization Algorithm (SLOA) and grid search. SHapley Additive exPlanations (SHAP) analysis is utilized to interpret the feature contributions.ResultsThe SVM_Bayesian model achieves 97.67% accuracy, 95.45% precision, 100.00% sensitivity, 97.67% F1-score, and 99.00% AUC, outperforming logistic regression (93.02% accuracy, 92.68% F1-score), random forest (95.45% accuracy, 93.33% F1-score), standard SVM (77.00% accuracy), and SLOA-optimized SVM (93.02% accuracy). Ablation studies and Wilcoxon signed-rank tests confirm the statistical superiority of the proposed model.DiscussionSHAP analysis reveals clinically meaningful feature contributions (e.g., Typical Chest Pain, Age, EFTTE). 95% bootstrap confidence intervals and temporal generalization on an independent test set ensure robustness and prevent overfitting. Future work includes validation on external real-world datasets. This framework provides a transparent, generalizable, and clinically actionable tool for CAD risk stratification, aligned with the principles of network physiology by focusing on interconnected cardiovascular features in predicting systemic disease.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1729999</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1729999</link>
        <title><![CDATA[Signal propagation in small networks of Hodgkin-Huxley neurons]]></title>
        <pubdate>2025-12-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tatiana R. Bogatenko</author><author>Konstantin S. Sergeev</author><author>Galina I. Strelkova</author>
        <description><![CDATA[The study of neuron models and their networks is a riveting topic for many researchers worldwide because it allows to glimpse the fundamental processes using accessible methodology. The paper considers dynamics of small networks of Hodkin-Huxley neurons, namely a chain of three neurons and a small-world-like network of seven neurons. The ensembles of neurons are represented by systems of ordinary differential equations, so the research has been conducted numerically. It has been found that complex quasi-periodic and chaotic regimes may arise in the systems, and the existense of such regimes is caused by the inner parameters of the systems, such as individual currents of the neurons and the coupling between them. This research contributes to the fundamental understanding of signal propagation in networks of neuron models and may provide insight into the physiology of real neuronal systems.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1701758</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1701758</link>
        <title><![CDATA[Towards standardizing mitral transcatheter edge-to-edge repair with deep-learning algorithm: a comprehensive multi-model strategy]]></title>
        <pubdate>2025-11-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Silvia Corona</author><author>Théo Godefroy</author><author>Olivier Tastet</author><author>Denis Corbin</author><author>Thomas Modine</author><author>Stephan von Bardeleben</author><author>Frédéric Lesage</author><author>Walid Ben Ali</author>
        <description><![CDATA[BackgroundSevere mitral valve regurgitation requires comprehensive evaluation for optimal treatment. Initial screening uses transthoracic echocardiography (TTE), followed by transesophageal echocardiography (TEE) to determine eligibility for adequate intervention. Mitral Transcatheter Edge-to-Edge Repair (M-TEER) indications are based on detailed and quality valve and sub-valvular apparatus assessment, including anatomy and regurgitation pathophysiology.AimTo develop AI algorithms for standardizing M-TEER eligibility assessment using TTE and TEE echocardiograms, supporting all stages of mitral valve regurgitation evaluation to assist non-expert centers throughout the entire process, from severe mitral valve regurgitation diagnostic to M-TEER procedure.MethodsThree deep learning algorithms were developed using echocardiographic data from M-TEER patients performed at Montreal Heart Institute (2018–2025). 1. ECHO-PREP was trained to identify key diagnostic views in TTE (n = 530) and diagnostic and procedural views in TEE (n = 2,222) examinations to determine the level of quality images needed to do a M-TEER. 2. 4D TEE segmentation with automated mitral valve area (MVA) quantification (n = 221), and 3. 2D TEE scallop-level segmentation of leaflets and sub-valvular structures (n = 992).ResultsPreliminary results on test sets showed 95.7% accuracy in TTE view classification and 91% accuracy for TEE view classification. The 4D segmentation module demonstrated excellent agreement with manual MVA measurements (R = 0.84, p < 0.001), successfully discriminating patients undergoing M-TEER from those referred for surgical replacement (p = 0.046 for AI predictions). The 2D scallop-level analysis achieved a mean Dice score of 0.534 across 11 anatomical structures, with better performance in commonly represented configurations (e.g., A2-P2, P1-A2-P3).ConclusionECHO-PREP demonstrates the feasibility of an integrated AI-assisted workflow for MR assessment, combining quality control, dynamic 4D valve quantification, and scallop-level anatomy interpretation. These results support the potential of AI to standardize M-TEER eligibility, reduce inter-observer variability, and provide decision support across centers with different levels of expertise.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1692372</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1692372</link>
        <title><![CDATA[Termination of figure-of-eight reentry via resonant feedback pacing]]></title>
        <pubdate>2025-11-19T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Navneet Roshan</author><author>Rupamanjari Majumder</author>
        <description><![CDATA[Sudden cardiac death (SCD) is often precipitated by reentrant arrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF), whose underlying dynamics are frequently sustained by spiral waves of electrical activity. Disrupting these waves can restore normal rhythm, but conventional low-energy pacing strategies are often ineffective in VF, where high-frequency, multi-wave interactions dominate. Resonant feedback-controlled antitachycardia pacing (rF-ATP), which times global electrical stimuli based on real-time feedback from the tissue, has been shown to robustly terminate single spirals under diverse conditions. However, its impact on interacting spiral waves—arguably a more realistic substrate for life-threatening arrhythmias—remains unexplored. Here, we use numerical simulations to investigate the effect of rF-ATP on figure-of-eight reentry, a clinically relevant configuration consisting of two counter-rotating spirals. We show that rF-ATP consistently terminates this pattern, regardless of feedback point location, through two distinct dynamical pathways: mutual collision of phase singularities or annihilation at inexcitable boundaries. We further demonstrate the method’s efficacy across variations in feedback point and spiral arrangement, indicating robustness to geometrical and positional heterogeneity. These results highlight rF-ATP as a promising low-energy intervention for complex reentrant structures and provide mechanistic insight into feedback-driven control of multi-core spiral wave dynamics in cardiac tissue.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1674919</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1674919</link>
        <title><![CDATA[Analysing complex excitation patterns in cardiac tissue using wave event networks]]></title>
        <pubdate>2025-11-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hans Friedrich Von Koeller</author><author>Alexander Schlemmer</author><author>Stefan Luther</author><author>Yannic Döring</author><author>Niels Voigt</author><author>Ulrich Parlitz</author>
        <description><![CDATA[Cardiac dynamics is governed by complex electrical wave patterns, with disruptions leading to pathological conditions like atrial or ventricular fibrillation. Experimentally electrical excitation waves can be made visible by optical mapping using fluorescent dyes. While this imaging technique has enabled detailed studies of cardiac wave dynamics, the manual analysis of activation and phase maps often limits the ability to systematically identify and quantify wave patterns. This study employs a wave tracking algorithm that constructs a graph-based representation of wave dynamics. With that the algorithm detects key events such as wave emergence, splitting, and merging. Applied to both simulated cardiac tissue and experimental data from cell cultures, the algorithm identifies and quantifies wave patterns as wave event networks. Initial results demonstrate its utility in filtering for and focusing on dominant dynamics, providing a robust tool for analyzing cardiac wave patterns. This approach offers potential applications, e.g., to study the effects of external stimuli on cardiac excitation patterns and to better understand the mechanisms involved.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1612495</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1612495</link>
        <title><![CDATA[Metastability in the mixing/demixing of two species with reciprocally concentration-dependent diffusivity]]></title>
        <pubdate>2025-11-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alexander B. Neiman</author><author>Xiaochen Dong</author><author>Benjamin Lindner</author>
        <description><![CDATA[It has been shown before that two species of diffusing particles can separate from each other by the mechanism of reciprocally concentration-dependent diffusivity: the presence of one species amplifies the diffusion coefficient of the respective other one, causing the two densities of particles to separate spontaneously. In a minimal model, this could be observed with a quadratic dependence of the diffusion coefficient on the density of the other species. Here, we consider a more realistic sigmoidal dependence as a logistic function on the other particle’s density averaged over a finite sensing radius. The sigmoidal dependence accounts for the saturation effects of the diffusion coefficients, which cannot grow without bounds. We show that sigmoidal (logistic) cross-diffusion leads to a new regime in which a homogeneous disordered (well-mixed) state and a spontaneously separated ordered (demixed) state coexist, forming two long-lived metastable configurations. In systems with a finite number of particles, random fluctuations induce repeated transitions between these two states. By tracking an order parameter that distinguishes mixed from demixed phases, we measure the corresponding mean residence in each state and demonstrate that one lifetime increases and the other decreases as the logistic coupling parameter is varied. The system thus displays typical features of a first-order phase transition, including hysteresis for large particle numbers. In addition, we compute the correlation time of the order parameter and show that it exhibits a pronounced maximum within the bistable parameter range, growing exponentially with the total particle number.]]></description>
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