Frontiers in network physiology最新文献

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.

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.

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.

Introduction: Cardiovascular 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.

Methods: A 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.

Results: The 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.

Discussion: SHAP 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.

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.

Background: Severe 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.

Aim: To 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.

Methods: Three 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).

Results: Preliminary 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).

Conclusion: ECHO-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.