<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Network Physiology | Information Theory section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/network-physiology/sections/information-theory</link>
        <description>RSS Feed for Information Theory section in the Frontiers in Network Physiology journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-13T15:05:56.967+00:00</pubDate>
        <ttl>60</ttl>
        <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.2025.1632144</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1632144</link>
        <title><![CDATA[The nature of quantum parallel processing and its implications for coding in brain neural networks: a novel computational mechanism]]></title>
        <pubdate>2025-10-08T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Andrew S. Johnson</author><author>William Winlow</author>
        <description><![CDATA[Conventionally it is assumed that the nerve impulse is an electrical process based upon the observation that electrical stimuli produce an action potential as defined by Hodgkin Huxley (1952) (HH). Consequently, investigations into the computation of nerve impulses have almost universally been directed to electrically observed phenomenon. However, models of computation are fundamentally flawed and assume that an undiscovered timing system exists within the nervous system. In our view it is synchronisation of the action potential pulse (APPulse) that effects computation. The APPulse, a soliton pulse, is a novel purveyor of computation and is a quantum mechanical pulse: i.e., It is a non-Turing synchronised computational event. Furthermore, the APPulse computational interactions change frequencies measured in microseconds, rather than milliseconds, producing effective efficient computation. However, the HH action potential is a necessary component for entropy equilibrium, providing energy to open ion channels, but it is too slow to be functionally computational in a neural network. Here, we demonstrate that only quantum non-electrical soliton pulses converging to points of computation are the main computational structure with synaptic transmission occurring at slower millisecond speeds. Thus, the APPulse accompanying the action potential is the purveyor of computation; a novel computational mechanism, that is incompatible with Turing timed computation and artificial intelligence (AI).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1570530</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1570530</link>
        <title><![CDATA[Complexity synchronization analysis of neurophysiological data: Theory and methods]]></title>
        <pubdate>2025-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ioannis Schizas</author><author>Sabrina Sullivan</author><author>Scott Kerick</author><author>Korosh Mahmoodi</author><author>J. Cortney Bradford</author><author>David L. Boothe</author><author>Piotr J. Franaszczuk</author><author>Paolo Grigolini</author><author>Bruce J. West</author>
        <description><![CDATA[IntroductionWe present a theoretical foundation based on the spontaneous self-organized temporal criticality (SOTC) and multifractal dimensionality μ to model complex neurophysiological and behavioral systems to infer the optimal empirical transfer of information among them. We hypothesize that heterogeneous time series characterizing the brain, heart, and lung organ-networks (ONs) are necessarily multifractal, whose level of complexity and, therefore, their information content is measured by their multifractal dimensions.MethodsWe apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS), and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network-of-organ-networks (NoONs). An automated parameter selection process is proposed that relies on the Kolmogorov-Smirnov statistic to properly choose stripe sizes which are crucial in the MDEA analysis using synthetic duration times derived from the Mittag-Leffler map, shows the strength of KS-based stripe size selection to track changes in the IPL parameter μ. The purpose of this paper is to advance the validation, standardization, and reconstruct-ability of MDEA and CS analysis of heterogeneous neurophysiological time series data.ResultsResults from processing these datasets show that the complexity of brain, heart, and lung ONTS co-vary over time during cognitive task performance in 44% of subjects, while complexity of brain-heart interactions significantly co-vary in 85% of subjects.DiscussionWe conclude that certain principles, guidelines, and strategies for the application of MDEA analysis need further consideration. We conclude with a summary of the MDEA’s limitations and future research directions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1551043</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1551043</link>
        <title><![CDATA[Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics]]></title>
        <pubdate>2025-04-16T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Nuray Vakitbilir</author><author>Amanjyot Singh Sainbhi</author><author>Abrar Islam</author><author>Alwyn Gomez</author><author>Kevin Yuwa Stein</author><author>Logan Froese</author><author>Tobias Bergmann</author><author>Davis McClarty</author><author>Rahul Raj</author><author>Frederick Adam Zeiler</author>
        <description><![CDATA[Cerebral physiological signals embody complex neural, vascular, and metabolic processes that provide valuable insight into the brain’s dynamic nature. Profound comprehension and analysis of these signals are essential for unraveling cerebral intricacies, enabling precise identification of patterns and anomalies. Therefore, the advancement of computational models in cerebral physiology is pivotal for exploring the links between measurable signals and underlying physiological states. This review provides a detailed explanation of computational models, including their mathematical formulations, and discusses their relevance to the analysis of cerebral physiology dynamics. It emphasizes the importance of linear multivariate statistical models, particularly autoregressive (AR) models and the Kalman filter, in time series modeling and prediction of cerebral processes. The review focuses on the analysis and operational principles of multivariate statistical models such as AR models and the Kalman filter. These models are examined for their ability to capture intricate relationships among cerebral parameters, offering a holistic representation of brain function. The use of multivariate statistical models enables the capturing of complex relationships among cerebral physiological signals. These models provide valuable insights into the dynamic nature of the brain by representing intricate neural, vascular, and metabolic processes. The review highlights the clinical implications of using computational models to understand cerebral physiology, while also acknowledging the inherent limitations, including the need for stationary data, challenges with high dimensionality, computational complexity, and limited forecasting horizons.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2024.1361915</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2024.1361915</link>
        <title><![CDATA[The constrained-disorder principle defines the functions of systems in nature]]></title>
        <pubdate>2024-12-18T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Yaron Ilan</author>
        <description><![CDATA[The Constrained Disorder Principle (CDP) defines all systems in nature by their degree of inherent variability. Per the CDP, the intrinsic variability is mandatory for their proper function and is dynamically changed based on pressures. The CDP defines the boundaries of inherent variability as a mechanism for continuous adaptation to internal and external perturbations, enabling survival and function under dynamic conditions. The laws of nature govern the world’s natural phenomena and underlie the function of all systems. Nevertheless, the laws of physics do not entirely explain systems’ functionality under pressure, which is essential for determining the correct operation of complex systems in nature. Variability and noise are two broad sources of inherent unpredictability in biology and technology. This paper explores how the CDP defines the function of systems and provides examples from various areas in nature where the CDP applies, including climate, genetic, biology, and human behavioral variabilities. According to the CDP, system malfunction results from inappropriate performance of the boundaries of inherent variability. The environment influences the physiological variability, and species interactions influence eco-evolutionary outcomes. The CDP defines human behavior as being driven by randomness and accounts for malfunctions and their corrections. The paper reviews variability-based CDP algorithms and CDP-based second-generation artificial intelligence systems and their potential for improving systems’ prediction and efficiency by using variability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2024.1211413</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2024.1211413</link>
        <title><![CDATA[Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness]]></title>
        <pubdate>2024-06-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jacob M. Ryan</author><author>Shreenithi Navaneethan</author><author>Natalie Damaso</author><author>Stephan Dilchert</author><author>Wendy Hartogensis</author><author>Joseph L. Natale</author><author>Frederick M. Hecht</author><author>Ashley E. Mason</author><author>Benjamin L. Smarr</author>
        <description><![CDATA[Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term “manifestations,” as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2024.1385421</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2024.1385421</link>
        <title><![CDATA[Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis]]></title>
        <pubdate>2024-05-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Helder Pinto</author><author>Ivan Lazic</author><author>Yuri Antonacci</author><author>Riccardo Pernice</author><author>Danlei Gu</author><author>Chiara Barà</author><author>Luca Faes</author><author>Ana Paula Rocha</author>
        <description><![CDATA[The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2024.1346424</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2024.1346424</link>
        <title><![CDATA[A method to assess linear self-predictability of physiologic processes in the frequency domain: application to beat-to-beat variability of arterial compliance]]></title>
        <pubdate>2024-04-04T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Laura Sparacino</author><author>Yuri Antonacci</author><author>Chiara Barà</author><author>Dávid Švec</author><author>Michal Javorka</author><author>Luca Faes</author>
        <description><![CDATA[The concept of self-predictability plays a key role for the analysis of the self-driven dynamics of physiological processes displaying richness of oscillatory rhythms. While time domain measures of self-predictability, as well as time-varying and local extensions, have already been proposed and largely applied in different contexts, they still lack a clear spectral description, which would be significantly useful for the interpretation of the frequency-specific content of the investigated processes. Herein, we propose a novel approach to characterize the linear self-predictability (LSP) of Gaussian processes in the frequency domain. The LSP spectral functions are related to the peaks of the power spectral density (PSD) of the investigated process, which is represented as the sum of different oscillatory components with specific frequency through the method of spectral decomposition. Remarkably, each of the LSP profiles is linked to a specific oscillation of the process, and it returns frequency-specific measures when integrated along spectral bands of physiological interest, as well as a time domain self-predictability measure with a clear meaning in the field of information theory, corresponding to the well-known information storage, when integrated along the whole frequency axis. The proposed measure is first illustrated in a theoretical simulation, showing that it clearly reflects the degree and frequency-specific location of predictability patterns of the analyzed process in both time and frequency domains. Then, it is applied to beat-to-beat time series of arterial compliance obtained in young healthy subjects. The results evidence that the spectral decomposition strategy applied to both the PSD and the spectral LSP of compliance identifies physiological responses to postural stress of low and high frequency oscillations of the process which cannot be traced in the time domain only, highlighting the importance of computing frequency-specific measures of self-predictability in any oscillatory physiologic process.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1335808</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1335808</link>
        <title><![CDATA[Gradients of O-information highlight synergy and redundancy in physiological applications]]></title>
        <pubdate>2024-01-09T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Tomas Scagliarini</author><author>Laura Sparacino</author><author>Luca Faes</author><author>Daniele Marinazzo</author><author>Sebastiano Stramaglia</author>
        <description><![CDATA[The study of high order dependencies in complex systems has recently led to the introduction of statistical synergy, a novel quantity corresponding to a form of emergence in which patterns at large scales are not traceable from lower scales. As a consequence, several works in the last years dealt with the synergy and its counterpart, the redundancy. In particular, the O-information is a signed metric that measures the balance between redundant and synergistic statistical dependencies. In spite of its growing use, this metric does not provide insight about the role played by low-order scales in the formation of high order effects. To fill this gap, the framework for the computation of the O-information has been recently expanded introducing the so-called gradients of this metric, which measure the irreducible contribution of a variable (or a group of variables) to the high order informational circuits of a system. Here, we review the theory behind the O-information and its gradients and present the potential of these concepts in the field of network physiology, showing two new applications relevant to brain functional connectivity probed via functional resonance imaging and physiological interactions among the variability of heart rate, arterial pressure, respiration and cerebral blood flow.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1227861</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1227861</link>
        <title><![CDATA[Changes to balance dynamics following a high-intensity run are associated with future injury occurrence in recreational runners]]></title>
        <pubdate>2023-11-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mariana R. C. Aquino</author><author>Joshua J. Liddy</author><author>C. Dane Napoli</author><author>Sérgio T. Fonseca</author><author>Richard E. A. van Emmerik</author><author>Michael A. Busa</author>
        <description><![CDATA[Background: Fatigue is associated with increased injury risk along with changes in balance control and task performance. Musculoskeletal injury rates in runners are high and often result from an inability to adapt to the demands of exercise and a breakdown in the interaction among different biological systems. This study aimed to investigate whether changes in balance dynamics during a single-leg squat task following a high-intensity run could distinguish groups of recreational runners who did and did not sustain a running-related injury within 6 months.Methods: Thirty-one healthy recreational runners completed 60 s of single-leg squat before and after a high-intensity run. Six months after the assessment, this cohort was separated into two groups of 13 matched individuals with one group reporting injury within this period and the other not. Task performance was assessed by the number of repetitions, cycle time, amplitude, and speed. To evaluate balance dynamics, the regularity and temporal correlation structure of the center of mass (CoM) displacements in the transverse plane was analyzed. The interaction between groups (injury, non-injured) and time (pre, post) was assessed through a two-way ANOVA. Additionally, a one-way ANOVA investigated the percent change difference of each group across time.Results: The injured group presented more regular (reduced entropy; 15.6%) and diffusive (increased short-term persistence correlation; 5.6%) CoM displacements after a high-intensity run. No changes were observed in the non-injured group. The within-subject percent change was more sensitive in demonstrating the effects of fatigue and distinguishing the groups, compared to group absolute values. No differences were observed in task performance.Discussion: Runners who were injured in the future demonstrate changes in balance dynamics compared to runners who remain injury-free after fatigue. The single-leg squat test adopted appears to be a potential screening protocol that provides valuable information about balance dynamics for identifying a diminished ability to respond to training and exercise.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1242505</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1242505</link>
        <title><![CDATA[Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions]]></title>
        <pubdate>2023-10-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuri Antonacci</author><author>Chiara Barà</author><author>Andrea Zaccaro</author><author>Francesca Ferri</author><author>Riccardo Pernice</author><author>Luca Faes</author>
        <description><![CDATA[Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact to maintain health. Within the information theory framework the information storage (IS) allows to measure the regularity and predictability of a dynamic process under stationarity assumption. However, this assumption does not allow to track over time the transient pathways occurring in the dynamical activity of a physiological system. To address this limitation, we propose a time-varying approach based on the recursive least squares algorithm (RLS) for estimating IS at each time instant, in non-stationary conditions. We tested this approach in simulated time-varying dynamics and in the analysis of electroencephalographic (EEG) signals recorded from healthy volunteers and timed with the heartbeat to investigate brain-heart interactions. In simulations, we show that the proposed approach allows to track both abrupt and slow changes in the information stored in a physiological system. These changes are reflected in its evolution and variability over time. The analysis of brain-heart interactions reveals marked differences across the cardiac cycle phases of the variability of the time-varying IS. On the other hand, the average IS values exhibit a weak modulation over parieto-occiptal areas of the scalp. Our study highlights the importance of developing more advanced methods for measuring IS that account for non-stationarity in physiological systems. The proposed time-varying approach based on RLS represents a useful tool for identifying spatio-temporal dynamics within the neurocardiac system and can contribute to the understanding of brain-heart interactions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1284256</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1284256</link>
        <title><![CDATA[Editorial: Granger causality and information transfer in physiological systems: basic research and applications]]></title>
        <pubdate>2023-10-13T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Sonia Charleston-Villalobos</author><author>Michal Javorka</author><author>Luca Faes</author><author>Andreas Voss</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1085347</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1085347</link>
        <title><![CDATA[Information theoretic measures of causal influences during transient neural events]]></title>
        <pubdate>2023-05-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kaidi Shao</author><author>Nikos K. Logothetis</author><author>Michel Besserve</author>
        <description><![CDATA[Introduction: Transient phenomena play a key role in coordinating brain activity at multiple scales, however their underlying mechanisms remain largely unknown. A key challenge for neural data science is thus to characterize the network interactions at play during these events.Methods: Using the formalism of Structural Causal Models and their graphical representation, we investigate the theoretical and empirical properties of Information Theory based causal strength measures in the context of recurring spontaneous transient events.Results: After showing the limitations of Transfer Entropy and Dynamic Causal Strength in this setting, we introduce a novel measure, relative Dynamic Causal Strength, and provide theoretical and empirical support for its benefits.Discussion: These methods are applied to simulated and experimentally recorded neural time series and provide results in agreement with our current understanding of the underlying brain circuits.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2022.958550</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2022.958550</link>
        <title><![CDATA[Correlation between heart rate variability and polysomnography-derived scores of obstructive sleep apnea]]></title>
        <pubdate>2022-09-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rafael Rodrigues dos Santos</author><author>Thais Marques da Silva</author><author>Luiz Eduardo Virgilio Silva</author><author>Alan Luiz Eckeli</author><author>Helio Cesar Salgado</author><author>Rubens Fazan</author>
        <description><![CDATA[Obstructive sleep apnea (OSA) is one of the most common sleep disorders and affects nearly a billion people worldwide. Furthermore, it is estimated that many patients with OSA are underdiagnosed, which contributes to the development of comorbidities, such as cardiac autonomic imbalance, leading to high cardiac risk. Heart rate variability (HRV) is a non-invasive, widely used approach to evaluating neural control of the heart. This study evaluates the relationship between HRV indices and the presence and severity of OSA. We hypothesize that HRV, especially the nonlinear methods, can serve as an easy-to-collect marker for OSA early risk stratification. Polysomnography (PSG) exams of 157 patients were classified into four groups: OSA-free (N = 26), OSA-mild (N = 39), OSA-moderate (N = 37), and OSA-severe (N = 55). The electrocardiogram was extracted from the PSG recordings, and a 15-min beat-by-beat series of RR intervals were generated every hour during the first 6 h of sleep. Linear and nonlinear HRV approaches were employed to calculate 32 indices of HRV. Specifically, time- and frequency-domain, symbolic analysis, entropy measures, heart rate fragmentation, acceleration and deceleration capacities, asymmetry measures, and fractal analysis. Results with indices of sympathovagal balance provided support to reinforce previous knowledge that patients with OSA have sympathetic overactivity. Nonlinear indices showed that HRV dynamics of patients with OSA display a loss of physiologic complexity that could contribute to their higher risk of development of cardiovascular disease. Moreover, many HRV indices were found to be linked with clinical scores of PSG. Therefore, a complete set of HRV indices, especially the ones obtained by the nonlinear approaches, can bring valuable information about the presence and severity of OSA, suggesting that HRV can be helpful for in a quick diagnosis of OSA, and supporting early interventions that could potentially reduce the development of comorbidities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2022.845327</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2022.845327</link>
        <title><![CDATA[Partial Directed Coherence and the Vector Autoregressive Modelling Myth and a Caveat]]></title>
        <pubdate>2022-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Luiz A. Baccalá</author><author>Koichi Sameshima</author>
        <description><![CDATA[Here we dispel the lingering myth that Partial Directed Coherence is a Vector Autoregressive (VAR) Modelling dependent concept. In fact, our examples show that it is spectral factorization that lies at its heart, for which VAR modelling is a mere, albeit very efficient and convenient, device. This applies to Granger Causality estimation procedures in general and also includes instantaneous Granger effects. Care, however, must be exercised for connectivity between multivariate data generated through nonminimum phase mechanisms as it may possibly be incorrectly captured.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2022.834056</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2022.834056</link>
        <title><![CDATA[Effects of Supplemental Oxygen on Cardiovascular and Respiratory Interactions by Extended Partial Directed Coherence in Idiopathic Pulmonary Fibrosis]]></title>
        <pubdate>2022-03-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Laura M. Santiago-Fuentes</author><author>Sonia Charleston-Villalobos</author><author>Ramón González-Camarena</author><author>Andreas Voss</author><author>Mayra E. Mejía-Avila</author><author>Ivette Buendía-Roldan</author><author>Sina Reulecke</author><author>Tomás Aljama-Corrales</author>
        <description><![CDATA[Idiopathic pulmonary fibrosis (IPF) is a chronic and restrictive disease characterized by fibrosis and inflammatory changes in lung tissue producing a reduction in diffusion capacity and leading to exertional chronic arterial hypoxemia and dyspnea. Furthermore, clinically, supplemental oxygen (SupplO2) has been prescribed to IPF patients to improve symptoms. However, the evidence about the benefits or disadvantages of oxygen supplementation is not conclusive. In addition, the impact of SupplO2 on the autonomic nervous system (ANS) regulation in respiratory diseases needs to be evaluated. In this study the interactions between cardiovascular and respiratory systems in IPF patients, during ambient air (AA) and SupplO2 breathing, are compared to those from a matched healthy group. Interactions were estimated by time series of successive beat-to-beat intervals (BBI), respiratory amplitude (RESP) at BBI onset, arterial systolic (SYS) and diastolic (DIA) blood pressures. The paper explores the Granger causality (GC) between systems in the frequency domain by the extended partial directed coherence (ePDC), considering instantaneous effects. Also, traditional linear and nonlinear markers as power in low (LF) and high frequency (HF) bands, symbolic dynamic indices as well as arterial baroreflex, were calculated. The results showed that for IPF during AA phase: 1) mean BBI and power of BBI-HF band, as well as mean respiratory frequency were significantly lower (p < 0.05) and higher (p < 0.001), respectively, indicating a strong sympathetic influence, and 2) the RESP → SYS interaction was characterized by Mayer waves and diminished RESP → BBI, i.e., decreased respiratory sinus arrhythmia. In contrast, during short-term SupplO2 phase: 1) oxygen might produce a negative influence on the systolic blood pressure variability, 2) the arterial baroreflex reduced significantly (p < 0.01) and 3) reduction of RSA reflected by RESP → BBI with simultaneous increase of Traube-Hering waves in RESP → SYS (p < 0.001), reflected increased sympathetic modulation to the vessels. The results gathered in this study may be helpful in the management of the administration of SupplO2.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2022.840829</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2022.840829</link>
        <title><![CDATA[Linear and Nonlinear Directed Connectivity Analysis of the Cardio-Respiratory System in Type 1 Diabetes]]></title>
        <pubdate>2022-03-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michele Sorelli</author><author>T. Noah Hutson</author><author>Leonidas Iasemidis</author><author>Leonardo Bocchi</author>
        <description><![CDATA[In this study, we explored the possibility of developing non-invasive biomarkers for patients with type 1 diabetes (T1D) by quantifying the directional couplings between the cardiac, vascular, and respiratory systems, treating them as interconnected nodes in a network configuration. Towards this goal, we employed a linear directional connectivity measure, the directed transfer function (DTF), estimated by a linear multivariate autoregressive modelling of ECG, respiratory and skin perfusion signals, and a nonlinear method, the dynamical Bayesian inference (DBI) analysis of bivariate phase interactions. The physiological data were recorded concurrently for a relatively short time period (5 min) from 10 healthy control subjects and 10 T1D patients. We found that, in both control and T1D subjects, breathing had greater influence on the heart and perfusion with respect to the opposite coupling direction and that, by both employed methods of analysis, the causal influence of breathing on the heart was significantly decreased (p < 0.05) in T1D patients compared to the control group. These preliminary results, although obtained from a limited number of subjects, provide a strong indication for the usefulness of a network-based multi-modal analysis for the development of biomarkers of T1D-related complications from short-duration data, as well as their potential in the exploration of the pathophysiological mechanisms that underlie this devastating and very widespread disease.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2021.765332</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2021.765332</link>
        <title><![CDATA[Measuring the Rate of Information Exchange in Point-Process Data With Application to Cardiovascular Variability]]></title>
        <pubdate>2022-01-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gorana Mijatovic</author><author>Riccardo Pernice</author><author>Alessio Perinelli</author><author>Yuri Antonacci</author><author>Alessandro Busacca</author><author>Michal Javorka</author><author>Leonardo Ricci</author><author>Luca Faes</author>
        <description><![CDATA[The amount of information exchanged per unit of time between two dynamic processes is an important concept for the analysis of complex systems. Theoretical formulations and data-efficient estimators have been recently introduced for this quantity, known as the mutual information rate (MIR), allowing its continuous-time computation for event-based data sets measured as realizations of coupled point processes. This work presents the implementation of MIR for point process applications in Network Physiology and cardiovascular variability, which typically feature short and noisy experimental time series. We assess the bias of MIR estimated for uncoupled point processes in the frame of surrogate data, and we compensate it by introducing a corrected MIR (cMIR) measure designed to return zero values when the two processes do not exchange information. The method is first tested extensively in synthetic point processes including a physiologically-based model of the heartbeat dynamics and the blood pressure propagation times, where we show the ability of cMIR to compensate the negative bias of MIR and return statistically significant values even for weakly coupled processes. The method is then assessed in real point-process data measured from healthy subjects during different physiological conditions, showing that cMIR between heartbeat and pressure propagation times increases significantly during postural stress, though not during mental stress. These results document that cMIR reflects physiological mechanisms of cardiovascular variability related to the joint neural autonomic modulation of heart rate and arterial compliance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2021.706487</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2021.706487</link>
        <title><![CDATA[Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG]]></title>
        <pubdate>2021-09-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Christos Koutlis</author><author>Vasilios K. Kimiskidis</author><author>Dimitris Kugiumtzis</author>
        <description><![CDATA[The usage of methods for the estimation of the true underlying connectivity among the observed variables of a system is increasing, especially in the domain of neuroscience. Granger causality and similar concepts are employed for the estimation of the brain network from electroencephalogram (EEG) data. Also source localization techniques, such as the standardized low resolution electromagnetic tomography (sLORETA), are widely used for obtaining more reliable data in the source space. In this work, connectivity structures are estimated in the sensor and in the source space making use of the sLORETA transformation for simulated and for EEG data with episodes of spontaneous epileptiform discharges (ED). From the comparative simulation study on high-dimensional coupled stochastic and deterministic systems originating in the sensor space, we conclude that the structure of the estimated causality networks differs in the sensor space and in the source space. Moreover, different network types, such as random, small-world and scale-free, can be better discriminated on the basis of the data in the original sensor space than on the transformed data in the source space. Similarly, in EEG epochs containing epileptiform discharges, the discriminative ability of network topological indices was significantly better in the sensor compared to the source level. In conclusion, causality networks constructed at the sensor and source level, for both simulated and empirical data, exhibit significant structural differences. These observations indicate that further studies are warranted in order to clarify the exact relationship between data registered in the sensor and source space.]]></description>
      </item>
      </channel>
    </rss>