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Special Issue "Information Dynamics in Brain and Physiological Networks"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (30 April 2019)

Special Issue Editors

Guest Editor
Prof. Dr. Luca Faes

Department of Engineering, University of Palermo, 90128 Palermo, Italy
Website | E-Mail
Interests: time series analysis; cardiovascular neuroscience; network physiology
Guest Editor
Prof. Dr. Alberto Porta

Department of Biomedical Sciences for Health, University of Milan, Milan, Italy and Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
E-Mail
Interests: time series analysis; cardiovascular control; complexity
Guest Editor
Prof. Dr. Sebastiano Stramaglia

Dipartimento Interateneo di Fisica, Università di Bari, and INFN Sezione di Bari. 70126 Bari, Italy
Website | E-Mail
Interests: time series analysis; network neuroscience; network physiology

Special Issue Information

Dear Colleagues,

It is, nowadays, widely acknowledged that the brain and several other organ systems, including the cardiovascular, respiratory, and muscular systems, among others, exhibit complex dynamic behaviors that result from the combined effects of multiple regulatory mechanisms, coupling effects and feedback interactions, acting in both space and time.

The field of information theory is becoming more and more relevant for the theoretical description and quantitative assessment of the dynamics of the brain and physiological networks, defining concepts, such as those of information generation, storage, transfer, and modification. These concepts are quantified by several information measures (e.g., approximate entropy, conditional entropy, multiscale entropy, transfer entropy, redundancy and synergy, and many others), which are being increasingly used to investigate how physiological dynamics arise from the activity and connectivity of different structural units, and evolve across a variety of physiological states and pathological conditions.

This Special Issue focuses on blending theoretical developments in the new emerging field of information dynamics with innovative applications targeted to the analysis of complex brain and physiological networks in health and disease. To favor this multidisciplinary view, contributions are welcome from different fields, ranging from mathematics and physics to biomedical engineering, neuroscience, and physiology.

Prof. Dr. Luca Faes
Prof. Dr. Alberto Porta
Prof. Dr. Sebastiano Stramaglia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Dynamical complexity,
  • Multivariate time series analysis,
  • Information storage,
  • Transfer entropy,
  • Redundancy and synergy,
  • Network physiology,
  • Brain connectivity,
  • Cardiovascular oscillations,
  • Neuroscience

 

Published Papers (10 papers)

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Research

Open AccessArticle
Information-Domain Analysis of Cardiovascular Complexity: Night and Day Modulations of Entropy and the Effects of Hypertension
Entropy 2019, 21(6), 550; https://doi.org/10.3390/e21060550
Received: 30 April 2019 / Revised: 27 May 2019 / Accepted: 28 May 2019 / Published: 31 May 2019
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Abstract
Multiscale entropy (MSE) provides information-domain measures of the systems’ complexity. The increasing interest in MSE of the cardiovascular system lies in the possibility of detecting interactions with other regulatory systems, as higher neural networks. However, most of the MSE studies considered the heart-rate [...] Read more.
Multiscale entropy (MSE) provides information-domain measures of the systems’ complexity. The increasing interest in MSE of the cardiovascular system lies in the possibility of detecting interactions with other regulatory systems, as higher neural networks. However, most of the MSE studies considered the heart-rate (HR) series only and a limited number of scales: actually, an integrated approach investigating HR and blood-pressure (BP) entropies and cross-entropy over the range of scales of traditional spectral analyses is missing. Therefore, we aim to highlight influences of higher brain centers and of the autonomic control on multiscale entropy and cross-entropy of HR and BP over a broad range of scales, by comparing different behavioral states over 24 h and by evaluating the influence of hypertension, which reduces the autonomic control of BP. From 24-h BP recordings in eight normotensive and eight hypertensive participants, we selected subperiods during daytime activities and nighttime sleep. In each subperiod, we derived a series of 16,384 consecutive beats for systolic BP (SBP), diastolic BP (DBP), and pulse interval (PI). We applied a modified MSE method to obtain robust estimates up to time scales of 334 s, covering the traditional frequency bands of spectral analysis, for three embedding dimensions and compared groups (rank-sum test) and conditions (signed-rank test) at each scale. Results demonstrated night-and-day differences at scales associable with modulations in vagal activity, in respiratory mechanics, and in local vascular regulation, and reduced SBP-PI cross-entropy in hypertension, possibly representing a loss of complexity due to an impaired baroreflex sensitivity. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress
Entropy 2019, 21(5), 526; https://doi.org/10.3390/e21050526
Received: 23 April 2019 / Revised: 19 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
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Abstract
Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different [...] Read more.
Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer T RESP , SBP RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration ( U SBP RR ), nonbaroreflex ( U RESP RR ) and baroreflex-mediated ( R RESP , SBP RR ) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences ( S RESP , SBP RR ), respectively. We find that fast (short time scale) HRV oscillations—respiratory sinus arrhythmia—originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer’s Disease
Entropy 2019, 21(5), 475; https://doi.org/10.3390/e21050475
Received: 14 March 2019 / Revised: 28 April 2019 / Accepted: 28 April 2019 / Published: 6 May 2019
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Abstract
In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer’s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network [...] Read more.
In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer’s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Time-Frequency Analysis of Cardiovascular and Cardiorespiratory Interactions During Orthostatic Stress by Extended Partial Directed Coherence
Entropy 2019, 21(5), 468; https://doi.org/10.3390/e21050468
Received: 5 February 2019 / Revised: 24 April 2019 / Accepted: 28 April 2019 / Published: 5 May 2019
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Abstract
In this study, the linear method of extended partial directed coherence (ePDC) was applied to establish the temporal dynamic behavior of cardiovascular and cardiorespiratory interactions during orthostatic stress at a 70° head-up tilt (HUT) test on young age-matched healthy subjects and patients with [...] Read more.
In this study, the linear method of extended partial directed coherence (ePDC) was applied to establish the temporal dynamic behavior of cardiovascular and cardiorespiratory interactions during orthostatic stress at a 70° head-up tilt (HUT) test on young age-matched healthy subjects and patients with orthostatic intolerance (OI), both male and female. Twenty 5-min windows were used to analyze the minute-wise progression of interactions from 5 min in a supine position (baseline, BL) until 18 min of the orthostatic phase (OP) without including pre-syncopal phases. Gender differences in controls were present in cardiorespiratory interactions during OP without compromised autonomic regulation. However in patients, analysis by ePDC revealed considerable dynamic alterations within cardiovascular and cardiorespiratory interactions over the temporal course during the HUT test. Considering the young female patients with OI, the information flow from heart rate to systolic blood pressure (mechanical modulation) was already increased before the tilt-up, the information flow from systolic blood pressure to heart rate (neural baroreflex) increased during OP, while the information flow from respiration to heart rate (respiratory sinus arrhythmia) decreased during the complete HUT test. Findings revealed impaired cardiovascular interactions in patients with orthostatic intolerance and confirmed the usefulness of ePDC for causality analysis. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
The Understanding Capacity and Information Dynamics in the Human Brain
Entropy 2019, 21(3), 308; https://doi.org/10.3390/e21030308
Received: 23 December 2018 / Revised: 8 March 2019 / Accepted: 15 March 2019 / Published: 21 March 2019
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Abstract
This article proposes a theory of neuronal processes underlying cognition, focusing on the mechanisms of understanding in the human brain. Understanding is a product of mental modeling. The paper argues that mental modeling is a form of information production inside the neuronal system [...] Read more.
This article proposes a theory of neuronal processes underlying cognition, focusing on the mechanisms of understanding in the human brain. Understanding is a product of mental modeling. The paper argues that mental modeling is a form of information production inside the neuronal system extending the reach of human cognition “beyond the information given” (Bruner, J.S., Beyond the Information Given, 1973). Mental modeling enables forms of learning and prediction (learning with understanding and prediction via explanation) that are unique to humans, allowing robust performance under unfamiliar conditions having no precedents in the past history. The proposed theory centers on the notions of self-organization and emergent properties of collective behavior in the neuronal substrate. The theory motivates new approaches in the design of intelligent artifacts (machine understanding) that are complementary to those underlying the technology of machine learning. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress
Entropy 2019, 21(3), 275; https://doi.org/10.3390/e21030275
Received: 30 January 2019 / Revised: 26 February 2019 / Accepted: 9 March 2019 / Published: 13 March 2019
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Abstract
In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical [...] Read more.
In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical activity of seven physiological systems was probed with one-second time resolution measuring the time series of the δ , θ , α and β brain wave amplitudes, the cardiac period (RR interval), the respiratory amplitude, and the duration of blood pressure wave propagation (pulse arrival time, PAT). Synchronous 5-min windows of these time series, obtained from 18 subjects during resting wakefulness (REST), mental stress induced by mental arithmetic (MA) and sustained attention induced by serious game (SG), were taken to describe the dynamics of the nodes composing the observed physiological network. Network activity and connectivity were then assessed in the framework of information dynamics computing the new information generated by each node, the information dynamically stored in it, and the information transferred to it from the other network nodes. Moreover, the network topology was investigated using directed measures of conditional information transfer and assessing their statistical significance. We found that all network nodes dynamically produce and store significant amounts of information, with the new information being prevalent in the brain systems and the information storage being prevalent in the peripheral systems. The transition from REST to MA was associated with an increase of the new information produced by the respiratory signal time series (RESP), and that from MA to SG with a decrease of the new information produced by PAT. Each network node received a significant amount of information from the other nodes, with the highest amount transferred to RR and the lowest transferred to δ , θ , α and β . The topology of the physiological network underlying such information transfer was node- and state-dependent, with the peripheral subnetwork showing interactions from RR to PAT and between RESP and RR, PAT consistently across states, the brain subnetwork resulting more connected during MA, and the subnetwork of brain–peripheral interactions involving different brain rhythms in the three states and resulting primarily activated during MA. These results have both physiological relevance as regards the interpretation of central and autonomic effects on cardiovascular and respiratory variability, and practical relevance as regards the identification of features useful for the automatic distinction of different mental states. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Macroscopic Cluster Organizations Change the Complexity of Neural Activity
Entropy 2019, 21(2), 214; https://doi.org/10.3390/e21020214
Received: 14 December 2018 / Revised: 11 February 2019 / Accepted: 19 February 2019 / Published: 23 February 2019
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Abstract
In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of [...] Read more.
In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of spiking neurons with different topological properties of a macroscopic network based on the Watts and Strogatz model. The complexity of spontaneous activity is analyzed using multiscale entropy, and the structural properties of the network are analyzed using complex network theory. Experimental results show that a macroscopic structure with high clustering and high degree centrality increases the firing rates of neurons in a neuron group and enhances intraconnections from the excitatory neurons to inhibitory neurons in a neuron group. As a result, the intensity of the specific frequency components of neural activity increases. This decreases the complexity of neural activity. Finally, we discuss the research relevance of the complexity of the brain activity. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Entropic Approach to the Detection of Crucial Events
Entropy 2019, 21(2), 178; https://doi.org/10.3390/e21020178
Received: 22 December 2018 / Revised: 29 January 2019 / Accepted: 12 February 2019 / Published: 14 February 2019
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Abstract
In this paper, we establish a clear distinction between two processes yielding anomalous diffusion and 1/f noise. The first process is called Stationary Fractional Brownian Motion (SFBM) and is characterized by the use of stationary correlation functions. The second process rests [...] Read more.
In this paper, we establish a clear distinction between two processes yielding anomalous diffusion and 1 / f noise. The first process is called Stationary Fractional Brownian Motion (SFBM) and is characterized by the use of stationary correlation functions. The second process rests on the action of crucial events generating ergodicity breakdown and aging effects. We refer to the latter as Aging Fractional Brownian Motion (AFBM). To settle the confusion between these different forms of Fractional Brownian Motion (FBM) we use an entropic approach properly updated to incorporate the recent advances of biology and psychology sciences on cognition. We show that although the joint action of crucial and non-crucial events may have the effect of making the crucial events virtually invisible, the entropic approach allows us to detect their action. The results of this paper lead us to the conclusion that the communication between the heart and the brain is accomplished by AFBM processes. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Paced Breathing Increases the Redundancy of Cardiorespiratory Control in Healthy Individuals and Chronic Heart Failure Patients
Entropy 2018, 20(12), 949; https://doi.org/10.3390/e20120949
Received: 18 October 2018 / Revised: 4 December 2018 / Accepted: 6 December 2018 / Published: 10 December 2018
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Abstract
Synergy and redundancy are concepts that suggest, respectively, adaptability and fault tolerance of systems with complex behavior. This study computes redundancy/synergy in bivariate systems formed by a target X and a driver Y according to the predictive information decomposition approach and partial information [...] Read more.
Synergy and redundancy are concepts that suggest, respectively, adaptability and fault tolerance of systems with complex behavior. This study computes redundancy/synergy in bivariate systems formed by a target X and a driver Y according to the predictive information decomposition approach and partial information decomposition framework based on the minimal mutual information principle. The two approaches assess the redundancy/synergy of past of X and Y in reducing the uncertainty of the current state of X. The methods were applied to evaluate the interactions between heart and respiration in healthy young subjects (n = 19) during controlled breathing at 10, 15 and 20 breaths/minute and in two groups of chronic heart failure patients during paced respiration at 6 (n = 9) and 15 (n = 20) breaths/minutes from spontaneous beat-to-beat fluctuations of heart period and respiratory signal. Both methods suggested that slowing respiratory rate below the spontaneous frequency increases redundancy of cardiorespiratory control in both healthy and pathological groups, thus possibly improving fault tolerance of the cardiorespiratory control. The two methods provide markers complementary to respiratory sinus arrhythmia and the strength of the linear coupling between heart period variability and respiration in describing the physiology of the cardiorespiratory reflex suitable to be exploited in various pathophysiological settings. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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Open AccessArticle
Interaction Information Along Lifespan of the Resting Brain Dynamics Reveals a Major Redundant Role of the Default Mode Network
Entropy 2018, 20(10), 742; https://doi.org/10.3390/e20100742
Received: 1 August 2018 / Revised: 7 September 2018 / Accepted: 24 September 2018 / Published: 28 September 2018
Cited by 2 | PDF Full-text (2633 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Interaction Information (II) generalizes the univariate Shannon entropy to triplets of variables, allowing the detection of redundant (R) or synergetic (S) interactions in dynamical networks. Here, we calculated II from functional magnetic resonance imaging data and asked whether R or S vary across [...] Read more.
Interaction Information (II) generalizes the univariate Shannon entropy to triplets of variables, allowing the detection of redundant (R) or synergetic (S) interactions in dynamical networks. Here, we calculated II from functional magnetic resonance imaging data and asked whether R or S vary across brain regions and along lifespan. Preserved along lifespan, we found high overlapping between the pattern of high R and the default mode network, whereas high values of S were overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, we have found a robust balance between R and S among different age intervals, indicating informational compensatory mechanisms in brain networks. Full article
(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)
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