Search results for " network physiology"

showing 6 items of 6 documents

Plasticity of brain wave network interactions and evolution across physiologic states

2015

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions repre…

AdultMaleNerve netCognitive NeuroscienceNeuroscience (miscellaneous)Sensory systemPlasticityCognitive neurosciencelcsh:RC321-571Young AdultCellular and Molecular NeuroscienceNeuroplasticitymedicineHumanslcsh:Neurosciences. Biological psychiatry. NeuropsychiatryOriginal ResearchSlow-wave sleepCerebral CortexNetwork physiologySleep StagesNeuronal PlasticityBrain WaveBrain wave interactions; Network physiology; Neural plasticity; Sleep; Time delay stability; Adult; Brain Waves; Cerebral Cortex; Female; Humans; Male; Nerve Net; Neuronal Plasticity; Sleep; Young Adult; Neuroscience (miscellaneous); Cellular and Molecular Neuroscience; Sensory Systems; Cognitive NeuroscienceNetwork dynamicsBrain WavesSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Sensory Systemsbrain wave interactionsmedicine.anatomical_structureBrain wave interactionFemaletime delay stabilityNerve NetSensory SystemPsychologySleepNeuroscienceHumanNeuroscienceneural plasticityFrontiers in Neural Circuits
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Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Artificial neural networks; Chaotic oscillators; Granger causality; Multivariate time series analysis; Network physiology; Penalized regression techniques; Remote synchronization; State-space models; Stochastic gradient descent L1; Vector autoregressive modelGeneral Computer ScienceDynamical systems theoryComputer science02 engineering and technologyChaotic oscillatorsPenalized regression techniquesNetwork topologySettore ING-INF/01 - ElettronicaMultivariate time series analysisVector autoregression03 medical and health sciences0302 clinical medicineScientific Computing and Simulation0202 electrical engineering electronic engineering information engineeringRepresentation (mathematics)Optimization Theory and ComputationNetwork physiologyState-space modelsArtificial neural networkArtificial neural networksData ScienceTheory and Formal MethodsQA75.5-76.95Stochastic gradient descent L1Granger causality State-space models Vector autoregressive model Artificial neural networks Stochastic gradient descent L1 Multivariate time series analysis Network physiology Remote synchronization Chaotic oscillators Penalized regression techniquesRemote synchronizationStochastic gradient descentAutoregressive modelAlgorithms and Analysis of AlgorithmsVector autoregressive modelElectronic computers. Computer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causality020201 artificial intelligence & image processingGradient descentAlgorithm030217 neurology & neurosurgeryPeerJ Computer Science
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Information dynamics of brain-heart physiological networks during sleep

2014

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissec…

Conditional entropyPhysicsSleep StagesInformation transfermedicine.diagnostic_testGeneral Physics and AstronomyElectroencephalographynetwork physiologybrainheart interactions; information dynamics; network physiology; Physics and Astronomy (all)Physics and Astronomy (all)Settore ING-INF/06 - Bioingegneria Elettronica E Informaticamedicinebrainheart interactionHeart rate variabilityEntropy (information theory)Transfer entropyNeuroscienceinformation dynamicSlow-wave sleep
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Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

2019

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 &delta

Information transferInformation dynamicsComputer scienceStress assessmentGeneral Physics and Astronomylcsh:Astrophysics030204 cardiovascular system & hematologyNetwork topologynetwork physiologyInformation Theory Network Physiology StressArticlePhysics and Astronomy (all)03 medical and health sciences0302 clinical medicineRhythmwearable deviceslcsh:QB460-466stress assessmentlcsh:ScienceSubnetworkNetwork physiologyPulse (signal processing)Node (networking)Information dynamics; Network physiology; Stress assessment; Wearable deviceslcsh:QC1-999Wearable devicesPeripheralInformation dynamics; Network physiology; Stress assessment; Wearable devices; Physics and Astronomy (all)lcsh:QWakefulnessinformation dynamicsNeurosciencelcsh:Physics030217 neurology & neurosurgeryEntropy
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Quantifying High-Order Interactions in Cardiovascular and Cerebrovascular Networks

2022

We present a method to analyze the dynamics of physiological networks beyond the framework of pairwise interactions. Our method defines the so-called O-information rate (OIR) as a measure of the higher-order interaction among several physiological variables. The OIR measure is computed from the vector autoregressive representation of multiple time series, and is applied to the network formed by heart period, systolic and diastolic arterial pressure, respiration and cerebral blood flow variability series measured in healthy subjects at rest and after head-up tilt. Our results document that cardiovascular, cerebrovascular and respiratory interactions are highly redundant, and that redundancy …

Settore ING-INF/06 - Bioingegneria Elettronica e InformaticaInformation dynamics spectral analysis Granger causality time series analysis network physiology2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
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Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological N…

2020

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state&ndash

conditional transfer entropyInformation transferlinear predictionDynamical systems theoryComputer scienceState–space modelsGeneral Physics and Astronomylcsh:AstrophysicsNetwork topologycomputer.software_genrenetwork physiology01 natural sciencesArticle03 medical and health sciences0302 clinical medicinepenalized regression techniquelcsh:QB460-4660103 physical sciencesEntropy (information theory)Statistics::Methodologylcsh:Science010306 general physicspartial information decompositionmultivariate time series analysisinformation dynamics; partial information decomposition; entropy; conditional transfer entropy; network physiology; multivariate time series analysis; State–space models; vector autoregressive model; penalized regression techniques; linear predictionState–space modellcsh:QC1-999multivariate time series analysiInformation dynamicData pointpenalized regression techniquesAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelOrdinary least squaresvector autoregressive modellcsh:QData mininginformation dynamicsentropycomputerlcsh:Physics030217 neurology & neurosurgery
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