1,721,161 research outputs found
Information transfer an information modification to identify the structure of cardiovascular and cardiorespiratory networks
No full text
Information theory for network physiology: multivariate and multiscale methods to dissect the information content of brain, cardiovascular and muscolar networks
No full textOn the interpretability and computational reliability of frequency-domain Granger causality
No full textThis Correspondence article is a comment which directly relates to the paper "A study of problems encountered in Granger causality analysis from a neuroscience perspective" (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name "causality", as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved
Synergetic and redundant information flow in dynamical systems: an operative definition based on prediction
No full textLetter by Masè et al Regarding Article, "Granger Causality-Based Analysis for Classification of Fibrillation Mechanisms and Localization of Rotational Drivers"
No full textTesting different methodologies for Granger causality estimation: A simulation study
No full textGranger causality (GC) is a method for determining whether and how two time series exert causal influences one over the other. As it is easy to implement through vector autoregressive (VAR) models and can be generalized to the multivariate case, GC has spread in many different areas of research such as neuroscience and network physiology. In its basic formulation, the computation of GC involves two different regressions, taking respectively into account the whole past history of the investigated multivariate time series (full model) and the past of all time series except the putatively causal time series (restricted model). However, the restricted model cannot be represented through a finite order VAR process and, when few data samples are available or the number of time series is very high, the estimation of GC exhibits a strong reduction in accuracy. To mitigate these problems, improved estimation strategies have been recently implemented, including state space (SS) models and partial conditioning (PC) approaches. In this work, we propose a new method to compute GC which combines SS and PC and tests it together with other four commonly used estimation approaches. In simulated networks of linearly interacting time series, we show the possibility to reconstruct the network structure even in challenging conditions of data samples available
A Model-Free Method to Quantify Memory Utilization in Neural Point Processes
Full text linkObjective: Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively utilize it for dynamic system evolution, is a key component of neural information processing. Information storage (IS), the main information-theoretic measure quantifying the active utilization of memory in a dynamic system, is only defined for discrete-time processes, and although recent theoretical work laid the foundations for its continuous-time analysis, a reliable computation method is still needed for broader application to neural data. Methods: This work introduces a method for the model-free estimation of the so-called memory utilization rate (MUR), the continuous-time counterpart of the IS, specifically designed to quantify the predictive capacity stored in neural point processes. Moreover, a surrogate data-based procedure is used to correct estimation bias and detect significant memory levels in the analyzed point process. Results: The method is first validated in simulations of Poisson processes, both memoryless and with memory, as well as in realistic models of coupled cortical dynamics and heartbeat dynamics. It is then applied to real spike trains reflecting central and autonomic nervous system activities: in spontaneously growing cortical neuron cultures, the MUR detected increasing levels of memory utilization across maturation stages, linked to the emergence of synchronized bursting; in heartbeat modulation analysis, the MUR reflected sympathetic activation and vagal withdrawal occurring with postural stress, but not with mental stress. Conclusion and Significance: The proposed approach offers a novel, computationally reliable tool for the analysis of spike train data in computational neuroscience and physiology
Advanced computation in cardiovascular physiology: New challenges and opportunities
Full text linkRecent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes' may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a specific focus on cardiovascular control physiology and pathology. This includes the development and adaptation of complex signal processing methods, multivariate cardiovascular models, multiscale and nonlinear models for central-peripheral dynamics, as well as deep and transfer learning algorithms applied to large datasets. The width of this perspective highlights the issues of specificity in heartbeat-related features and supports the need for an imminent transition from the black-box paradigm to explainable and personalized clinical models in cardiovascular research. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'
- …
