131,159 research outputs found
Twitter matrix comm of users - Diaz-Faes et al
No full textDataset: Users activity on Twitter around science from 2011 to 2017 in a selection of social media metrics. Also files for VOSviewer visualisations are provided: random sample of 200,000 Twitter profile accounts descriptions.Díaz-Faes, A.A., Bowman, T. & Costas, R. Towards a second generation of ‘altmetrics’: Characterizing the interactions of Twitter communities of attention around science. PLOS ONE.</div
Memoria de prácticas en la empresa Faes Farma, S. A
No full textEn esta memoria se relatan las actividades realizadas durante las prácticas en el Departamento de I+D+i de la empresa farmacéutica Faes Farma, S. A.Francés Monerris, A. (2011). Memoria de prácticas en la empresa Faes Farma, S. A. Universitat Politècnica de València. https://riunet.upv.es/handle/10251/15616Archivo delegad
On 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 On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures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 textLinear and non-linear brain-heart and brain-brain interactions during sleep
No full textIn this study, the physiological networks underlying the joint modulation of the parasympathetic component of heart rate variability (HRV) and of the different electroencephalographic (EEG) rhythms during sleep were assessed using two popular measures of directed interaction in multivariate time series, namely Granger causality (GC) and transfer entropy (TE). Time series representative of cardiac and brain activities were obtained in 10 young healthy subjects as the normalized high frequency (HF) component of HRV and EEG power in the delta, theta, alpha, sigma, and beta bands, measured during the whole duration of sleep. The magnitude and statistical significance of GC and TE were evaluated between each pair of series, conditional on the remaining series, using respectively a linear model-based approach exploiting regression models, and a nonlinear model-free approach combining nearestneighbor entropy estimation with a procedure for dimensionality reduction. The contribution of nonlinear dynamics to the TE was also assessed using surrogate data. GC and TE consistently detected structured networks of physiological interactions, with links directed predominantly from HRV to the EEG waves in the brain-heart network, and from the sigma and beta EEG waves to the delta,theta, and alpha waves in the brain-brain network. While these common patterns supported the suitability of a linear model-based analysis, we also found a significant contribution of nonlinear dynamics, particularly involving the information transferred out of the delta node in the two networks. This suggested the importance of nonparametric TE estimation for evidencing the fine structure of the physiological networks underlying the autonomic regulation of cardiac and brain functions during sleep
On the interpretability and computational reliability of frequency-domain Granger causality [version 1; referees: 2 approved]
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
Partial Information Decomposition in the Frequency Domain: Application to Control Mechanisms of Heart Rate Variability at Rest and during Postural Stress
No full textWe exploit a recently proposed framework for assessing causal influences in the frequency domain to construct the partial information decomposition (PID) for informational circuits of three variables, thus obtaining the spectral decomposition of redundancy, synergy and unique information. The approach is applied to heart period (HP), systolic pressure (SP) and respiration (RESP) variability series measured in healthy subjects in baseline and head up tilt conditions. Integrating the informational quantities in the respiratory band, the total influence from RESP to HP does not change in the two conditions. However, we find that in baseline RESP causes HP mostly through the direct pathway describing central autonomic effects, whilst in head up tilt condition the direct influence decreases and becomes comparable to the information pathway mediated by SP describing baroreflex effects. Our results show the usefulness of the spectral decomposition of PID
Inclusion of instantaneous influences in the spectral decomposition of causality: Application to the control mechanisms of heart rate variability
No full textHeart rate variability is the result of several physiological regulation mechanisms, including cardiovascular and cardiorespiratory interactions. Since instantaneous influences occurring within the same cardiac beat are commonplace in this regulation, their inclusion is mandatory to get a realistic model of physiological causal interactions. Here we exploit a recently proposed framework for the spectral decomposition of causal influences between autoregressive processes [2] and generalize it by introducing instantaneous couplings in the vector autoregressive model (VAR). We show the effectiveness of the proposed approach on a toy model, and on real data consisting of heart period (RR), systolic pressure (SAP) and respiration (RESP) variability series measured in healthy subjects in baseline and head up tilt conditions. In particular, we show that our framework allows one to highlight patterns of frequency domain causality that are consistent with well-interpretable physiological interaction mechanisms like the weakening of respiratory sinus arrhythmia at high frequencies and the activation of the baroreflex control at lower frequencies, in response to postural stress
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