1,721,098 research outputs found
Evolutionary dynamics of group formation
No full textGroup formation is a quite ubiquitous phenomenon across different animal species, whose individuals cluster together forming communities of diverse size. Previous investigations suggest that, in general, this phenomenon might have similar underlying reasons across the interested species, despite genetic and behavioral differences. For instance improving the individual safety (e.g. from predators), and increasing the probability to get food resources. Remarkably, the group size might strongly vary from species to species, e.g. shoals of fishes and herds of lions, and sometimes even within the same species, e.g. tribes and families in human societies. Here we build on previous theories stating that the dynamics of group formation may have evolutionary roots, and we explore this fascinating hypothesis from a purely theoretical perspective, with a model using the framework of Evolutionary Game Theory. In our model we hypothesize that homogeneity constitutes a fundamental ingredient in these dynamics. Accordingly, we study a population that tries to form homogeneous groups, i.e. composed of similar agents. The formation of a group can be interpreted as a strategy. Notably, agents can form a group (receiving a ‘group payoff’), or can act individually (receiving an ‘individual payoff’). The phase diagram of the modeled population shows a sharp transition between the ‘group phase’ and the ‘individual phase’, characterized by a critical ‘individual payoff’. Our results then support the hypothesis that the phenomenon of group formation has evolutionary roots
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 On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures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
The measure of randomness by leave-one-out prediction error in the analysis of EEG after laser painful stimulation in healthy subjects and migraine patients
No full textObjective: We aimed to perform a quantitative analysis of event-related modulation of EEG activity, resulting from a not-warned and a
warned paradigm of painful laser stimulation, in migraine patients and controls, by the use of a novel analysis, based upon a parametric
approach to measure predictability of short and noisy time series.
Methods: Ten migraine patients were evaluated during the not-symptomatic phase and compared to seven age and sex matched controls. The
dorsum of the right hand and the right supraorbital zone were stimulated by a painful CO2 laser, in presence or in absence of a visual warning
stimulus. An analysis time of 1 s after the stimulus was submitted to a time–frequency analysis by a complex Morlet wavelet and to a crosscorrelation
analysis, in order to detect the development of EEG changes and the most activated cortical regions. A parametric approach to
measure predictability of short and noisy time series was applied, where time series were modeled by leave-one-out (LOO) error.
Results: The averaged laser-evoked potentials features were similar between the two groups in the alerted and not alerted condition. A strong
reset of the beta rhythms after the painful stimuli was seen for three groups of electrodes along the midline in patients and controls: the
predictability of the series induced by the laser stimulus changed very differently in controls and patients. The separation was more evident
after the warning signal, leading to a separation with P-values of 0.0046 for both the hand and the face.
Discussion: As painful stimulus causes organization of the local activity in cortex, EEG series become more predictable after stimulation.
This phenomenon was less evident in migraine, as a sign of an inadequate cortical reactivity to pain.
Significance: The LOO method enabled to show in migraine subtle changes in the cortical response to pain
Linear 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
Synergy and redundancy in the Granger causal analysis of dynamical networks
No full textWe analyze, by means of Granger causality (GC), the effect of synergy and
redundancy in the inference (from time series data) of the information flow
between subsystems of a complex network. While we show that fully conditioned
GC (CGC) is not affected by synergy, the pairwise analysis fails to prove
synergetic effects. In cases when the number of samples is low, thus making the
fully conditioned approach unfeasible, we show that partially conditioned GC
(PCGC) is an effective approach if the set of conditioning variables is properly
chosen. Here we consider two different strategies (based either on informational
content for the candidate driver or on selecting the variables with highest pairwise
influences) for PCGC and show that, depending on the data structure, either
one or the other might be equally valid. On the other hand, we observe that fully
conditioned approaches do not work well in the presence of redundancy, thus
suggesting the strategy of separating the pairwise links in two subsets: those
corresponding to indirect connections of the CGC (which should thus be
excluded) and links that can be ascribed to redundancy effects and, together with
the results from the fully connected approach, provide a better description of the
causality pattern in the presence of redundancy. Finally we apply these methodsto two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure
occurrences. Second, our analysis applied to gene expression time series from
HeLa culture shows that the underlying regulatory networks are characterized by
both redundancy and synergy
Decomposition of the transfer entropy: Partial conditioning and informative clustering
No full textWe propose a formal expansion of the transfer entropy to address the problem or partial conditioning evaluating information flow in multivariate datasets. This approach will then be adapted to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by an high value will be associated to informational circuits present in the system, with an informational character (synergetic or redundant) which can be associated to the sign of the contribution. These methods are then applied to the analysis of fMRI data
Synergetic and redundant information flow in dynamical systems: an operative definition based on prediction
No full textBeyond pairwise network similarity: exploring mediation and suppression between networks
Full text linkNetwork similarity measures quantify how and when two networks are symmetrically related, including measures of statistical association such as pairwise distance or other correlation measures between networks or between the layers of a multiplex network, but neither can directly unveil whether there are hidden confounding network factors nor can they estimate when such correlation is underpinned by a causal relation. In this work we extend this pairwise conceptual framework to triplets of networks and quantify how and when a network is related to a second network (of the same number of nodes) directly or via the indirect mediation or interaction with a third network. Accordingly, we develop a simple and intuitive set-theoretic approach to quantify mediation and suppression between networks. We validate our theory with synthetic models and further apply it to triplets (multiplex) of real-world networks, unveiling mediation and suppression effects which emerge when considering different modes of interaction in online social networks and different routes of information processing in the nervous system
Kernel Method for Nonlinear Granger Causality
Full text linkImportant information on the structure of complex systems can be obtained by measuring to what extent
the individual components exchange information among each other. The linear Granger approach, to
detect cause-effect relationships between time series, has emerged in recent years as a leading statistical
technique to accomplish this task. Here we generalize Granger causality to the nonlinear case using the
theory of reproducing kernel Hilbert spaces. Our method performs linear Granger causality in the feature
space of suitable kernel functions, assuming arbitrary degree of nonlinearity.We develop a new strategy to
cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces.
Applications to coupled chaotic maps and physiological data sets are presented
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