Temperature effects on neuronal synchronization in seizures.

We present a computational model of networked neurons developed to study the effect of temperature on neuronal synchronization in the brain in association with seizures. The network consists of a set of chaotic bursting neurons surrounding a core tonic neuron in a square lattice with periodic boundary conditions. Each neuron is reciprocally coupled to its four nearest neighbors via temperature dependent gap junctions.

Recurrence microstates for machine learning classification.

Recurrence microstates are obtained from the cross recurrence of two sequences of values embedded in a time series, being the generalization of the concept of recurrence of a given state in phase space. The probability of occurrence of each microstate constitutes a recurrence quantifier. The set of probabilities of all microstates are capable of detecting even small changes in the data pattern.

Characterizing the spike timing of a chaotic laser by using ordinal analysis and machine learning.

Semiconductor lasers with optical feedback are well-known nonlinear dynamical systems. Under appropriate feedback conditions, these lasers emit optical pulses that resemble neural spikes. Influenced by feedback delay and various noise sources, including quantum spontaneous emission noise, the dynamics are highly stochastic.

Unravelling COVID-19 waves in Rio de Janeiro city: Qualitative insights from nonlinear dynamic analysis.

Since the COVID-19 pandemic was first reported in 2019, it has rapidly spread around the world. Many countries implemented several measures to try to control the virus spreading. The healthcare system and consequently the general quality of life population in the cities have all been significantly impacted by the Coronavirus pandemic.

Phase synchronization in a sparse network of randomly connected neurons under the effect of Poissonian spike inputs.

This article investigates the emergence of phase synchronization in a network of randomly connected neurons by chemical synapses. The study uses the classic Hodgkin-Huxley model to simulate the neuronal dynamics under the action of a train of Poissonian spikes. In such a scenario, we observed the emergence of irregular spikes for a specific range of conductances and also that the phase synchronization of the neurons is reached when the external current is strong enough to induce spiking activity but without overcoming the coupling current.

Neuropsychiatric symptoms in a patient under cystic fibrosis transmembrane conductance regulator modulators treatment: a case report.

In recent times, some research has focused on the study of potential treatments for cystic fibrosis (CF), such as cystic fibrosis transmembrane conductance regulator (CFTR) modulators. These treatments have been reported to produce neuropsychiatric symptoms in a few patients, even though there is still no clear correlation nor underlying mechanism proposed. We present the case of a 23-year-old woman with CF and no previous psychiatric history who was admitted to our inpatient psychiatric unit presenting a wide range of neuropsychiatric symptoms, such as disorganized speech, bizarre poses or persecutory delusional ideation, after going under CFTR modulators treatment.

Power-grid vulnerability and its relation with network structure.

Interconnected systems with critical infrastructures can be affected by small failures that may trigger a large-scale cascade of failures, such as blackouts in power grids. Vulnerability indices provide quantitative measures of a network resilience to component failures, assessing the break of information or energy flow in a system. Here, we focus on a network vulnerability analysis, that is, indices based solely on the network structure and its static characteristics, which are reliably available for most complex networks.

: A code for fitting multi-wave epidemic models.

The COVID-19 pandemic has given rise to a great demand for computational models capable of describing and inferring the evolution of an epidemic outbreak in the short term. In this sense, we introduce , a package that provides a framework for fitting multi-wave epidemic models to data from actual outbreaks of COVID-19 and other infectious diseases.

Basin sizes depend on stable eigenvalues in the Kuramoto model.

We show that for the Kuramoto model (with identical phase oscillators equally coupled), its global statistics and size of the basins of attraction can be estimated through the eigenvalues of all stable (frequency) synchronized states. This result is somehow unexpected since, by doing that, one could just use a local analysis to obtain the global dynamic properties. But recent works based on Koopman and Perron-Frobenius operators demonstrate that the global features of a nonlinear dynamical system, with some specific conditions, are somehow encoded in the local eigenvalues of its equilibrium states.

Characterisation of neonatal cardiac dynamics using ordinal partition network.

The maturation of the autonomic nervous system (ANS) starts in the gestation period and it is completed after birth in a variable time, reaching its peak in adulthood. However, the development of ANS maturation is not entirely understood in newborns. Clinically, the ANS condition is evaluated with monitoring of gestational age, Apgar score, heart rate, and by quantification of heart rate variability using linear methods.

Gompertz model in COVID-19 spreading simulation.

The paper reports on application of the Gompertz model to describe the growth dynamics of COVID-19 cases during the first wave of the pandemic in different countries. Modeling has been performed for 23 countries: Australia, Austria, Belgium, Brazil, Great Britain, Germany, Denmark, Ireland, Spain, Italy, Canada, China, the Netherlands, Norway, Serbia, Turkey, France, Czech Republic, Switzerland, South Korea, USA, Mexico, and Japan. The model parameters are determined by regression analysis based on official World Health Organization data available for these countries.

How heterogeneity in connections and cycles matter for synchronization of complex networks.

We analyze how the structure of complex networks of non-identical oscillators influences synchronization in the context of the Kuramoto model. The complex network metrics assortativity and clustering coefficient are used in order to generate network topologies of Erdös-Rényi, Watts-Strogatz, and Barabási-Albert types that present high, intermediate, and low values of these metrics. We also employ the total dissonance metric for neighborhood similarity, which generalizes to networks the standard concept of dissonance between two non-identical coupled oscillators.

Synchronization in populations of electrochemical bursting oscillators with chaotic slow dynamics.

We investigate the synchronization of coupled electrochemical bursting oscillators using the electrodissolution of iron in sulfuric acid. The dynamics of a single oscillator consisted of slow chaotic oscillations interrupted by a burst of fast spiking, generating a multiple time-scale dynamical system. A wavelet analysis first decomposed the time series data from each oscillator into a fast and a slow component, and the corresponding phases were also obtained.

Totally Implantable Venous-Access Device in Maintenance Electroconvulsive Therapy: A Retrospective Case Series.

Objectives: Electroconvulsive therapy (ECT) is an effective and safe treatment of certain severe mental disorders, but there are some barriers to the implementation of continuation/maintenance ECT courses in some cases. Repeated difficulties in achieving intravenous access before each session may contribute to premature ECT discontinuation. The placement of a totally implantable venous-access device (TIVAD) could be an alternative to overcome these difficulties in certain subjects.

Topological indexes and community structure for urban mobility networks: Variations in a business day.

Topological analysis and community detection in mobility complex networks have an essential role in many contexts, from economics to the environmental agenda. However, in many cases, the dynamic component of mobility data is not considered directly. In this paper, we study how topological indexes and community structure changes in a business day.

Spatiotemporal data analysis with chronological networks.

The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically.

Electroconvulsive therapy protocol adaptation during the COVID-19 pandemic.

Background: During the COVID-19 pandemic, electroconvulsive therapy units have had to confront challenges such as the infectious hazard due to aerosol-generating ventilation, or the lack of staff and material resources. Our objective was to elaborate a protocol to make ECT during the COVID-19 pandemic a safer procedure for patients and professionals.

Methods: A multidisciplinary workgroup (including mental health, anesthesia, preventive medicine, and occupational risk professionals) was formed in the Hospital Clínic de Barcelona, in March 2020.

Acute effects of a session of electroconvulsive therapy on brain-derived neurotrophic factor plasma levels.

Brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) are neurotrophins that play critical roles in brain neuronal function. Previous studies have established the association between BDNF and NGF signaling and severe mental disorders, but changes in BDNF plasma levels and electroconvulsive therapy (ECT) response are controversial. The aim of his study was to explore the acute effects of a single session of ECT on these neurotrophins signaling.

Extraction of slow and fast dynamics of multiple time scale systems using wavelet techniques.

A methodology is presented based on wavelet techniques to approximate fast and slow dynamics present in time-series whose behavior is characterized by different local scales in time. These approximations are useful to understand the global dynamics of the original full systems, especially in experimental situations where all information is contained in a one-dimensional time-series. Wavelet analysis is a natural approach to handle these approximations because each dynamical behavior manifests its specific subset in frequency domain, for example, with two time scales, the slow and fast dynamics, present in low and high frequencies, respectively.

Uncovering episodic influence of oceans on extreme drought events in Northeast Brazil by ordinal partition network approaches.

Since 2012, the semiarid region of Northeast Brazil (NEB) has been experiencing a continuous dry condition imposing significant social impacts and economic losses. Characterizing the recent extreme drought events and uncovering the influence from the surrounding oceans remain to be big challenges. The physical mechanisms of extreme drought events in the NEB are due to varying interacting time scales from the surrounding tropical oceans (Pacific and Atlantic).

Maximum entropy principle in recurrence plot analysis on stochastic and chaotic systems.

The recurrence analysis of dynamic systems has been studied since Poincaré's seminal work. Since then, several approaches have been developed to study recurrence properties in nonlinear dynamical systems. In this work, we study the recently developed entropy of recurrence microstates.

Recurrence quantification analysis with wavelet denoising and the characterization of magnetic flux emergence regions in solar photosphere.

Solar systems complexity, multiscale, and nonlinearity are governed by numerous and continuous changes where the sun magnetic fields can successfully represent many of these phenomena. Thus, nonlinear tools to study these challenging systems are required. The dynamic system recurrence approach has been successfully used to deal with this kind challenge in many scientific areas, objectively improving the recognition of state changes, randomness, and degrees of complexity that are not easily identified by traditional techniques.

Effects of electroconvulsive therapy in the systemic inflammatory balance of patients with severe mental disorder.

Aim: There is a great interest in the role of the immune system and the inflammatory balance as key mechanisms involved in the pathophysiology of severe mental disorders. Previous studies have indicated that electroconvulsive therapy (ECT) produces changes in certain inflammatory mediators or in the immune system response. This study aimed to explore the effects of ECT on the nuclear transcription factor κB (NFκB) pathway, a main regulatory pathway of the inflammatory/immune response.

Bistable Firing Pattern in a Neural Network Model.

Excessively high, neural synchronization has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neural synchronization mechanisms can thus help control or even treat epilepsy. In this paper, we study neural synchronization in a random network where nodes are neurons with excitatory and inhibitory synapses, and neural activity for each node is provided by the adaptive exponential integrate-and-fire model.

Search and return model for stochastic path integrators.

We extend a recently introduced prototypical stochastic model describing uniformly the search and return of objects looking for new food sources around a given home. The model describes the kinematic motion of the object with constant speed in two dimensions. The angular dynamics is driven by noise and describes a "pursuit" and "escape" behavior of the heading and the position vectors.