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The dorsal (DRN) and median (MRN) raphe are important nuclei involved in similar functions, including mood and sleep, but playing distinct roles. These nuclei have a different composition of neuronal types and set of neuronal connections, which among other factors, determine their neuronal dynamics. Most works characterize the neuronal dynamics using classic measures, such as using the average spiking frequency (FR), the coefficient of variation (CV), and action potential duration (APD). In the current study, to refine the characterization of neuronal firing profiles, we examined the neurons within the raphe nuclei. Through the utilization of nonlinear measures, our objective was to discern the redundancy and complementarity of these measures, particularly in comparison with classic methods. To do this, we analyzed the neuronal basal firing profile in both nuclei of urethane-anesthetized rats using the Shannon entropy (Bins Entropy) of the inter-spike intervals, permutation entropy of ordinal patterns (OP Entropy), and Permutation Lempel-Ziv Complexity (PLZC). Firstly, we found that classic (i.e., FR, CV, and APD) and nonlinear measures fail to distinguish between the dynamics of DRN and MRN neurons, except for the OP Entropy. We also found strong relationships between measures, including the CV with FR, CV with Bins entropy, and FR with PLZC, which imply redundant information. However, APD and OP Entropy have either a weak or no relationship with the rest of the measures tested, suggesting that they provide complementary information to the characterization of the neuronal firing profiles. Secondly, we studied how these measures are affected by the oscillatory properties of the firing patterns, including rhythmicity, bursting patterns, and clock-like behavior. We found that all measures are sensitive to rhythmicity, except for the OP Entropy. Overall, our work highlights OP Entropy as a powerful and useful quantity for the characterization of neuronal discharge patterns.

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Divergences or similarity measures between probability distributions have become a very useful tool for studying different aspects of statistical objects, such as time series, networks, and images. Notably, not every divergence provides identical results when applied to the same problem. Therefore, it seems convenient to have the widest possible set of divergences to be applied to the problems under study. Besides this choice, an essential step in the analysis of every statistical object is the mapping of each one of their representing values into an alphabet of symbols conveniently chosen. In this work, we choose the family of divergences known as the Burbea-Rao centroids (BRCs). For the mapping of the original time series into a symbolic sequence, we work with the ordinal pattern scheme. We apply our proposals to analyze simulated and real time series and to real textured images. The main conclusion of our work is that the best BRC, at least in the studied cases, is the Jensen-Shannon divergence, besides the fact that it verifies some interesting formal properties.

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Urethane is a general anaesthetic widely used in animal research. The state of urethane anaesthesia is unique because it alternates between macroscopically distinct electrographic states: a slow-wave state that resembles non-rapid eye movement (NREM) sleep and an activated state with features of both REM sleep and wakefulness. Although it is assumed that urethane produces unconsciousness, this has been questioned because of states of cortical activation during drug exposure. Furthermore, the similarities and differences between urethane anaesthesia and physiological sleep are still unclear. In this study, we recorded the electroencephalogram (EEG) and electromyogram in chronically prepared rats during natural sleep-wake states and during urethane anaesthesia. We subsequently analysed the power, coherence, directed connectivity and complexity of brain oscillations and found that EEG under urethane anaesthesia has clear signatures of unconsciousness, with similarities to other general anaesthetics. In addition, the EEG profile under urethane is different in comparison with natural sleep states. These results suggest that consciousness is disrupted during urethane. Furthermore, despite similarities that have led others to conclude that urethane is a model of sleep, the electrocortical traits of depressed and activated states during urethane anaesthesia differ from physiological sleep states.

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The use of anaesthesia is a fundamental tool in the investigation of consciousness. Anesthesia procedures allow to investigate different states of consciousness from sedation to deep anesthesia within controlled scenarios. In this study we use information quantifiers to measure the complexity of electrocorticogram recordings in monkeys. We apply these metrics to compare different stages of general anesthesia for evaluating consciousness in several anesthesia protocols. We find that the complexity of brain activity can be used as a correlate of consciousness. For two of the anaesthetics used, propofol and medetomidine, we find that the anaesthetised state is accompanied by a reduction in the complexity of brain activity. On the other hand we observe that use of ketamine produces an increase in complexity measurements. We relate this observation with increase activity within certain brain regions associated with the ketamine used doses. Our measurements indicate that complexity of brain activity is a good indicator for a general evaluation of different levels of consciousness awareness, both in anesthetized and non anesthetizes states.

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Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.

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The d2, test of attention is one of the most used neuropsychological tests to measure attention in clinical and research settings. To date, no studies have examined neither its convergent and divergent validity in children nor its relationship with academic skills at school age. The aims of the present study were: (1) to examine the convergent and divergent validity of the d2 Test in a non-clinical pediatric population, (2) to explore the relationship between d2 task performance and academic skills (i.e., math, reading and writing abilities) and (3) to develop normative data for Spanish-speaking children (n = 360 8- to 12-year-old children) stratified by age and socioeconomic status (SES). Pearson's correlation and Structural Equation Models (SEM) were used to analyze the d2 Test validity and its relationship with academic skills. A between-subjects factorial MANOVA was used to examine differences among SES (Middle, Low), age (8-10, 11-12), and sex (male, female). Findings revealed a significant relationship between d2 task performance and all attention and executive functions (EF) measures under analysis providing evidence of good convergent validity. Furthermore, SEM results showed that attention has direct effects on math and reading and writing skills. Finally, our study confirms the influence of age and SES on d2 task performance and provides normative data for middle- and low-SES children. These results have important implications for the assessment of attention functions in clinical and research settings in children with typical and atypical development.

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The transfer entropy and the transfer entropy rate are closely related concepts that measure information exchange between two dynamical systems. These measures allow us to study linear and nonlinear causality relations and can be estimated through the use of different methodologies. However, some of them assume a data model and/or are computationally expensive. This article depicts a methodology to estimate the transfer entropy rate between two systems through the Lempel-Ziv complexity. This methodology offers a set of advantages: It estimates the transfer entropy rate from two single discrete series of measures, it is not computationally expensive, and it does not assume any data model. The simulation results over three different unidirectional coupled dynamical systems suggest that this methodology can be used to assess the direction and strength of the information flow between systems. Moreover, it provides good estimations for short-length time series.