Biology subjects

Mateos, D. M.

Publications and source records attributed to Mateos, D. M..

18 recordsLinked to original sources

bioRxiv2026

Maturation of Sleep EEG Complexity in Preterm Newborns: Insights from Lempel-Ziv and Joint Lempel-Ziv Analyses

The development of the sleep-wake cycle reflects the progressive structural and functional maturation of the brain. However, the organization of neural dynamics during prematurity remains incompletely understood. In this study, we analyzed the EEG from 54 polysomnographic recordings obtained from 39 preterm infants, grouped according to postmenstrual age (PMA) into three categories: 30-31, 32-33 and 34-35 weeks. Lempel-Ziv Complexity (LZC) and Joint Lempel-Ziv Complexity (JLZC) of the electroencephalogram (EEG) were analyzed during active sleep (AS), quiet sleep (QS), and indeterminate sleep (IS). LZC computed from the raw, unfiltered recordings were significantly higher during QS than during AS and increased with PMA during AS. To further refine the analysis, LZC was also evaluated separately in the low-frequency (1-15.5 Hz) and high-frequency (16-30 Hz) EEG bands. In the low-frequency band, LZC was consistently higher during QS than during AS, an effect that was most pronounced in more immature groups. Furthermore, LZC increased with maturation particularly during AS. Sleep-state comparisons of LZC in the high-frequency EEG band also revealed higher values during QS than during AS across all PMA groups. Moreover, in contrast to the low-frequency band, LZC progressively decreased with advancing PMA both in AS and QS, suggesting that the neural mechanisms underlying low- and high-frequency EEG activity follow distinct maturational trajectories. Interestingly, larger LZC in the temporal cortex and interhemispheric differences were detected in the 32-33 PMA group. On the other hand, JLZC analysis revealed greater joint spatiotemporal dynamics across EEG channels during QS than during AS, with consistently higher JLZC values in temporal regions and lower in occipital regions. Together, these findings show that these complexity metrics distinguishes sleep states and captures maturational changes in EEG activity in preterm infants. These results provide novel insights into early brain development and suggest potential quantitative biomarkers of neonatal brain maturation.

neuroscience↗

bioRxiv2026

Partitioning away consciousness: an equal and cross-frequency connectivity analysis from the integration-segregation perspective

Derived from previous observations on equal and cross-frequency coupling, we evaluated the proposal that equal and cross-frequency phase synchronization may characterize the integration-segregation perspective of cerebral sensory-motor processing. Using brain recordings obtained in normal conditions and in conditions of diminished sensory input (eyes closed wakefulness, sleep and coma, when there is presumably less functional segregation of sensory-motor processing in neural networks), we assessed potential differences in partitioning of the synchrony state space linked to cross-frequency synchronization. More partitions were found in conditions of decreased sensory input. In addition, there was a less complex synchrony state space in cross-frequency as compared with equal-frequency coupling, in terms of fewer connectivity configurations. These results support the idea that equal-frequency coupling favours integration from multiple brain regions occurring in a complex synchrony state space rich in possible connectivity configurations, whereas cross-frequency coupling contributes to segregation, or localized sensory-motor transformations taking place in specific brain areas. This evidence may contribute to new considerations about the much-discussed role of multi-frequency relations in neuronal activity, and how the structural and functional modular organization of the nervous system is able to generate the coordinated activity needed for consciousness and appropriate cognitive behaviors in complex environments.

neuroscience↗

bioRxiv2026

Analysis of Long-Term Neuronal Dynamics via Ordinal Pattern Quantifiers Following Traumatic Brain Injury and Pharmacological Modulation

Traumatic brain injury (TBI) profoundly disrupts hippocampal network dynamics, triggering persistent alterations in oscillatory activity that underlie cognitive deficits and increased susceptibility to post-traumatic epilepsy. Characterizing these alterations quantitatively remains challenging: the resulting signals are nonlinear, non-stationary, and exhibit complex multiscale structure that conventional spectral metrics fail to resolve. Ordinal-pattern information-theoretic quantifiers offer a principled, model-free alternative for probing such dynamics. In this work we apply permutation entropy (PE), statistical complexity (SC), Fisher information (FI), and permutation Lempel-Ziv complexity (PLZC) to hippocampal local field potentials (LFPs) recorded over 21 days in a rodent controlled cortical impact model of TBI, across five experimental groups under distinct pharmacological conditions. Embedding signal trajectories in the SC-PE, FI-PE, and PLZC-PE information planes reveals group- and time-dependent dynamical signatures in the theta (4-8 Hz) and high-frequency oscillation (80-200 Hz) bands, exposing state transitions invisible to spectral analysis. Unsupervised dimensionality reduction (UMAP) combined with HDBSCAN clustering further delineates distinct regions of dynamical state space associated with injury progression and pharmacological modulation. We additionally applied the Ordinal Modulation Index (OMI), an ordinal-based measure of theta-HFO cross-frequency coupling, which captures treatment-dependent reorganization of phase-amplitude coordination. These results establish ordinal-pattern analysis as a sensitive and interpretable framework for tracking the nonlinear reorganization of hippocampal dynamics following TBI.

neuroscience↗

bioRxiv2026

Modafinil Modulation of Oscillatory and Aperiodic Brain Activity in Rats Across Wakefulness and Subsequent Sleep

Cortical states shape sensory processing, decision-making, and behavior, and are characterized by distinct patterns of neuronal oscillatory activity. Pharmacological agents can modulate these states and their underlying electrophysiological signatures. Modafinil is a widely used wake-promoting agent and cognitive enhancer, but its effects on cortical oscillations are not fully understood. In this study, we evaluated the effect of modafinil on cortical activity during wakefulness and subsequent sleep. Ten male Wistar rats were implanted with electrocorticographic electrodes and recorded following oral administration of modafinil or its vehicle. Our results show that modafinil increased wakefulness in a dose-dependent manner, with doses of 200 and 300 mg/kg producing prolonged wake episodes and reducing hypnogram complexity. During wakefulness, modafinil enhanced gamma-band power in anterior cortices, an effect primarily driven by increases in the aperiodic rather than oscillatory components. Although no sleep rebound was observed, subsequent NREM sleep exhibited increased delta power, particularly in anterior regions, indicating heightened sleep pressure. In conclusion, our findings demonstrate that modafinil-induced wakefulness is associated with an enhanced aperiodic gamma band component at the anterior cortices in the EEG. Furthermore, modafinil modulates sleep homeostasis by enhancing NREM sleep intensity rather than extending its duration.

neuroscience↗

bioRxiv2025

Study of changes in brain dynamics during sleep cycles in dogs under effect of trazodone

Sleep is a fundamental biological process essential for cognitive function, memory consolidation, and overall health in both humans and animals. Dogs, in particular, share many physiological and neurological similarities with humans, making them a valuable model for sleep research. Similar to humans, dogs can experience sleep disorders that disrupt sleep cycles and impair cognitive function. While serotonin antagonist and reuptake inhibitors (SARIs), like trazodone, have been shown to alleviate these conditions in dogs, their underlying neural mechanisms remain poorly understood. This study investigates the effects of trazodone on brain dynamics in healthy dogs using electroencephalographic (EEG) analysis. We compared treated subjects with a control group by characterizing EEG activity across wakefulness, drowsiness, Non-REM (NREM), and REM sleep states. Hypnogram analysis was used to assess sleep architecture, including alterations in cycle patterns and time spent in each stage. Additionally, we examined linear and non-linear EEG dynamics using Power Spectral Density (PSD), Permutation Entropy (PE), and Lempel-Ziv complexity (LZC), as well as connectivity changes through Phase Lag Index (PLI) and coherence analysis. Our findings indicate that trazodone significantly alters sleep structure by modifying sleep cycles, reducing power in lower frequency bands across most sleep stages, and increasing power in frequencies above 13 Hz during Drowsiness and NREM. Furthermore, treated dogs exhibited increased signal entropy and complexity in lower frequency bands across all sleep stages, along with a reduction in brain connectivity in most stages and frequency bands. These results provide new insights into the short-term effects of trazodone on brain activity during sleep, with potential implications for its clinical use as a sleep aid in dogs.

animal behavior and cognition↗

bioRxiv2024

Higher-Order Interaction Analysis via Hypergraph Models for Studying Multidimensional Neuroscience Data.

Higher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data.

neuroscience↗

bioRxiv2024

A novel perspective on equal and cross-frequency neural coupling: integration and segregation of the brain networks' function

We introduce a novel perspective in equal and multifrequency coupling derived from considering neuronal synchrony as a possible equivalence relation. The experimental results agree with the theoretical prediction that cross-frequency coupling results in a partition of the brain synchrony state space. We place these results in the framework of the integration and segregation of information in the processing of sensorimotor transformations by the brain cell circuits and propose that equal frequency (1:1) connectivity favours integration of information in the brain whereas cross-frequency coupling (n:m) favours segregation. These observations may provide an outlook about how to reconcile the need for stability in the brains operations with the requirement for diversity of activity in order to process many sensorimotor transformations simultaneously.

neuroscience↗

bioRxiv2024

Traumatic brain injury enhances the intrinsic excitation and excitatory transmission of granule cells

Traumatic brain injury (TBI) leads to a wide range of long-lasting physical and cognitive impairments. Changes in neuronal excitability and synaptic functions in the hippocampus have been proposed to underlie cognitive alterations. The dentate gyrus (DG) acts as a "gatekeeper" of hippocampal information processing and as a filter of excessive or aberrant input activity. In this study, we investigated the effects of controlled cortical impact, a model of TBI, on the excitability of granule cells (GCs) and excitatory postsynaptic transmission in the DG at three time points, 3 days, 15 days and 4 months after the injury. Our results indicate that changes in the short term are related to intrinsic properties, while changes in the long term are more related to input and synaptic activity, in agreement with the notion that TBI-related pathology courses with an acute phase and a later long-term secondary phase. A biphasic response, a reduction in the shorter term and an increase in the long term, was found in TBI neurons in the frequency of sEPSC. These changes correlated with a loss of complexity in the pattern of the synaptic input, an alteration that could therefore play a role in the chronic and recurrent TBI-asssociated hyperexcitation.

neuroscience↗

bioRxiv2023

Cortical high-frequency oscillations ({approx} 110 Hz) in cats are state-dependent and enhanced by a subanesthetic dose of ketamine

Ketamine is an NMDA receptor antagonist that has both antidepressant and anesthetic properties. At subanesthetic doses, ketamine can cause transient psychosis in humans, and is used to model psychosis in experimental animals. In rodents, subanesthetic doses of ketamine increase the power of high-frequency oscillations (HFO, 100-180 Hz) in the electroencephalogram and field potentials, a frequency band linked to cognitive functions. However, the effects of ketamine in higher mammals, with more translatable relevance, are poorly investigated. Here, we have examined cortical HFO during wakefulness, sleep, and after administering a sub-anesthetic dose of ketamine (15 mg/kg), utilizing the cat as an animal model. Four cats were implanted with cortical electrodes for chronic polysomnographic recordings. HFO power, connectivity, information flow directionality, and their relationships with respiratory activity were analyzed. During wakefulness, but not during sleep, we found that HFO were coupled with the inspiratory phase of the respiration. After ketamine administration, HFO were enhanced significantly and remained associated with the inspiratory phase. The analysis of the information flow after ketamine suggest that HFO originate from the olfactory bulb and stream towards the prefrontal cortex. Accordingly, occluding the nostrils significantly reduced HFO power in both the olfactory bulb and prefrontal cortex. In contrast, auditory stimulation did not affect HFO. In conclusion, spontaneous cortical HFO show certain state-dependent features in cats, and enhancement of this rhythm by ketamine may disrupt cortical information processing, which could contribute to some of the neuropsychiatric manifestations associated with ketamine. HighlightsO_LIKetamine is used to model psychosis in humans and experimental animals C_LIO_LISubanesthetic doses of ketamine increase the power of high-frequency oscillations C_LIO_LIHigh-frequency oscillations are coupled with the inspiratory phase of respiration C_LIO_LIThese oscillations originate in the olfactory bulb and stream to the neocortex C_LIO_LINostril occlusion lowers high-frequency activity in the olfactory bulb and neocortex C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/543142v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@e386feorg.highwire.dtl.DTLVardef@1b3c06aorg.highwire.dtl.DTLVardef@1d93ecorg.highwire.dtl.DTLVardef@1daa161_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Dorsal and median raphe neuronal firing dynamics characterized by non-linear metrics

The dorsal (DRN) and median (MRN) raphe are the main serotonergic nuclei, being implicated in sleep and mood regulation. The DRN is mainly serotonergic, where neurons have regular spiking activity, slow firing rate (FR), and long action potential duration (APD). The MRN is divided in a median serotonergic region and a paramedian region, containing principally GABAergic neurons, resulting in more diverse neurochemical and electrophysiological features. In the present study, we aimed to enrich the characterization of the raphe nuclei neurons by using non-linear metrics. This was done by analyzing the neuronal basal firing profile in both nuclei of urethane-anesthetized rats using Ordinal Patterns (OP) Entropy, Bins Entropy, and Permutation Lempel-Ziv Complexity (PLZC). In a first step, we found that typical linear metrics - such as FR, coefficient of variation (CV), and APD - fail to distinguish between MRN and DRN neurons, while OP entropy is significantly different between these nuclei. We also found that the FR has a strong linear relationship with CV, Bins Entropy, and PLZC. Similarly, CV has a strong correlation with FR and Bins Entropy, whereas PLZC shows a strong linear fit with Bins Entropy. However, OP Entropy has either a weak or no linear relationship with the rest of the metrics tested, suggesting that OP Entropy is a good metric to differentiate neuronal firing profiles. In a second step, we studied how these metrics are affected by the oscillatory properties of the firing patterns. We found that all metrics are sensitive to rhythmicity - with the exception of OP Entropy. Again, this highlights OP Entropy as a powerful and useful quantity for the characterization of neuronal discharge patterns.

neuroscience↗

bioRxiv2022

A Transfer Entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective

Studying brain dynamics under normal or pathological conditions has proven to be a challenging task, as there is no unified consensus on the best approach. In this article, we present a methodology based on Transfer Entropy to study the information flow between different brain hemispheres in healthy subjects during eyes-open (EO) and eyes-closed (EC) resting states. We used an experimental setup that mimics the technical conditions found in clinical settings and collected data sets from short records of 24 channels electroencephalogram (EEG) at a sampling rate of 65 Hz. Our methodology accounts for interhemispheric and intrahemispheric information flow analysis in both conditions and relies on 4 indexes calculated from the transfer entropy estimations between EEG channels. These indexes provide information on the number, strength, and directionality of active connections. Our results suggest an increase in information transfer in the EC condition for the alpha, beta1, and beta2 frequency bands, but no preferred direction of interhemispheric information movement under either condition. These results are consistent with previously reported studies conducted with denser EEG recordings sampled at a higher rate. In conclusion, our methodology shows a significant difference in the brains dynamics of information transfer between EO and EC resting states, which can also be applied to regular clinical sessions.

neuroscience↗

bioRxiv2021

Low Frequency Oscillations drive EEG's complexity changes during wakefulness and sleep

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 EEGs 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 EEGs low frequencies are critical in shaping the sleep-wake states complexity across cortical scales.

neuroscience↗

bioRxiv2021

Urethane Anesthesia Exhibits Neurophysiological Correlates of Unconsciousness and is Distinct from Sleep

Urethane is a general anesthetic widely used in animal research. It is unique among anesthetics because urethane anesthesia alternates between macroscopically distinct electrographic states: a slow-wave state that resembles NREM sleep (NREMure), and an activated state with features of both REM sleep and wakefulness (REMure). However, the relationship between urethane anesthesia and physiological sleep is still unclear. In this study, electroencephalography (EEG) and electromyography were recorded in chronically prepared rats during natural sleep-wake states and during urethane anesthesia. We subsequently analyzed the EEG signatures associated with the loss of consciousness and found that, in comparison to natural sleep-wake states, the power, coherence, directed connectivity and complexity of brain oscillations are distinct during urethane. We also demonstrate that both urethane states have clear EEG signatures of general anesthesia. Thus, despite superficial similarities that have led others to conclude that urethane is a model of sleep, the electrocortical traits of depressed and activated states during urethane anesthesia differ from physiological sleep states.

neuroscience↗

bioRxiv2021

Complexity of brain dynamics as a correlate of consciousness in anaesthetized monkeys

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.

neuroscience↗

bioRxiv2021

Consciousness is supported by near-critical cortical electrodynamics

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition, and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, for the first time, we empirically identify the specific critical point near which conscious cortical dynamics operate as the edge-of-chaos critical point, or the boundary between periodicity/stability and chaos/instability. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magne-toencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, GABAergic anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of cortical dynamics away from this critical point; conversely, we show that psychedelics may increase the information-richness of cortical activity by tuning cortical electrodynamics closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC), and show that assessing the proximity of cortical electrodynamics to the edge-of-chaos critical point may be clinically useful as a new biomarker of consciousness. Significance StatementWhat changes in the brain when we lose consciousness? One possibility is that the loss of consciousness corresponds to a transition of the brains electric activity away from edge-of-chaos criticality, or the knifes edge in between stability and chaos. Recent mathematical developments have produced novel tools for testing this hypothesis, which we apply for the first time to cortical recordings from diverse brain states. We show that the electric activity of the cortex is indeed poised near the boundary between stability and chaos during conscious states and transitions away from this boundary during unconsciousness, and that this transition disrupts cortical information processing.

neuroscience↗

bioRxiv2019

On a simple general principle of brain organization.

A possible framework to characterise nervous system dynamics and its organization in conscious and unconscious states is introduced, derived from a high level perspective on the coordinated activity of brain cell ensembles. Some questions are best addressable in a global framework and here we build on past observations about the structure of configurations of brain networks in conscious and unconscious states and about neurophysiological results. Aiming to bind some results together into some sort of coherence with a central theme, the scenario that emerges underscores the crucial importance of the creation and dissipation of energy gradients in brain cellular ensembles resulting in maximisation of the configurations in the functional connectivity among those networks that favour conscious awareness and healthy conditions. These considerations are then applied to indicate approaches that can be used to improve neuropathological syndromes.

neuroscience↗

bioRxiv2019

Using time causal quantifiers to characterize Sleep stages

Sleep plays substantial role in daily cognitive performance, mood and memory. The study of sleep has attracted the interest of neuroscientists, clinicans and the overall population, with increasing number of adults suffering from insufficient amounts of sleep. Sleep is an activity composed of different stages whose temporal dynamics, cycles and inter dependencies are not fully understood. Healthy body function and personal well being, however, depends on proper unfolding and continuance of the sleep cycles. The characterization of the different sleep stages can be undertaken with the development of biomarkers derived from sleep recording. For this purpose, in this work we analyzed single-channel EEG signals from 106 healthy subjects. The signals were quantified using the permutation vector approach using five different information theoretic measures: i) Shannons entropy, ii) MPR statistical complexity, iii) Fisher information, iv) Renyi Min-entropy and v) Lempel-Ziv complexity. The results show that all five information theory-based measures make possible to quantify and classify the underlying dynamics of the different sleep stages. In addition to this, we combine these measures to show that planes containing pairs of measures, such as the plane composed of Lempel-Ziv and Shannon, have a better performance for differentiating sleep states than measures used individually for the same purpose.

neuroscience↗

bioRxiv2017

Complexity-Entropy maps as a tool for the characterization of the clinical electrophysiological evolution of patients under pharmacological treatment with psychotropic drugs

In the clinical electrophisiologic practice, the reading and comparing electroencephalographic (EEG) recordings some times is insufficient and take to much time. That is why in the last years it has begun to introduce new methods of EEG analysis, that give a better and faster understanding of the EEG dynamics and allow a rapid intervention in the patients treatment. Tools coming from the information theory or nonlinear system as an entropy and complexity have been shown to be a very good alternative to address this problem. In this work we introduce a novel method -the permutation Lempel-ziv complexity vs permutation entropy map. This method was applied to EEG of two patients with specific diagnosed pathologies during respective follow up processes of pharmacological changes in order to detect changes that are not evident with the usual inspection method. Our results show that the proposed method are useful for observing an evolutionary retrospective clinical effects of pharmacological interventions in both patients, and from these, to follow the clinical response to the proposed treatment.

neuroscience↗