Electrophysiological damage to neuronal membrane alters ephaptic entrainment.

The brain is commonly understood as a complex network system with a particular organization and topology that can result in specific electrophysiological patterns. Among all the dynamic elements resulting from the circuits of the brain's network, ephapticity is a cellular communication mechanism that has received little attention. To understand the network's properties of ephaptic entrainment, we start investigating the ephaptic effect on a single neuron. In this study, we used numerical simulations to examine the relationship between alterations in ephaptic neuronal entrainment and impaired electrophysiological properties of the neuronal membrane, which can occur via spike field coherence (SFC). This change in frequency band amplitude is observed in some neurodegenerative diseases, such as Parkinson's or Alzheimer's. To further investigate these phenomena, we proposed a damaged model based on the impairment of both the resistance of the ion channels and the capacitance of the lipid membrane. Therefore, we simulated ephaptic entrainment with the hybrid neural model quadratic integrate-and-fire ephaptic (QIF-E), which mimics an ephaptic entrainment generated by an LFP (simulate a neuronal group). Our results indicate a link between peak entrainment (ephapticity) preference and a shift in frequency band when damage occurs mainly in ion channels. Finally, we discuss possible relationships between ephaptic entrainment and neurodegenerative diseases associated with aging factors.

Analysis of stress tolerance, competitive-anxiety, heart rate variability and salivary cortisol during successive matches in male futsal players.

BACKGROUND: This study aimed to compare the stress tolerance, competitive anxiety, heart rate variability and salivary cortisol before and during successive futsal competitive matches (3 matches in 4 days) in young male futsal players. METHODS: 10 young male futsal players (16.9 ± 0.7 age; 71.0 ± 5.1 kg; 174.9 ± 4.3 cm) were monitored during one training session and across a competitive period with 3 successive matches. External load was determined by the PlayerLoad method, while session rating of perceived exertion was used to calculate the internal training and competitive load. The stress tolerance was examined using Daily Analysis of Life Demand in Athletes questionnaire and the Competitive State Anxiety Inventory was used to analyze the competitive anxiety. The Time and frequency monitoring parameters were used to analyze the vagal cardiac autonomic marker. sC was analyzed using enzyme-linked immunosorbent assay. RESULTS: A generalized estimating equation showed a significant difference for PlayerLoad from M1 to TS, M2 and M3, from M2 to M3 (p < 0.05), and for session rating of perceived exertion from M1 to Ts and M3 (p < 0.05). A difference for sources [χ2 (3) = 1.481, p = 0.68] or symptoms [χ2 (3) = 3.893, p = 0.27] was not found. There was no significant difference in any of the competitive anxiety [cognitive anxiety (F (1.644; 14.799) = 4.6, p = 0.73, ŋ2 p = 0.28), somatic anxiety (F (2,09; 18,85) = 26.07 p = 0.057; ŋ2p = 0.27) or self-confidence (F(2.07; 18.85) = 15.875 p = 0.152; ŋ2p = 0.18)] domains. The HRV parameters (time domain and frequency) and Salivary Cortisol (sC) (χ2 (3) = 4.320 p = 0.229) did not significantly change during the successive matches. CONCLUSION: The competitive scenario in which the players were evaluated did not significantly modify the stress tolerance, or the athletes' state of anxiety, which in turn was not able to promote changes in the cardiac vagal modulation or in the sC levels before the matches.

Ephaptic entrainment in hybrid neuronal model.

In recent decades, there has been a growing interest in the impact of electric fields generated in the brain. Transmembrane ionic currents originate electric fields in the extracellular space and are capable of affecting nearby neurons, a phenomenon called ephaptic neuronal communication. In the present work, the Quadratic Integrated-and-Fire model (QIF-E) underwent an adjustment/improvement to include the ephaptic entrainment behavior between neurons and electric fields. Indeed, the aim of our study is to validate the QIF-E model, which is a model to estimate the influence of electric fields on neurons. For this purpose, we evaluated whether the main properties observed in an experiment by Anastassiou et al. (Nat Neurosci 14:217-223, 2011), which analyzed the effect of an electric field on cortical pyramidal neurons, are reproduced with the QIF-E model. In this way, the analysis tools are employed according to the neuronal activity regime: (i) for the subthreshold regime, the circular statistic is used to describe the phase differences between the input stimulus signal (electrode) and the modeled membrane response; (ii) in the suprathreshold regime, the Population Vector and the Spike Field Coherence are used to estimate phase preferences and the entrainment intensity between the input stimulus and Action Potentials. The results observed are (i) in the subthreshold regime the values of the phase differences change with distinct frequencies of the input stimulus; (ii) in the supra-threshold regime the preferential phase of Action Potentials changes for different frequencies. In addition, we explore other parameters of the model, such as noise and membrane characteristic-time, in order to understand different types of neurons and extracellular environment related to ephaptic communication. Such results are consistent with results observed in empirical experiments based on ephaptic phenomenon. In addition, the QIF-E model allows further studies on the physiological importance of ephaptic communication in the brain, and its simplicity may open a door to simulate the ephaptic response in neuronal networks and assess the impact of ephaptic communication in such scenarios.

Scale-free distribution of silences.

Soundscape studies help us understand ecological processes, biodiversity distribution, anthropic influences, and even urban quality, across a wide variety of places and time periods. In this work, instead of looking for differences, we ask if there are common characteristics shared by all soundscapes. Based on our results, we propose a universal distribution of quiet-time (background noise) and sound-time (acoustic energy bursts) in audio recordings. We analyzed one continuous hour during daylight and one at night, from ten randomly selected days in each environment: urban, dry forest, savanna, rupestrian field, Atlantic forest, marine, and freshwater. We found that the histograms of the quiet-time followed a power law for all scenarios analyzed, they present fractal events or scale-free distributions. This distribution covers up to four orders of magnitude, with an exponent of 1.6≤α≤2.0 for all soundscapes. By contrast, the sound-time distribution in all environments followed a log-normal or timescale dependence, with a typical time for the duration of sounds (0.06-0.12 s). Such time duration limitation can be related to the physiology of sound emission in animals.

Disruption of neocortical synchronisation during slow-wave sleep in the rotenone model of Parkinson's disease.

Parkinson's disease motor dysfunctions are associated with improperly organised neural oscillatory activity. The presence of such disruption at the early stages of the disease in which altered sleep is one of the main features could be a relevant predictive feature. Based on this, we aimed to investigate the neocortical synchronisation dynamics during slow-wave sleep (SWS) in the rotenone model of Parkinson's disease. After rotenone administration within the substantia nigra pars compacta, one group of male Wistar rats underwent sleep-wake recording. Considering the association between SWS oscillatory activity and memory consolidation, another group of rats underwent a memory test. The fine temporal structure of synchronisation dynamics was evaluated by a recently developed technique called first return map. We observed that rotenone administration decreased the time spent in SWS and altered the power spectrum within different frequency bands, whilst it increased the transition rate from a synchronised to desynchronised state. This neurotoxin also increased the probability of longer and decreased the probability of shorter desynchronisation events. At the same time, we observed impairment in object recognition memory. These findings depict an electrophysiological fingerprint represented by a disruption in the typical oscillatory activity within the neocortex at the early stages of Parkinson's disease, concomitant with a decrease in the time spent in SWS and impairment in recognition memory.

Waiting-time statistics in magnetic systems.

Many complex systems, from earthquakes and financial markets to Barkhausen effect in ferromagnetic materials, respond with a noise consisting of discrete avalanche-like events with broad range of sizes and durations, separated by waiting times. Here we focus on the waiting-time statistics in magnetic systems. By investigating the Barkhausen noise in amorphous and polycrystalline ferromagnetic films having different thicknesses, we uncover the form of the waiting-time distribution in time series recorded from the irregular and irreversible motion of magnetic domain walls. Further, we address the question of if the waiting-time distribution evolves with the threshold level, as well as with the film thickness and structural character of the materials. Our results, besides informing on the temporal avalanche correlations, disclose the waiting-time statistics in magnetic systems also bring fingerprints of the universality classes of Barkhausen avalanches and a dimensional crossover in the domain wall dynamics.

Hippocampal and cortical communication around micro-arousals in slow-wave sleep.

Sleep plays a crucial role in the regulation of body homeostasis and rhythmicity in mammals. Recently, a specific component of the sleep structure has been proposed as part of its homeostatic mechanism, named micro-arousal. Here, we studied the unique progression of the dynamic behavior of cortical and hippocampal local field potentials (LFPs) during slow-wave sleep-related to motor-bursts (micro-arousals) in mice. Our main results comprised: (i) an abrupt drop in hippocampal LFP amplitude preceding micro-arousals which persisted until the end of motor-bursts (we defined as t interval, around 4s) and a similar, but delayed amplitude reduction in cortical (S1/M1) LFP activity occurring at micro-arousal onset; (ii) two abrupt frequency jumps in hippocampal LFP activity: from Theta (6-12 Hz) to Delta (2-4 Hz), also t seconds before the micro-arousal onset, and followed by another frequency jump from Delta to Theta range (5-7 Hz), now occurring at micro-arousal onset; (iii) a pattern of cortico-hippocampal frequency communication precedes micro-arousals: the analysis between hippocampal and cortical LFP fluctuations reveal high coherence during τ interval in a broader frequency band (2-12 Hz), while at a lower frequency band (0.5-2 Hz) the coherence reaches its maximum after the onset of micro-arousals. In conclusion, these novel findings indicate that oscillatory dynamics pattern of cortical and hippocampal LFPs preceding micro-arousals could be part of the regulatory processes in sleep architecture.

Optimizing the detection of nonstationary signals by using recurrence analysis.

Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology.