Interactions between cardiac, respiratory and EEG-delta oscillations in rats during anaesthesia

Potentiation of the depressant effect of alcohol by flunitrazepam in rats: an electrocorticographic, respiratory and electrocardiographic study

Alcohol, a widely commercialized psychotropic drug, and the benzodiazepine Flunitrazepam, an anxiolytic widely prescribed for patients with anxiety and insomnia problems, are well known drugs and both act on the central nervous system. The misuse and the association of these two drugs are public health concerns in several countries and could cause momentary, long-lasting and even lethal neurophysiological problems due to the potentiation of their adverse effects in synergy. The present study observed the result of the association of these drugs on electrophysiological responses in the brain, heart, and respiratory rate in Wistar rats. 8 experimental groups were determined: control, one alcohol group (20% at a dose of 1 ml/100 g VO), three Flunitrazepam groups (doses 0.1; 0.2 and 0.3 mg/kg) and three alcohol-Flunitrazepam groups (20% at a dose of 1 ml/100 g VO of alcohol, combined with 0.1; 0.2 and 0.3 mg/kg of Flunitrazepam, respectively). The results showed that there was a more pronounced reduction in alpha and theta wave power in the alcohol-Flunitrazepam groups, a decrease in the power of beta oscillations and greater sedation. There was a progressive decrease in respiratory rate linked to the increase of Flunitrazepam dose in the alcohol-Flunitrazepam associated administration. It was observed alteration in heart rate and Q-T interval in high doses of Flunitrazepam. Therefore, we conclude that the association alcohol-Flunitrazepam presented deepening of depressant synergistic effects according to the increase in the dose of the benzodiazepine, and this could cause alterations in low frequency brain oscillations, breathing, and hemodynamics of the patient.

The phase coherence of the neurovascular unit is reduced in Huntington's disease

Huntington's disease is a neurodegenerative disorder in which neuronal death leads to chorea and cognitive decline. Individuals with ≥40 cytosine-adenine-guanine repeats on the interesting transcript 15 gene develop Huntington's disease due to a mutated huntingtin protein. While the associated structural and molecular changes are well characterized, the alterations in neurovascular function that lead to the symptoms are not yet fully understood. Recently, the neurovascular unit has gained attention as a key player in neurodegenerative diseases. The mutant huntingtin protein is known to be present in the major parts of the neurovascular unit in individuals with Huntington's disease. However, a non-invasive assessment of neurovascular unit function in Huntington's disease has not yet been performed. Here, we investigate neurovascular interactions in presymptomatic (N = 13) and symptomatic (N = 15) Huntington's disease participants compared to healthy controls (N = 36). To assess the dynamics of oxygen transport to the brain, functional near-infrared spectroscopy, ECG and respiration effort were recorded. Simultaneously, neuronal activity was assessed using EEG. The resultant time series were analysed using methods for discerning time-resolved multiscale dynamics, such as wavelet transform power and wavelet phase coherence. Neurovascular phase coherence in the interval around 0.1 Hz is significantly reduced in both Huntington's disease groups. The presymptomatic Huntington's disease group has a lower power of oxygenation oscillations compared to controls. The spatial coherence of the oxygenation oscillations is lower in the symptomatic Huntington's disease group compared to the controls. The EEG phase coherence, especially in the α band, is reduced in both Huntington's disease groups and, to a significantly greater extent, in the symptomatic group. Our results show a reduced efficiency of the neurovascular unit in Huntington's disease both in the presymptomatic and symptomatic stages of the disease. The vasculature is already significantly impaired in the presymptomatic stage of the disease, resulting in reduced cerebral blood flow control. The results indicate vascular remodelling, which is most likely a compensatory mechanism. In contrast, the declines in α and γ coherence indicate a gradual deterioration of neuronal activity. The results raise the question of whether functional changes in the vasculature precede the functional changes in neuronal activity, which requires further investigation. The observation of altered dynamics paves the way for a simple method to monitor the progression of Huntington's disease non-invasively and evaluate the efficacy of treatments.

Synchronization through frequency shuffling

A wide variety of engineered and natural systems are modeled as networks of coupled nonlinear oscillators. In nature, the intrinsic frequencies of these oscillators are not constant in time. Here, we probe the effect of such a temporal heterogeneity on coupled oscillator networks through the lens of the Kuramoto model. To do this, we shuffle repeatedly the intrinsic frequencies among the oscillators at either random or regular time intervals. What emerges is the remarkable effect that frequent shuffling induces earlier onset (i.e., at a lower coupling) of synchrony among the oscillator phases. Our study provides a novel strategy to induce and control synchrony under resource constraints. We demonstrate our results analytically and in experiments with a network of Wien Bridge oscillators with internal frequencies being shuffled in time.

Inferring connectivity of an oscillatory network via the phase dynamics reconstruction

We review an approach for reconstructing oscillatory networks' undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network's coupling matrix in the first approximation in the coupling strength.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

White-matter degradation and dynamical compensation support age-related functional alterations in human brain

Structural connectivity of the brain at different ages is analyzed using diffusion-weighted magnetic resonance imaging (MRI) data. The largest decrease of streamlines is found in frontal regions and for long inter-hemispheric links. The average length of the tracts also decreases, but the clustering is unaffected. From functional MRI we identify age-related changes of dynamic functional connectivity (dFC) and spatial covariation features of functional connectivity (FC) links captured by metaconnectivity. They indicate more stable dFC, but wider range and variance of MC, whereas static features of FC did not show any significant differences with age. We implement individual connectivity in whole-brain models and test several hypotheses for the mechanisms of operation among underlying neural system. We demonstrate that age-related functional fingerprints are only supported if the model accounts for: (i) compensation of the individual brains for the overall loss of structural connectivity and (ii) decrease of propagation velocity due to the loss of myelination. We also show that with these 2 conditions, it is sufficient to decompose the time-delays as bimodal distribution that only distinguishes between intra- and inter-hemispheric delays, and that the same working point also captures the static FC the best, and produces the largest variability at slow time-scales.

High-resolution 3D demonstration of regional heterogeneity in the glymphatic system

Accumulating evidence indicates that the glymphatic system has a critical role in maintaining brain homeostasis. However, the detailed anatomy of the glymphatic pathway is not well understood, mostly due to a lack of high spatial resolution 3D visualization. In this study, a fluorescence micro-optical sectioning tomography (fMOST) was used to characterize the glymphatic architecture in the mouse brain. At 30 and 120 min after intracisternal infusion with fluorescent dextran (Dex-3), lectin was injected to stain the cerebral vasculature. Using fMOST, a high-resolution 3D dataset of the brain-wide distribution of Dex-3 was acquired. Combined with fluorescence microscopy and microplate array, the heterogeneous glymphatic flow and the preferential irrigated regions were identified. These cerebral regions containing large-caliber penetrating arteries and/or adjacent to the subarachnoid space had more robust CSF flow compared to other regions. Moreover, the major glymphatic vessels for CSF influx and fluid efflux in the entire brain were shown in 3D. This study demonstrates the regional heterogeneity in the glymphatic system and provides an anatomical resource for further investigation of the glymphatic function.

Selective blockade of rat brain T-type calcium channels provides insights on neurophysiological basis of arousal dependent resting state functional magnetic resonance imaging signals

A number of studies point to slow (0.1–2 Hz) brain rhythms as the basis for the resting-state functional magnetic resonance imaging (rsfMRI) signal. Slow waves exist in the absence of stimulation, propagate across the cortex, and are strongly modulated by vigilance similar to large portions of the rsfMRI signal. However, it is not clear if slow rhythms serve as the basis of all neural activity reflected in rsfMRI signals, or just the vigilance-dependent components. The rsfMRI data exhibit quasi-periodic patterns (QPPs) that appear to increase in strength with decreasing vigilance and propagate across the brain similar to slow rhythms. These QPPs can complicate the estimation of functional connectivity (FC) via rsfMRI, either by existing as unmodeled signal or by inducing additional wide-spread correlation between voxel-time courses of functionally connected brain regions. In this study, we examined the relationship between cortical slow rhythms and the rsfMRI signal, using a well-established pharmacological model of slow wave suppression. Suppression of cortical slow rhythms led to significant reduction in the amplitude of QPPs but increased rsfMRI measures of intrinsic FC in rats. The results suggest that cortical slow rhythms serve as the basis of only the vigilance-dependent components (e.g., QPPs) of rsfMRI signals. Further attenuation of these non-specific signals enhances delineation of brain functional networks.

Fluid transport in the brain

The brain harbors a unique ability to, figuratively speaking, shift its gears. During wakefulness, the brain is geared fully toward processing information and behaving, while homeostatic functions predominate during sleep. The blood-brain barrier establishes a stable environment that is optimal for neuronal function, yet the barrier imposes a physiological problem; transcapillary filtration that forms extracellular fluid in other organs is reduced to a minimum in brain. Consequently, the brain depends on a special fluid [the cerebrospinal fluid (CSF)] that is flushed into brain along the unique perivascular spaces created by astrocytic vascular endfeet. We describe this pathway, coined the term glymphatic system, based on its dependency on astrocytic vascular endfeet and their adluminal expression of aquaporin-4 water channels facing toward CSF-filled perivascular spaces. Glymphatic clearance of potentially harmful metabolic or protein waste products, such as amyloid-β, is primarily active during sleep, when its physiological drivers, the cardiac cycle, respiration, and slow vasomotion, together efficiently propel CSF inflow along periarterial spaces. The brain's extracellular space contains an abundance of proteoglycans and hyaluronan, which provide a low-resistance hydraulic conduit that rapidly can expand and shrink during the sleep-wake cycle. We describe this unique fluid system of the brain, which meets the brain's requisites to maintain homeostasis similar to peripheral organs, considering the blood-brain-barrier and the paths for formation and egress of the CSF.

Directional couplings between the respiration and parasympathetic control of the heart rate during sleep and wakefulness in healthy subjects at different ages

Cardiorespiratory interactions are important, both for understanding the fundamental processes of functioning of the human body and for development of methods for diagnostics of various pathologies. The properties of cardiorespiratory interaction are determined by the processes of autonomic control of blood circulation, which are modulated by the higher nervous activity. We study the directional couplings between the respiration and the process of parasympathetic control of the heart rate in the awake state and different stages of sleep in 96 healthy subjects from different age groups. The detection of directional couplings is carried out using the method of phase dynamics modeling applied to experimental RR-intervals and the signal of respiration. We reveal the presence of bidirectional couplings between the studied processes in all age groups. Our results show that the coupling from respiration to the process of parasympathetic control of the heart rate is stronger than the coupling in the opposite direction. The difference in the strength of bidirectional couplings between the considered processes is most pronounced in deep sleep.

The relationship between fasting-induced torpor, sleep, and wakefulness in laboratory mice

STUDY OBJECTIVES

Torpor is a regulated and reversible state of metabolic suppression used by many mammalian species to conserve energy. Whereas the relationship between torpor and sleep has been well-studied in seasonal hibernators, less is known about the effects of fasting-induced torpor on states of vigilance and brain activity in laboratory mice.

METHODS

Continuous monitoring of electroencephalogram (EEG), electromyogram (EMG), and surface body temperature was undertaken in adult, male C57BL/6 mice over consecutive days of scheduled restricted feeding.

RESULTS

All animals showed bouts of hypothermia that became progressively deeper and longer as fasting progressed. EEG and EMG were markedly affected by hypothermia, although the typical electrophysiological signatures of non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wakefulness enabled us to perform vigilance-state classification in all cases. Consistent with previous studies, hypothermic bouts were initiated from a state indistinguishable from NREM sleep, with EEG power decreasing gradually in parallel with decreasing surface body temperature. During deep hypothermia, REM sleep was largely abolished, and we observed shivering-associated intense bursts of muscle activity.

CONCLUSIONS

Our study highlights important similarities between EEG signatures of fasting-induced torpor in mice, daily torpor in Djungarian hamsters and hibernation in seasonally hibernating species. Future studies are necessary to clarify the effects on fasting-induced torpor on subsequent sleep.

An Index From Transcranial Doppler Signals for Evaluation of Stroke Rehabilitation Using External Counterpulsation

This study aimed to develop a sensitive index from transcranial Doppler (TCD) signals for quantitatively evaluating the effects of long-term external counterpulsation (ECP) treatment on stroke rehabilitation. We recruited 27 patients with unilateral ischemic stroke and a good acoustic window within 7 days of stroke onset. 15 of them received 35 daily 1-hour ECP treatment (ECP group) and the others underwent conventional therapy without ECP treatment (No-ECP group). We monitored blood flow in middle cerebral arteries on both sides by TCD, and analyzed them via discrete wavelet analysis method. The overall changes of National Institutes of Health Stroke Scale (NIHSS) and Barthel Index were assessed. A 'big-wave' phenomenon was observed in TCD signals of patients in ECP group after 35 days' treatment, with significant fluctuation in frequency interval from 0.010 to 0.034 Hz as main feature. A new index, which was denoted as I , was derived from this phenomenon. The I was significantly higher for patients in ECP group than that for patients in No-ECP group after 35-days' treatment ( 0.01). And the I was positively correlated with NIHSS change in ECP group ( ). The new index could be used as an effective indicator for evaluating enhancement of endothelial metabolism and neurogenic activity after long-term ECP treatment.

Detection of coupling in Duffing oscillator systems

In complex dynamical systems, the detection of coupling and its direction from observed time series is a challenging task. We study coupling in coupled Duffing oscillator systems in regular and chaotic dynamical regimes. By observing the conditional mutual information (CMI) based on the Shannon entropy, we successfully infer the direction of coupling for different system regimes. Moreover, we show that, in the weak coupling limit, the values of CMI can be used to infer the coupling parameters by computing the derivative of the conditional mutual information with respect to the coupling strength, called the information susceptibility. The complete numerical implementation is available at https://repo.ijs.si/mbresar/duffing-cmi.

Poincaré Plot Area of Gamma-Band EEG as a Measure of Emergence From Inhalational General Anesthesia

The Poincaré plot obtained from electroencephalography (EEG) has been used to evaluate the depth of anesthesia. A standalone EEG Analyzer application was developed; raw EEG signals obtained from a bispectral index (BIS) monitor were analyzed using an on-line monitoring system. Correlations between Poincaré plot parameters and other measurements associated with anesthesia depth were evaluated during emergence from inhalational general anesthesia. Of the participants, 20 were adults anesthetized with sevoflurane (adult_{_SEV}), 20 were adults anesthetized with desflurane (adult_{_DES}), and 20 were pediatric patients anesthetized with sevoflurane (ped_{_SEV}). EEG signals were preprocessed through six bandpass digital filters (f0: 0.5–47 Hz, f1: 0.5–8 Hz, f2: 8–13 Hz, f3: 13–20 Hz, f4: 20–30 Hz, and f5: 30–47 Hz). The Poincaré plot-area ratio (PP_AR = PP_{A_fx}/PP_{A_f0}, fx = f1∼f5) was analyzed at five frequency ranges. Regardless of the inhalational anesthetic used, there were strong linear correlations between the logarithm of PP_AR at f5 and BIS (R² = 0.67, 0.79, and 0.71, in the adult_{_SEV}, adult_{_DES}, and ped_{_SEV} groups, respectively). As an additional observation, a part of EMG activity at the gamma range of 30–47 Hz probably influenced the calculations of BIS and PP_{AR_f5} with a non-negligible level. The logarithm of PP_AR in the gamma band was most sensitive to state changes during the emergence process and could provide a new non-proprietary parameter that correlates with changes in BIS during measurement of anesthesia depth.

The neuronal associations of respiratory-volume variability in the resting state

The desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a negative association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.

Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP

Goal: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. We set to record and analyze for the first time concurrent electroencephalographic (EEG), electrocardiographic (ECG), and unrestrained whole-body plethysmographic (Pleth) signals from control (WT - wild type) and SUDEP-prone mice (KO- knockout Kcna1 animal model). Employing multivariate autoregressive models (MVAR) we measured all tri-organ effective directional interactions by the generalized partial directed coherence (GPDC) in the frequency domain over time (hours). When compared to the control (WT) animals, the SUDEP-prone (KO) animals exhibited (p < 0.001) reduced afferent and efferent interactions between the heart and the brain over the full frequency spectrum (0-200Hz), enhanced efferent interactions from the brain to the lungs and from the heart to the lungs at high (>90 Hz) frequencies (especially during periods with seizure activity), and decreased feedback from the lungs to the brain at low (<40 Hz) frequencies. These results show that impairment in the afferent and efferent pathways in the holistic neuro-cardio-respiratory network could lead to SUDEP, and effective connectivity measures and their dynamics could serve as novel biomarkers of susceptibility to SUDEP and seizures respectively.

Sleep as a Novel Biomarker and a Promising Therapeutic Target for Cerebral Small Vessel Disease: A Review Focusing on Alzheimer's Disease and the Blood-Brain Barrier

Cerebral small vessel disease (CSVD) is a leading cause of cognitive decline in elderly people and development of Alzheimer’s disease (AD). Blood–brain barrier (BBB) leakage is a key pathophysiological mechanism of amyloidal CSVD. Sleep plays a crucial role in keeping health of the central nervous system and in resistance to CSVD. The deficit of sleep contributes to accumulation of metabolites and toxins such as beta-amyloid in the brain and can lead to BBB disruption. Currently, sleep is considered as an important informative platform for diagnosis and therapy of AD. However, there are no effective methods for extracting of diagnostic information from sleep characteristics. In this review, we show strong evidence that slow wave activity (SWA) (0–0.5 Hz) during deep sleep reflects glymphatic pathology, the BBB leakage and memory deficit in AD. We also discuss that diagnostic and therapeutic targeting of SWA in AD might lead to be a novel era in effective therapy of AD. Moreover, we demonstrate that SWA can be pioneering non-invasive and bed–side technology for express diagnosis of the BBB permeability. Finally, we review the novel data about the methods of detection and enhancement of SWA that can be biomarker and a promising therapy of amyloidal CSVD and CSVD associated with the BBB disorders.

Synchronization transitions caused by time-varying coupling functions

Interacting dynamical systems are widespread in nature. The influence that one such system exerts on another is described by a coupling function; and the coupling functions extracted from the time-series of interacting dynamical systems are often found to be time-varying. Although much effort has been devoted to the analysis of coupling functions, the influence of time-variability on the associated dynamics remains largely unexplored. Motivated especially by coupling functions in biology, including the cardiorespiratory and neural delta-alpha coupling functions, this paper offers a contribution to the understanding of effects due to time-varying interactions. Through both numerics and mathematically rigorous theoretical consideration, we show that for time-variable coupling functions with time-independent net coupling strength, transitions into and out of phase- synchronization can occur, even though the frozen coupling functions determine phase-synchronization solely by virtue of their net coupling strength. Thus the information about interactions provided by the shape of coupling functions plays a greater role in determining behaviour when these coupling functions are time-variable. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

Uncovering temporal regularity in atmospheric dynamics through Hilbert phase analysis

Uncovering meaningful regularities in complex oscillatory signals is a challenging problem with applications across a wide range of disciplines. Here, we present a novel approach, based on the Hilbert transform (HT). We show that temporal periodicity can be uncovered by averaging the signal in a moving window of appropriated length, τ, before applying the HT. As a case study, we investigate global gridded surface air temperature (SAT) datasets. By analyzing the variation of the mean rotation period, T¯, of the Hilbert phase as a function of τ, we discover well-defined plateaus. In many geographical regions, the plateau corresponds to the expected 1-yr solar cycle; however, in regions where SAT dynamics is highly irregular, the plateaus reveal non-trivial periodicities, which can be interpreted in terms of climatic phenomena such as El Niño. In these regions, we also find that Fourier analysis is unable to detect the periodicity that emerges when τ increases and gradually washes out SAT variability. The values of T¯ obtained for different τs are then given to a standard machine learning algorithm. The results demonstrate that these features are informative and constitute a new approach for SAT time series classification. To support these results, we analyze the synthetic time series generated with a simple model and confirm that our method extracts information that is fully consistent with our knowledge of the model that generates the data. Remarkably, the variation of T¯ with τ in the synthetic data is similar to that observed in the real SAT data. This suggests that our model contains the basic mechanisms underlying the unveiled periodicities. Our results demonstrate that Hilbert analysis combined with temporal averaging is a powerful new tool for discovering hidden temporal regularity in complex oscillatory signals.

Measurement of Neurovascular Coupling in Neonates

Neurovascular coupling refers to the mechanism that links the transient neural activity to the subsequent change in cerebral blood flow, which is regulated by both chemical signals and mechanical effects. Recent studies suggest that neurovascular coupling in neonates and preterm born infants is different compared to adults. The hemodynamic response after a stimulus is later and less pronounced and the stimulus might even result in a negative (hypoxic) signal. In addition, studies both in animals and neonates confirm the presence of a short hypoxic period after a stimulus in preterm infants. In clinical practice, different methodologies exist to study neurovascular coupling. The combination of functional magnetic resonance imaging or functional near-infrared spectroscopy (brain hemodynamics) with EEG (brain function) is most commonly used in neonates. Especially near-infrared spectroscopy is of interest, since it is a non-invasive method that can be integrated easily in clinical care and is able to provide results concerning longer periods of time. Therefore, near-infrared spectroscopy can be used to develop a continuous non-invasive measurement system, that could be used to study neonates in different clinical settings, or neonates with different pathologies. The main challenge for the development of a continuous marker for neurovascular coupling is how the coupling between the signals can be described. In practice, a wide range of signal interaction measures exist. Moreover, biomedical signals often operate on different time scales. In a more general setting, other variables also have to be taken into account, such as oxygen saturation, carbon dioxide and blood pressure in order to describe neurovascular coupling in a concise manner. Recently, new mathematical techniques were developed to give an answer to these questions. This review discusses these recent developments.

Increased glymphatic influx is correlated with high EEG delta power and low heart rate in mice under anesthesia

The glymphatic system is responsible for brain-wide delivery of nutrients and clearance of waste via influx of cerebrospinal fluid (CSF) alongside perivascular spaces and through the brain. Glymphatic system activity increases during sleep or ketamine/xylazine (K/X) anesthesia, yet the mechanism(s) facilitating CSF influx are poorly understood. Here, we correlated influx of a CSF tracer into the brain with electroencephalogram (EEG) power, heart rate, blood pressure, and respiratory rate in wild-type mice under six different anesthesia regimens. We found that glymphatic CSF tracer influx was highest under K/X followed by isoflurane (ISO) supplemented with dexmedetomidine and pentobarbital. Mice anesthetized with α-chloralose, Avertin, or ISO exhibited low CSF tracer influx. This is the first study to show that glymphatic influx correlates positively with cortical delta power in EEG recordings and negatively with beta power and heart rate.

Causality, dynamical systems and the arrow of time

Using several methods for detection of causality in time series, we show in a numerical study that coupled chaotic dynamical systems violate the first principle of Granger causality that the cause precedes the effect. While such a violation can be observed in formal applications of time series analysis methods, it cannot occur in nature, due to the relation between entropy production and temporal irreversibility. The obtained knowledge, however, can help to understand the type of causal relations observed in experimental data, namely, it can help to distinguish linear transfer of time-delayed signals from nonlinear interactions. We illustrate these findings in causality detected in experimental time series from the climate system and mammalian cardio-respiratory interactions.

Assessment of Quadratic Nonlinear Cardiorespiratory Couplings During Tilt-Table Test by Means of Real Wavelet Biphase

OBJECTIVE

In this paper, a method for assessment of quadratic phase coupling (QPC) between respiration and heart rate variability (HRV) is presented.

METHODS

First, a method for QPC detection is proposed named real wavelet biphase (RWB). Then, a method for QPC quantification is proposed based on the normalized wavelet biamplitude (NWB). A simulation study has been conducted to test the reliability of RWB to identify QPC, even in the presence of constant delays between interacting oscillations, and to discriminate it from quadratic phase uncoupling. Significant QPC was assessed based on surrogate data analysis. Then, quadratic cardiorespiratory couplings were studied during a tilt-table test protocol of 17 young healthy subjects.

RESULTS

Simulation study showed that RWB is able to detect even weak QPC with delays in the range of [Formula: see text] s, which are usual in the autonomic nervous system (ANS) control of heart rate. Results from the database revealed a significant reduction ([Formula: see text]) of NWB between respiration and both low and high frequencies of HRV in head-up tilt position compared to early supine.

CONCLUSION

The proposed technique detects and quantifies robustly QPC and is able to track the coupling between respiration and various HRV components during ANS changes.

SIGNIFICANCE

The proposed method can help to assess alternations of nonlinear cardiorespiratory interactions related to ANS dysfunction and physiological regulation of HRV in cardiovascular diseases.

Analysis of generic coupling between EEG activity and PETCO2 in free breathing and breath-hold tasks using Maximal Information Coefficient (MIC)

Brain activations related to the control of breathing are not completely known. The respiratory system is a non-linear system. However, the relationship between neural and respiratory dynamics is usually estimated through linear correlation measures, completely neglecting possible underlying nonlinear interactions. This study evaluate the linear and nonlinear coupling between electroencephalographic (EEG) signal and variations in carbon dioxide (CO₂) signal related to different breathing task. During a free breathing and a voluntary breath hold tasks, the coupling between EEG power in nine different brain regions in delta (1–3 Hz) and alpha (8–13 Hz) bands and end-tidal CO₂ (P_ET CO₂) was evaluated. Specifically, the generic associations (i.e. linear and nonlinear correlations) and a “pure” nonlinear correlations were evaluated using the maximum information coefficient (MIC) and MIC-ρ² between the two signals, respectively (where ρ² represents the Pearson’s correlation coefficient). Our results show that in delta band, MIC indexes discriminate the two tasks in several regions, while in alpha band the same behaviour is observed for MIC-ρ², suggesting a generic coupling between delta EEG power and P_ETCO₂ and a pure nonlinear interaction between alpha EEG power and P_ETCO₂. Moreover, higher indexes values were found for breath hold task respect to free breathing.

Directionality indices: Testing information transfer with surrogate correction

Directionality indices can be used as an indicator of the asymmetry in coupling between systems and have found particular application in relation to neurological systems. The directionality index between two systems is a function of measures of information transfer in both directions. Here we illustrate that before inferring the directionality of coupling it is first necessary to consider the use of appropriate tests of significance. We propose a surrogate corrected directionality index which incorporates such testing. We also highlight the differences between testing the significance of the directionality index itself versus testing the individual measures of information transfer in each direction. To validate the approach we compared two different methods of estimating coupling, both of which have previously been used to estimate directionality indices. These were the modeling-based evolution map approach and a conditional mutual information (CMI) method for calculating dynamic information rates. For the CMI-based approach we also compared two different methods for estimating the CMI, an equiquantization-based estimator and a k-nearest neighbors estimator.

Paired motor cortex and cervical epidural electrical stimulation timed to converge in the spinal cord promotes lasting increases in motor responses

Key points

Pairing motor cortex stimulation and spinal cord epidural stimulation produced large augmentation in motor cortex evoked potentials if they were timed to converge in the spinal cord.

The modulation of cortical evoked potentials by spinal cord stimulation was largest when the spinal electrodes were placed over the dorsal root entry zone.

Repeated pairing of motor cortex and spinal cord stimulation caused lasting increases in evoked potentials from both sites, but only if the time between the stimuli was optimal.

Both immediate and lasting effects of paired stimulation are likely mediated by convergence of descending motor circuits and large diameter afferents onto common interneurons in the cervical spinal cord.

Abstract

Convergent activity in neural circuits can generate changes at their intersection. The rules of paired electrical stimulation are best understood for protocols that stimulate input circuits and their targets. We took a different approach by targeting the interaction of descending motor pathways and large diameter afferents in the spinal cord. We hypothesized that pairing stimulation of motor cortex and cervical spinal cord would strengthen motor responses through their convergence. We placed epidural electrodes over motor cortex and the dorsal cervical spinal cord in rats; motor evoked potentials (MEPs) were measured from biceps. MEPs evoked from motor cortex were robustly augmented with spinal epidural stimulation delivered at an intensity below the threshold for provoking an MEP. Augmentation was critically dependent on the timing and position of spinal stimulation. When the spinal stimulation was timed to coincide with the descending volley from motor cortex stimulation, MEPs were more than doubled. We then tested the effect of repeated pairing of motor cortex and spinal stimulation. Repetitive pairing caused strong augmentation of cortical MEPs and spinal excitability that lasted up to an hour after just 5 min of pairing. Additional physiology experiments support the hypothesis that paired stimulation is mediated by convergence of descending motor circuits and large diameter afferents in the spinal cord. The large effect size of this protocol and the conservation of the circuits being manipulated between rats and humans makes it worth pursuing for recovery of sensorimotor function after injury to the central nervous system.

Pairing motor cortex stimulation and spinal cord epidural stimulation produced large augmentation in motor cortex evoked potentials if they were timed to converge in the spinal cord.

The modulation of cortical evoked potentials by spinal cord stimulation was largest when the spinal electrodes were placed over the dorsal root entry zone.

Repeated pairing of motor cortex and spinal cord stimulation caused lasting increases in evoked potentials from both sites, but only if the time between the stimuli was optimal.

Both immediate and lasting effects of paired stimulation are likely mediated by convergence of descending motor circuits and large diameter afferents onto common interneurons in the cervical spinal cord.

Reconstructing networks of pulse-coupled oscillators from spike trains

We present an approach for reconstructing networks of pulse-coupled neuronlike oscillators from passive observation of pulse trains of all nodes. It is assumed that units are described by their phase response curves and that their phases are instantaneously reset by incoming pulses. Using an iterative procedure, we recover the properties of all nodes, namely their phase response curves and natural frequencies, as well as strengths of all directed connections.

Neural Cross-Frequency Coupling Functions

Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics-strength and form-are quantified. The method is applied to characterize the δ-to-α phase-to-phase neural coupling functions from electroencephalographic (EEG) data of the human resting state, and the differences that arise when the eyes are either open (EO) or closed (EC) are evaluated. The δ-to-α phase-to-phase coupling functions were reconstructed, quantified, compared, and followed as they evolved in time. Using phase-shuffled surrogates to test for significance, we show how the strength of the direct coupling, and the similarity and variability of the coupling functions, characterize the EO and EC states for different regions of the brain. We confirm an earlier observation that the direct coupling is stronger during EC, and we show for the first time that the coupling function is significantly less variable. Given the current understanding of the effects of e.g., aging and dementia on δ-waves, as well as the effect of cognitive and emotional tasks on α-waves, one may expect that new insights into the neural mechanisms underlying certain diseases will be obtained from studies of coupling functions. In principle, any pair of coupled oscillations could be studied in the same way as those shown here.

Alterations in the coupling functions between cortical and cardio-respiratory oscillations due to anaesthesia with propofol and sevoflurane

The precise mechanisms underlying general anaesthesia pose important and still open questions. To address them, we have studied anaesthesia induced by the widely used (intravenous) propofol and (inhalational) sevoflurane anaesthetics, computing cross-frequency coupling functions between neuronal, cardiac and respiratory oscillations in order to determine their mutual interactions. The phase domain coupling function reveals the form of the function defining the mechanism of an interaction, as well as its coupling strength. Using a method based on dynamical Bayesian inference, we have thus identified and analysed the coupling functions for six relationships. By quantitative assessment of the forms and strengths of the couplings, we have revealed how these relationships are altered by anaesthesia, also showing that some of them are differently affected by propofol and sevoflurane. These findings, together with the novel coupling function analysis, offer a new direction in the assessment of general anaesthesia and neurophysiological interactions, in general.

Experimental Study of the Triplet Synchronization of Coupled Nonidentical Mechanical Metronomes

Triplet synchrony is an interesting state when the phases and the frequencies of three coupled oscillators fulfill the conditions of a triplet locking, whereas every pair of systems remains asynchronous. Experimental observation of triplet synchrony is firstly realized in three coupled nonidentical mechanical metronomes. A more direct method based on the phase diagram is proposed to observe and determine triplet synchronization. Our results show that the stable triplet synchrony is observed in several intervals of the parameter space. Moreover, the experimental results are verified according to the theoretical model of the coupled metronomes. The outcomes are useful to understand the inner regimes of collective dynamics in coupled oscillators.

The discriminatory value of cardiorespiratory interactions in distinguishing awake from anaesthetised states: a randomised observational study

Depth of anaesthesia monitors usually analyse cerebral function with or without other physiological signals; non‐invasive monitoring of the measured cardiorespiratory signals alone would offer a simple, practical alternative. We aimed to investigate whether such signals, analysed with novel, non‐linear dynamic methods, would distinguish between the awake and anaesthetised states. We recorded ECG, respiration, skin temperature, pulse and skin conductivity before and during general anaesthesia in 27 subjects in good cardiovascular health, randomly allocated to receive propofol or sevoflurane. Mean values, variability and dynamic interactions were determined. Respiratory rate (p = 0.0002), skin conductivity (p = 0.03) and skin temperature (p = 0.00006) changed with sevoflurane, and skin temperature (p = 0.0005) with propofol. Pulse transit time increased by 17% with sevoflurane (p = 0.02) and 11% with propofol (p = 0.007). Sevoflurane reduced the wavelet energy of heart (p = 0.0004) and respiratory (p = 0.02) rate variability at all frequencies, whereas propofol decreased only the heart rate variability below 0.021 Hz (p < 0.05). The phase coherence was reduced by both agents at frequencies below 0.145 Hz (p < 0.05), whereas the cardiorespiratory synchronisation time was increased (p < 0.05). A classification analysis based on an optimal set of discriminatory parameters distinguished with 95% success between the awake and anaesthetised states. We suggest that these results can contribute to the design of new monitors of anaesthetic depth based on cardiovascular signals alone.

Homogeneous delays in the Kuramoto model with time-variable parameters

The Kuramoto model with time-varying parameters has been extended to consider the effect of delay in couplings. A collective dynamics arises from the interplay between the time scales of the original system, the external forcing, and the delays. This complex low-dimensional dynamics is described, uncovering an echo effect near the synchronization threshold. Hence, the delayed couplings substantially alter the dynamics of what is an open system and should be taken into consideration, depending on the ensemble's evolution time scale. We also introduce a first-harmonic approximation for the evolution of the mean field under harmonic forcing, valid for any delays and forcing of a coherent population.

Exercise and DHA prevent the negative effects of hypoxia on EEG and nerve conduction velocity

It is known that hypoxia has a negative effect on nervous system functions, but exercise and DHA (docosahexaenoic acid) have positive effect. In this study, it was investigated whether exercise and/or DHA can prevent the effects of hypoxia on EEG and nerve conduction velocity (NCV). 35 adult Wistar albino male rats were divided into five groups (n=7): control (C), hypoxia (H), hypoxia and exercise (HE), hypoxia and DHA (HD), and hypoxia and exercise and DHA (HED) groups. During the 28-day hypoxia exposure, the HE and HED groups of rats were exercised (0% incline, 30 m/min speed, 20 min/day, 5 days a week). In addition, DHA (36 mg/kg/day) was given by oral gavage to rats in the HD and HED groups. While EEG records were taken before and after the experimental period, NCV records were taken after the experimental period from anesthetized rats. Data were analyzed by paired t-test, one-way ANOVA, and post hoc Tukey test. In this study, it was shown that exposure to hypoxia decreased theta activity and NCV, but exercise and DHA reduced the delta activity, while theta, alpha, beta activities, and NCV were increased. These results have shown that the effects of hypoxia exposure on EEG and NCV can be prevented by exercise and/or DHA.

Time-frequency methods and voluntary ramped-frequency breathing: a powerful combination for exploration of human neurophysiological mechanisms

We experimentally altered the timing of respiratory motoneuron activity as a means to modulate and better understand otherwise hidden human central neural and hemodynamic oscillatory mechanisms. We recorded the electrocardiogram, finger photoplethysmographic arterial pressure, tidal carbon dioxide concentrations, and muscle sympathetic nerve activity in 13 healthy supine young men who gradually increased or decreased their breathing frequencies between 0.05 and 0.25 Hz over 9-min periods. We analyzed results with traditional time- and frequency-domain methods, and also with time-frequency methods (wavelet transform, wavelet phase coherence, and directional coupling). We determined statistical significance and identified frequency boundaries by comparing measurements with randomly generated surrogates. Our results support several major conclusions. First, respiration causally modulates both sympathetic (weakly) and vagal motoneuron (strongly) oscillations over a wide frequency range-one that extends well below the frequency of actual breaths. Second, breathing frequency broadly modulates vagal baroreflex gain, with peak gains registered in the low frequency range. Third, breathing frequency does not influence median levels of sympathetic or vagal activity over time. Fourth, phase relations between arterial pressure and sympathetic and vagal motoneurons are unaffected by breathing, and are therefore likely secondary to intrinsic responsiveness of these motoneurons to other synaptic inputs. Finally, breathing frequency does not affect phase coherence between diastolic pressure and muscle sympathetic oscillations, but it augments phase coherence between systolic pressure and R-R interval oscillations over a limited portion of the usual breathing frequency range. These results refine understanding of autonomic oscillatory processes and those physiological mechanisms known as the human respiratory gate.

In vivo recordings of GnRH neuron firing reveal heterogeneity and dependence upon GABAA receptor signaling

The gonadotropin-releasing hormone (GnRH) neurons are the key cells regulating fertility in all mammalian species. The scattered distribution of these neurons has made investigation of their properties extremely difficult and the key goal of recording their electrical activity in vivo near impossible. The caudal-most extension of the GnRH neuron continuum brings some cells very close to the base of the brain at the level of the anterior hypothalamic area. Taking insight from this, we developed an experimental procedure in anesthetized GnRH-GFP mice that allows the electrical activity of these GnRH neurons to be recorded in vivo. On-cell recordings revealed that the majority of GnRH neurons (86%) were spontaneously active, exhibiting a range of firing patterns, although only a minority (15%) exhibited burst firing. Mean firing frequencies ranged from 0.06 to 3.65 Hz, with the most common interspike interval being ~500 ms. All GnRH neurons tested were activated by AMPA and kisspeptin. Whereas the GABAA receptor agonist muscimol evoked excitatory, inhibitory, or mixed effects on GnRH neuron firing, the GABAA receptor antagonist picrotoxin resulted in a consistent suppression of firing. These observations represent the first electrical recordings of GnRH neurons in vivo. They reveal that GnRH neurons in vivo exhibit considerable heterogeneity in their firing patterns with both similarities and differences to firing in vitro. These variable patterns of firing in vivo are found to be critically dependent upon ongoing GABAA receptor signaling.

Detecting triplet locking by triplet synchronization indices

We discuss the effect of triplet synchrony in oscillatory networks. In this state the phases and the frequencies of three coupled oscillators fulfill the conditions of a triplet locking, whereas every pair of systems remains asynchronous. We suggest an easy to compute measure, a triplet synchronization index, which can be used to detect such states from experimental data.

Characterizing an ensemble of interacting oscillators: the mean-field variability index

We introduce a way of characterizing an ensemble of interacting oscillators in terms of their mean-field variability index κ, a dimensionless parameter defined as the variance of the oscillators' mean field r divided by the mean square of r. Based on the assumption that the overall mean field is the sum of a very large number of oscillators, each giving a small contribution to the total signal, we show that κ depends on the mutual interactions between the oscillators, independently of their number or spectral properties. For purely random phasors, or a noninteracting ensemble of oscillators, κ converges on 0.215. Interactions push κ in different directions: lower where there is interoscillator phase coherence, tending to zero for complete phase synchronization, or higher for amplitude synchronization or intermittent synchronization. We calculate κ for several different cases to illustrate its utility, using both numerically simulated data and electroencephalograph signals from the brains of human subjects while awake, while anesthetized, and while undergoing an epileptic fit.

Dynamical Bayesian inference of time-evolving interactions: from a pair of coupled oscillators to networks of oscillators

Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. [Phys. Rev. Lett. 109, 024101 (2012)] introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time-evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically generated data, data from an analog electronic circuit, and cardiorespiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.

Kuramoto model with time-varying parameters

We analyze the Kuramoto model generalized by explicit consideration of deterministically time-varying parameters. The oscillators' natural frequencies and/or couplings are influenced by external forces with constant or distributed strengths. A dynamics of the collective rhythms is observed, consisting of the external system superimposed on the autonomous one, a characteristic feature of many thermodynamically open systems. This deterministic, stable, continuously time-dependent, collective behavior is fully described, and the external impact to the original system is defined in both the adiabatic and the nonadiabatic limits.

Cortico-cardio-respiratory network interactions during anesthesia

General anesthetics are used during medical and surgical procedures to reversibly induce a state of total unconsciousness in patients. Here, we investigate, from a dynamic network perspective, how the cortical and cardiovascular systems behave during anesthesia by applying nonparametric spectral techniques to cortical electroencephalography, electrocardiogram and respiratory signals recorded from anesthetized rats under two drugs, ketamine-xylazine (KX) and pentobarbital (PB). We find that the patterns of low-frequency cortico-cardio-respiratory network interactions may undergo significant changes in network activity strengths and in number of network links at different depths of anesthesia dependent upon anesthetics used.

Effects of awareness and nociception on heart rate variability during general anaesthesia

During anaesthesia awareness and nociception are serious complications that may further lead to haemodynamic instability. Specific monitoring of depth of hypnosis and depth of analgesia based on heart rate variability (HRV) analysis is eligible to improve patient safety and reduce efforts in post-operative care. Consequently, in this analysis we assess the applicability of HRV parameters during surgical interventions with standardized intravenous propofol-remifentanil-anaesthesia. Peri-operative electrocardiograms were recorded from cardiovascular stable patients (ASA Score I/II, N = 32, age: 36.4 ± 11.23 a, BMI: 25.2 ± 3.16) scheduled for trauma and dentofacial surgery. HRV time- and frequency-domain parameters, measures of complexity and nonlinear dynamics were compared by analysing longitudinally distributed 300 s intervals preceding/following induction of anaesthesia (BL-I1), intubation (I1-I2) and extubation (E1-E2). Mean value (meanNN) and standard deviation (sdNN) of the heart rate are influenced in BL-I1 (p < 0.001), I1-I2 (p < 0.05) and E1-E2 (p < 0.001). The number of forbidden words of symbolic dynamics changes significantly for BL-I1 (p < 0.001) and not for I1-I2 and E1-E2 (p > 0.05). Probability of low-variability POLVAR10 is significantly altered in all comparisons (BL-I1: Δ = 0.032, p < 0.01, I1-I2: Δ = 0.12, p < 0.05, E1-E2: Δ = 0.169, p < 0.01) but especially during nociception. While standard time-domain parameters lacked selectivity, parameters of symbolic dynamics appear to be specifically influenced by changes in depth of hypnosis and nociception, respectively. However, the lack of steady-state ventilation/breathing in this study needs to be considered in future research. To be used for clinical anaesthesia monitoring our results have to be prospectively validated in clinical studies.

Spontaneous fMRI activity during resting wakefulness and sleep

Functional magnetic resonance imaging (fMRI) studies performed during both waking rest and sleep show that the brain is continually active in distinct patterns that appear to reflect its underlying functional connectivity. In this review, potential sources that contribute to spontaneous fMRI activity will be discussed.

Global analysis of phase locking in gene expression during cell cycle: the potential in network modeling

Background

In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation.

Results

In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation.

Conclusions

Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.

Mass synchronization: occurrence and its control with possible applications to brain dynamics

Occurrence of strong or mass synchronization of a large number of neuronal populations in the brain characterizes its pathological states. In order to establish an understanding of the mechanism underlying such pathological synchronization, we present a model of coupled populations of phase oscillators representing the interacting neuronal populations. Through numerical analysis, we discuss the occurrence of mass synchronization in the model, where a source population which gets strongly synchronized drives the target populations onto mass synchronization. We hypothesize and identify a possible cause for the occurrence of such a synchronization, which is so far unknown: Pathological synchronization is caused not just because of the increase in the strength of coupling between the populations but also because of the strength of the strong synchronization of the drive population. We propose a demand controlled method to control this pathological synchronization by providing a delayed feedback where the strength and frequency of the synchronization determine the strength and the time delay of the feedback. We provide an analytical explanation for the occurrence of pathological synchronization and its control in the thermodynamic limit.

Spatiotemporal coupling between hippocampal acetylcholine release and theta oscillations in vivo

Both acetylcholine (ACh) and theta oscillations are important for learning and memory, but the dynamic interaction between these two processes remains unclear. Recent advances in amperometry techniques have revealed phasic ACh releases in vivo. However, it is unknown whether phasic ACh release co-occurs with theta oscillations. We investigated this issue in the CA1 region of urethane-anesthetized male rats using amperometric and electrophysiological recordings. We found that ACh release was highly correlated with the appearance of both spontaneous and induced theta oscillations. Moreover, the maximal ACh release was observed around or slightly above the pyramidal layer. Interestingly, such release lagged behind theta initiation by 25-60 s. The slow ACh release profile was matched by the slow firing rate increase of a subset of medial-septal low-firing-rate neurons. Together, these results establish, for the first time, the in vivo coupling between phasic ACh release and theta oscillations on spatiotemporal scales much finer than previously known. These findings also suggest that phasic ACh is not required for theta initiation and may instead operate synergistically with theta oscillations to promote neural plasticity in the service of learning and memory.