Investigating paradoxical hysteresis effects in the mouse neocortical slice model

Explosive Synchronization-Based Brain Modulation Reduces Hypersensitivity in the Brain Network: A Computational Model Study

Fibromyalgia (FM) is a chronic pain condition that is characterized by hypersensitivity to multimodal sensory stimuli, widespread pain, and fatigue. We have previously proposed explosive synchronization (ES), a phenomenon wherein a small perturbation to a network can lead to an abrupt state transition, as a potential mechanism of the hypersensitive FM brain. Therefore, we hypothesized that converting a brain network from ES to general synchronization (GS) may reduce the hypersensitivity of FM brain. To find an effective brain network modulation to convert ES into GS, we constructed a large-scale brain network model near criticality (i.e., an optimally balanced state between order and disorders), which reflects brain dynamics in conscious wakefulness, and adjusted two parameters: local structural connectivity and signal randomness of target brain regions. The network sensitivity to global stimuli was compared between the brain networks before and after the modulation. We found that only increasing the local connectivity of hubs (nodes with intense connections) changes ES to GS, reducing the sensitivity, whereas other types of modulation such as decreasing local connectivity, increasing and decreasing signal randomness are not effective. This study would help to develop a network mechanism-based brain modulation method to reduce the hypersensitivity in FM.

Electroencephalographic Evidence for Individual Neural Inertia in Mice That Decreases With Time

Previous studies have demonstrated that the brain has an intrinsic resistance to changes in arousal state. This resistance is most easily measured at the population level in the setting of general anesthesia and has been termed neural inertia. To date, no study has attempted to determine neural inertia in individuals. We hypothesize that individuals with markedly increased or decreased neural inertia might be at increased risk for complications related to state transitions, from awareness under anesthesia, to delayed emergence or confusion/impairment after emergence. Hence, an improved theoretical and practical understanding of neural inertia may have the potential to identify individuals at increased risk for these complications. This study was designed to explicitly measure neural inertia in individuals and empirically test the stochastic model of neural inertia using spectral analysis of the murine EEG. EEG was measured after induction of and emergence from isoflurane administered near the EC₅₀ dose for loss of righting in genetically inbred mice on a timescale that minimizes pharmacokinetic confounds. Neural inertia was assessed by employing classifiers constructed using linear discriminant or supervised machine learning methods to determine if features of EEG spectra reliably demonstrate path dependence at steady-state anesthesia. We also report the existence of neural inertia at the individual level, as well as the population level, and that neural inertia decreases over time, providing direct empirical evidence supporting the predictions of the stochastic model of neural inertia.

Adiabatic dynamic causal modelling

This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.

Asymmetric neural dynamics characterize loss and recovery of consciousness

Anesthetics are known to disrupt neural interactions in cortical and subcortical brain circuits. While the effect of anesthetic drugs on consciousness is reversible, the neural mechanism mediating induction and recovery may be different. Insight into these distinct mechanisms can be gained from a systematic comparison of neural dynamics during slow induction of and emergence from anesthesia. To this end, we used functional magnetic resonance imaging (fMRI) data obtained in healthy volunteers before, during, and after the administration of propofol at incrementally adjusted target concentrations. We analyzed functional connectivity of corticocortical and subcorticocortical networks and the temporal autocorrelation of fMRI signal as an index of neural processing timescales. We found that en route to unconsciousness, temporal autocorrelation across the entire brain gradually increased, whereas functional connectivity gradually decreased. In contrast, regaining consciousness was associated with an abrupt restoration of cortical but not subcortical temporal autocorrelation and an abrupt boost of subcorticocortical functional connectivity. Pharmacokinetic effects could not account for the difference in neural dynamics between induction and emergence. We conclude that the induction and recovery phases of anesthesia follow asymmetric neural dynamics. A rapid increase in the speed of cortical neural processing and subcorticocortical neural interactions may be a mechanism that reboots consciousness.

Mechanisms of hysteresis in human brain networks during transitions of consciousness and unconsciousness: Theoretical principles and empirical evidence

Hysteresis, the discrepancy in forward and reverse pathways of state transitions, is observed during changing levels of consciousness. Identifying the underlying mechanism of hysteresis phenomena in the brain will enhance the ability to understand, monitor, and control state transitions related to consciousness. We hypothesized that hysteresis in brain networks shares the same underlying mechanism of hysteresis as other biological and non-biological networks. In particular, we hypothesized that the principle of explosive synchronization, which can mediate abrupt state transitions, would be critical to explaining hysteresis in the brain during conscious state transitions. We analyzed high-density electroencephalogram (EEG) that was acquired in healthy human volunteers during conscious state transitions induced by the general anesthetics sevoflurane or ketamine. We developed a novel method to monitor the temporal evolution of EEG networks in a parameter space, which consists of the strength and topography of EEG-based networks. Furthermore, we studied conditions of explosive synchronization in anatomically informed human brain network models. We identified hysteresis in the trajectory of functional brain networks during state transitions. The model study and empirical data analysis explained various hysteresis phenomena during the loss and recovery of consciousness in a principled way: (1) more potent anesthetics induce a larger hysteresis; (2) a larger range of EEG frequencies facilitates transitions into unconsciousness and impedes the return of consciousness; (3) hysteresis of connectivity is larger than that of EEG power; and (4) the structure and strength of functional brain networks reconfigure differently during the loss vs. recovery of consciousness. We conclude that the hysteresis phenomena observed during the loss and recovery of consciousness are generic network features. Furthermore, the state transitions are grounded in the same principle as state transitions in complex non-biological networks, especially during perturbation. These findings suggest the possibility of predicting and modulating hysteresis of conscious state transitions in large-scale brain networks.

Hysteresis, characterized by distinct forward and reverse phase transitions, is ubiquitous in nature. For example, there are distinct temperatures for water freezing and ice melting. Similarly, it has been found that state transitions related to consciousness exhibit hysteresis. In particular, the concentration of general anesthetics required to achieve loss of consciousness is significantly higher than the concentration at which consciousness is regained. However, it is unknown whether this is trivially reducible to the pharmacology of these drugs or if it is something related to brain function itself. In this study, we took a novel, network-based approach and hypothesized that the hysteresis observed during anesthetic state transitions shares the same underlying mechanism as that observed in non-biological networks. Our computational modeling, analytic study, and high-density human EEG analysis suggest that various hysteresis phenomena during loss and recovery of consciousness can be explained in principled ways by generic network features. Identifying these network mechanisms of hysteresis in the brain also provides a unified framework for understanding the radically different conscious state transitions associated with sleep, anesthesia, and disorders of consciousness.

Brain Aggregates: An Effective In Vitro Cell Culture System Modeling Neurodegenerative Diseases

Drug discovery for neurodegenerative diseases is particularly challenging because of the discrepancies in drug effects between in vitro and in vivo studies. These discrepancies occur in part because current cell culture systems used for drug screening have many limitations. First, few cell culture systems accurately model human aging or neurodegenerative diseases. Second, drug efficacy may differ between dividing and stationary cells, the latter resembling nondividing neurons in the CNS. Brain aggregates (BrnAggs) derived from embryonic day 15 gestation mouse embryos may represent neuropathogenic processes in prion disease and reflect in vivo drug efficacy. Here, we report a new method for the production of BrnAggs suitable for drug screening and suggest that BrnAggs can model additional neurological diseases such as tauopathies. We also report a functional assay with BrnAggs by measuring electrophysiological activities. Our data suggest that BrnAggs could serve as an effective in vitro cell culture system for drug discovery for neurodegenerative diseases.

Genetic and anatomical basis of the barrier separating wakefulness and anesthetic-induced unresponsiveness

A robust, bistable switch regulates the fluctuations between wakefulness and natural sleep as well as those between wakefulness and anesthetic-induced unresponsiveness. We previously provided experimental evidence for the existence of a behavioral barrier to transitions between these states of arousal, which we call neural inertia. Here we show that neural inertia is controlled by processes that contribute to sleep homeostasis and requires four genes involved in electrical excitability: Sh, sss, na and unc79. Although loss of function mutations in these genes can increase or decrease sensitivity to anesthesia induction, surprisingly, they all collapse neural inertia. These effects are genetically selective: neural inertia is not perturbed by loss-of-function mutations in all genes required for the sleep/wake cycle. These effects are also anatomically selective: sss acts in different neurons to influence arousal-promoting and arousal-suppressing processes underlying neural inertia. Supporting the idea that anesthesia and sleep share some, but not all, genetic and anatomical arousal-regulating pathways, we demonstrate that increasing homeostatic sleep drive widens the neural inertial barrier. We propose that processes selectively contributing to sleep homeostasis and neural inertia may be impaired in pathophysiological conditions such as coma and persistent vegetative states.

An annual 234 million surgical procedures are performed worldwide, making general anesthetics among the most common drugs administered to humans. Remarkably, however, we still do not understand the mechanisms by which general anesthetics render patients unconscious or the processes that re-establish consciousness upon emergence from anesthesia. We previously showed that the brain resists transitions between the wakeful and anesthesia states by generating a barrier to such transitions in both directions. We also showed that the existence of this barrier is conserved from invertebrates to mammals. In our present work, we use the genetic tractability and the simplified nervous system of the fruit fly Drosophila melanogaster to show that four genes are required to maintain this barrier. We also show that, as in mammals, there is overlap between pathways regulating natural sleep and general anesthesia. We propose that some of these shared pathways are impaired in conditions such as coma and persistent vegetative states, in which the barrier to transitioning to the waking state appears to be insurmountable.

Quantification of neocortical slice diffusion characteristics using pharmacokinetic and pharmacodynamic modelling

Pharmacological brain slice experiments are complicated by the need to ensure adequate drug delivery deep into the healthy layers of the tissue. Because tissue slices have no blood supply, this is achieved solely by passive drug diffusion. The aim of this study was to determine whether pharmacokinetic/pharmacodynamic (PKPD) modeling could be adapted to estimate drug diffusion times in neocortical brain slices. No-magnesium seizure-like event (SLE) activity was generated in 41 slices (400  μ m). Two anesthetic agents, etomidate (24  μ M, n = 14) and thiopental (250  μ M, n = 14), and magnesium ions (n = 13) were delivered to effect reversible reductions in SLE frequency. Concentration-effect hysteresis loops were collapsed using a first order rate constant model and equilibrium half-lives (t1/2Ke0) derived. The t1/2Ke0 values obtained were consistent with expectations. The median (range) t1/2Ke0 of 83.1 (19.4-330.1) min for etomidate is in keeping with its known slow diffusion into brain slice tissue. Values for etomidate and thiopental (111.8 (27.8-198.0) min) were similar, while magnesium had a significantly faster equilibration rate (t1/2Ke0 of 26.1 (8.6-77.0) min) compared to the anesthetics, as expected for a simple ion. In conclusion, PKPD modeling is a simple and practical method that can be applied to brain slice experiments for investigating drug diffusion characteristics.

Direct measurement of nitrous oxide kinetics

BACKGROUND

Using conscious subjects, measurement of the effects of low concentrations of anaesthetic agents can allow the dynamics of onset and offset of the agent to be measured and kinetic values estimated. However, the tests have to be rapid and preferably assess cerebral function.

METHODS

We used a short version of the digit symbol substitution test (DSST) that allowed frequent measurement of the impairment caused by nitrous oxide. We compared 10 min of onset and offset of breathing 5% and 30% nitrous oxide in 30% oxygen, compared with 30% oxygen only. End-tidal nitrous oxide concentrations were used to predict the concentration in a central compartment, according to a range of T(1/2) values chosen to be consistent with possible cerebral blood flow values.

RESULTS

We studied 19 volunteers and estimated a mean response. Only 30% nitrous oxide decreased the DSST. When DSST scores were related to the values in the predicted central compartment, the best dose-effect relationship was found when the T(1/2) was 37 s, consistent with a regional blood flow of about 120 ml 100 g(-1) min(-1).

CONCLUSIONS

The onset of nitrous oxide effect on DSST is rapid, consistent with the perfusion of metabolically active cerebral cortical tissues. The rate of onset is greater than previous measures based on a motor test which involved the function of subcortical structures in the central nervous system.