Network recruitment to coherent oscillations in a hippocampal computer model

Anticonvulsant Effect of Carbenoxolone on Chronic Epileptic Rats and Its Mechanism Related to Connexin and High-Frequency Oscillations

Objective

This study was designed to investigate the influence and mechanism of gap junction carbenoxolone (CBX) on dynamic changes in the spectral power of ripples and fast ripples (FRs) in the hippocampus of chronic epileptic rats.

Methods

The lithium-pilocarpine (PILO) status epilepticus (SE) model (PILO group) and the CBX pretreatment model (CBX + PILO group) were established to analyze dynamic changes in the spectral power of ripples and FRs, and the dynamic expression of connexin (CX)26, CX32, CX36, and CX43 in the hippocampus of chronic epileptic rats.

Results

Within 28 days after SE, the number of spontaneous recurrent seizures (SRSs) in the PILO group was significantly higher than that in the CBX + PILO group. The average spectral power of FRs in the PILO group was significantly higher than the baseline level at 1 and 7 days after SE. The average spectral power of FRs in the PILO group was significantly higher than that in the CBX + PILO group at 1, 7, and 14 days after SE. Seizures induced an increase in CX43 expression at 1 and 7 days after SE, but had no significant effect on CX26, CX36, or CX32. CBX pretreatment did not affect the expression of CXs in the hippocampus of normal rats, but it inhibited the expression of CX43 in epileptic rats. The number of SRSs at 2 and 4 weeks after SE had the highest correlation with the average spectral power of FRs; the average spectral power of FRs was moderately correlated with the expression of CX43.

Conclusion

The results of this study indicate that the energy of FRs may be regulated by its interference with the expression of CX43, and thus, affect seizures. Blocking the expression of CX43 thereby reduces the formation of pathological high-frequency oscillations (HFOs), making it a promising strategy for the treatment of chronic epilepsy.

Interictal high frequency background activity as a biomarker of epileptogenic tissue

High frequency oscillations (HFOs) are very brief events that are a well-established biomarker of the epileptogenic zone (EZ) but are rare and comprise only a tiny fraction of the total recorded EEG. We hypothesize that the interictal high frequency ‘background’ data, which has received little attention but represents the majority of the EEG record, also may contain additional, novel information for identifying the EZ. We analysed intracranial EEG (30–500 Hz frequency range) acquired from 24 patients who underwent resective surgery. We computed 38 quantitative features based on all usable, interictal data (63–307 h per subject), excluding all detected HFOs. We assessed association between each feature and the seizure onset zone (SOZ) and resected volume (RV) using logistic regression. A pathology score per channel was also created via principle component analysis and logistic regression, using hold-out-one-patient cross-validation to avoid in-sample training. Association of the pathology score with the SOZ and RV was quantified using an asymmetry measure. Many features were associated with the SOZ: 23/38 features had odds ratios >1.3 or <0.7 and 17/38 had odds ratios different than zero with high significance (P < 0.001/39, logistic regression with Bonferroni Correction). The pathology score, the rate of HFOs, and their channel-wise product were each strongly associated with the SOZ [median asymmetry ≥0.44, good surgery outcome patients; median asymmetry ≥0.40, patients with other outcomes; 95% confidence interval (CI) > 0.27 in both cases]. The pathology score and the channel-wise product also had higher asymmetry with respect to the SOZ than the HFO rate alone (median difference in asymmetry ≥0.18, 95% CI >0.05). These results support that the high frequency background data contains useful information for determining the EZ, distinct and complementary to information from detected HFOs. The concordance between the high frequency activity pathology score and the rate of HFOs appears to be a better biomarker of epileptic tissue than either measure alone.

Docosanoids and elovanoids from omega-3 fatty acids are pro-homeostatic modulators of inflammatory responses, cell damage and neuroprotection

The functional significance of the selective enrichment of the omega-3 essential fatty acid docosahexaenoic acid (DHA; 22C and 6 double bonds) in cellular membrane phospholipids of the nervous system is being clarified by defining its specific roles on membrane protein function and by the uncovering of the bioactive mediators, docosanoids and elovanoids (ELVs). Here, we describe the preferential uptake and DHA metabolism in photoreceptors and brain as well as the significance of the Adiponectin receptor 1 in DHA retention and photoreceptor cell (PRC) survival. We now know that this integral membrane protein is engaged in DHA retention as a necessary event for the function of PRCs and retinal pigment epithelial (RPE) cells. We present an overview of how a) NPD1 selectively mediates preconditioning rescue of RPE and PR cells; b) NPD1 restores aberrant neuronal networks in experimental epileptogenesis; c) the decreased ability to biosynthesize NPD1 in memory hippocampal areas of early stages of Alzheimer's disease takes place; d) NPD1 protection of dopaminergic circuits in an in vitro model using neurotoxins; and e) bioactivity elicited by DHA and NPD1 activate a neuroprotective gene-expression program that includes the expression of Bcl-2 family members affected by Aβ42, DHA, or NPD1. In addition, we highlight ELOVL4 (ELOngation of Very Long chain fatty acids-4), specifically the neurological and ophthalmological consequences of its mutations, and their role in providing precursors for the biosynthesis of ELVs. Then we outline evidence of ELVs ability to protect RPE cells, which sustain PRC integrity. In the last section, we present a summary of the protective bioactivity of docosanoids and ELVs in experimental ischemic stroke. The identification of early mechanisms of neural cell survival mediated by DHA-synthesized ELVs and docosanoids contributes to the understanding of cell function, pro-homeostatic cellular modulation, inflammatory responses, and innate immunity, opening avenues for prevention and therapeutic applications in neurotrauma, stroke and neurodegenerative diseases.

Nonictal EEG biomarkers for diagnosis and treatment

There are no reliable nonictal biomarkers for epilepsy, electroencephalography (EEG) or otherwise, but efforts to identify biomarkers that would predict the development of epilepsy after a potential epileptogenic insult, diagnose the existence of epilepsy, or assess the effects of antiseizure or antiepileptogenic interventions are relying heavily on electrophysiology. The most promising EEG biomarkers to date are pathologic high‐frequency oscillations (pHFOs), brief EEG events in the range of 100 to 600 Hz, which are believed to reflect summated action potentials from synchronously bursting neurons. Studies of patients with epilepsy, and experimental animal models, have been based primarily on direct brain recording, which makes pHFOs potentially useful for localizing the epileptogenic zone for surgical resection, but application for other diagnostic and therapeutic purposes is limited. Consequently, recent efforts have involved identification of HFOs recorded with scalp electrodes, and with magnetoencephalography, which may reflect the same pathophysiologic mechanisms as pHFOs recorded directly from the brain. The search is also on for other EEG changes that might serve as epilepsy biomarkers, and candidates include arcuate rhythms, which may reflect repetitive pHFOs, reduction in theta rhythm, which correlates with epileptogenesis in several rodent models of epilepsy, and shortened sleep spindles that correlate with ictogenesis.

Seizure development in the acute intrahippocampal epileptic focus

Currently, an epileptic seizure is considered to involve a temporary network that exists for a finite period of time. Formation of this network evolves through spread of epileptiform activity from a seizure onset zone (SOZ). Propagation of seizures evoked by kainic acid injection in hippocampus to different brain areas was analyzed at macro- and micro-intervals. The mean latency of seizure occurrence in different brain areas varied between 0.5 sec and 85 sec (mean 14.9 ± 14.5 (SD)), and it increased after each consecutive seizure in areas located contralateral to the area of injection, but not in the ipsilateral sites. We have shown that only 41% of epileptic individual events in target brain areas were driven by epileptic events generated in the SOZ once the seizure began. Fifty-nine percent of epileptiform events in target areas occurred one millisecond before or after events in the SOZ. These data illustrate that during seizure maintenance, only some individual epileptiform events in areas outside of SOZ could be consistently triggered by the SOZ; and the majority must be triggered by a driver located outside the SOZ or brain areas involved in ictal activity could be coupled to each other via an unknown mechanism such as stochastic resonance.

Update on the mechanisms and roles of high-frequency oscillations in seizures and epileptic disorders

High-frequency oscillations (HFOs) are a type of brain activity that is recorded from brain regions capable of generating seizures. Because of the close association of HFOs with epileptogenic tissue and ictogenesis, understanding their cellular and network mechanisms could provide valuable information about the organization of epileptogenic networks and how seizures emerge from the abnormal activity of these networks. In this review, we summarize the most recent advances in the field of HFOs and provide a critical evaluation of new observations within the context of already established knowledge. Recent improvements in recording technology and the introduction of optogenetics into epilepsy research have intensified experimental work on HFOs. Using advanced computer models, new cellular substrates of epileptic HFOs were identified and the role of specific neuronal subtypes in HFO genesis was determined. Traditionally, the pathogenesis of HFOs was explored mainly in patients with temporal lobe epilepsy and in animal models mimicking this condition. HFOs have also been reported to occur in other epileptic disorders and models such as neocortical epilepsy, genetically determined epilepsies, and infantile spasms, which further support the significance of HFOs in the pathophysiology of epilepsy. It is increasingly recognized that HFOs are generated by multiple mechanisms at both the cellular and network levels. Future studies on HFOs combining novel high-resolution in vivo imaging techniques and precise control of neuronal behavior using optogenetics or chemogenetics will provide evidence about the causal role of HFOs in seizures and epileptogenesis. Detailed understanding of the pathophysiology of HFOs will propel better HFO classification and increase their information yield for clinical and diagnostic purposes.

Emergence of Narrowband High Frequency Oscillations from Asynchronous, Uncoupled Neural Firing

Previous experimental studies have demonstrated the emergence of narrowband local field potential oscillations during epileptic seizures in which the underlying neural activity appears to be completely asynchronous. We derive a mathematical model explaining how this counterintuitive phenomenon may occur, showing that a population of independent, completely asynchronous neurons may produce narrowband oscillations if each neuron fires quasi-periodically, without requiring any intrinsic oscillatory cells or feedback inhibition. This quasi-periodicity can occur through cells with similar frequency-current ([Formula: see text]-[Formula: see text]) curves receiving a similar, high amount of uncorrelated synaptic noise. Thus, this source of oscillatory behavior is distinct from the usual cases (pacemaker cells entraining a network, or oscillations being an inherent property of the network structure), as it requires no oscillatory drive nor any specific network or cellular properties other than cells that repetitively fire with continual stimulus. We also deduce bounds on the degree of variability in neural spike-timing which will permit the emergence of such oscillations, both for action potential- and postsynaptic potential-dominated LFPs. These results suggest that even an uncoupled network may generate collective rhythms, implying that the breakdown of inhibition and high synaptic input often observed during epileptic seizures may generate narrowband oscillations. We propose that this mechanism may explain why so many disparate epileptic and normal brain mechanisms can produce similar high frequency oscillations.

Differential frequency-dependent antidromic resonance of the Schaffer collaterals and mossy fibers

To better understand information transfer along the hippocampal pathways and its plasticity, here we studied the antidromic responses of the dentate gyrus (DG) and CA3 to activation of the mossy fibers and Schaffer collaterals, respectively, in hippocampal slices from naïve and epileptic rats. We applied trains of 600 electrical stimuli at functionally meaningful frequencies (θ, β/γ and γ). The responses of the DG to θ frequency trains underwent rapid potentiation that lasted about 400 stimuli, after which they progressively returned to control value. At β/γ and γ frequencies, however, the initial potentiation was followed by a strong frequency-dependent depression within the first 50 stimuli. In kindled animals, the initial potentiation was stronger than in control preparations and the resonant phase at θ frequency lasted longer. In contrast, CA3 responses were exponentially depressed at all frequencies, but depression was significantly less intense at θ frequency in epileptic preparations. Failure of fibers to fire action potentials could account for some of the aforementioned characteristics, but waveforms of the intracellular action potentials also changed as the field responses did, i.e., half-duration and time-to-peak increased in both structures along the stimulation trains. Noteworthy, block of glutamate and GABA ionotropic receptors prevented resonance and reduced the depression of antidromic responses to β/γ and γ stimulation recorded in the DG, but not in CA3. We show that the different behavior in the information transfer along these pathways depends on the frequency at which action potentials are generated, excitability history and anatomical features, including myelination and tortuosity. In addition, the mossy fibers are endowed with ionotropic receptors and terminal active properties conferring them their sui generis non-passive antidromic responses.

Quantification of Interictal Neuromagnetic Activity in Absence Epilepsy with Accumulated Source Imaging

Aberrant brain activity in childhood absence epilepsy (CAE) during seizures has been well recognized as synchronous 3 Hz spike-and-wave discharges on electroencephalography. However, brain activity from low- to very high-frequency ranges in subjects with CAE between seizures (interictal) has rarely been studied. Using a high-sampling rate magnetoencephalography (MEG) system, we studied ten subjects with clinically diagnosed but untreated CAE in comparison with age- and gender-matched controls. MEG data were recorded from all subjects during the resting state. MEG sources were assessed with accumulated source imaging, a new method optimized for localizing and quantifying spontaneous brain activity. MEG data were analyzed in nine frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-55 Hz), high-gamma (65-90 Hz), ripple (90-200 Hz), high-frequency oscillation (HFO, 200-1,000 Hz), and very high-frequency oscillation (VHFO, 1,000-2,000 Hz). MEG source imaging revealed that subjects with CAE had higher odds of interictal brain activity in 200-1,000 and 1,000-2,000 Hz in the parieto-occipito-temporal junction and the medial frontal cortices as compared with controls. The strength of the interictal brain activity in these regions was significantly elevated in the frequency bands of 90-200, 200-1,000 and 1,000-2,000 Hz for subjects with CAE as compared with controls. The results indicate that CAE has significantly aberrant brain activity between seizures that can be noninvasively detected. The measurements of high-frequency neuromagnetic oscillations may open a new window for investigating the cerebral mechanisms of interictal abnormalities in CAE.

On the nature of seizure dynamics

Seizures can occur spontaneously and in a recurrent manner, which defines epilepsy; or they can be induced in a normal brain under a variety of conditions in most neuronal networks and species from flies to humans. Such universality raises the possibility that invariant properties exist that characterize seizures under different physiological and pathological conditions. Here, we analysed seizure dynamics mathematically and established a taxonomy of seizures based on first principles. For the predominant seizure class we developed a generic model called Epileptor. As an experimental model system, we used ictal-like discharges induced in vitro in mouse hippocampi. We show that only five state variables linked by integral-differential equations are sufficient to describe the onset, time course and offset of ictal-like discharges as well as their recurrence. Two state variables are responsible for generating rapid discharges (fast time scale), two for spike and wave events (intermediate time scale) and one for the control of time course, including the alternation between 'normal' and ictal periods (slow time scale). We propose that normal and ictal activities coexist: a separatrix acts as a barrier (or seizure threshold) between these states. Seizure onset is reached upon the collision of normal brain trajectories with the separatrix. We show theoretically and experimentally how a system can be pushed toward seizure under a wide variety of conditions. Within our experimental model, the onset and offset of ictal-like discharges are well-defined mathematical events: a saddle-node and homoclinic bifurcation, respectively. These bifurcations necessitate a baseline shift at onset and a logarithmic scaling of interspike intervals at offset. These predictions were not only confirmed in our in vitro experiments, but also for focal seizures recorded in different syndromes, brain regions and species (humans and zebrafish). Finally, we identified several possible biophysical parameters contributing to the five state variables in our model system. We show that these parameters apply to specific experimental conditions and propose that there exists a wide array of possible biophysical mechanisms for seizure genesis, while preserving central invariant properties. Epileptor and the seizure taxonomy will guide future modeling and translational research by identifying universal rules governing the initiation and termination of seizures and predicting the conditions necessary for those transitions.

Temporal changes of neocortical high-frequency oscillations in epilepsy

High-frequency (100-500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient's entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

Gap junctions facilitate propagation of synchronous firing in the cortical neural population: a numerical simulation study

This study investigates the effect of gap junctions on firing propagation in a feedforward neural network by a numerical simulation with biologically plausible parameters. Gap junctions are electrical couplings between two cells connected by a binding protein, connexin. Recent electrophysiological studies have reported that a large number of inhibitory neurons in the mammalian cortex are mutually connected by gap junctions, and synchronization of gap junctions, spread over several hundred microns, suggests that these have a strong effect on the dynamics of the cortical network. However, the effect of gap junctions on firing propagation in cortical circuits has not been examined systematically. In this study, we perform numerical simulations using biologically plausible parameters to clarify this effect on population firing in a feedforward neural network. The results suggest that gap junctions switch the temporally uniform firing in a layer to temporally clustered firing in subsequent layers, resulting in an enhancement in the propagation of population firing in the feedforward network. Because gap junctions are often modulated in physiological conditions, we speculate that gap junctions could be related to a gating function of population firing in the brain.

Proceedings of the Third International Workshop on Advances in Electrocorticography

The Third International Workshop on Advances in Electrocorticography (ECoG) was convened in Washington, DC, on November 10-11, 2011. As in prior meetings, a true multidisciplinary fusion of clinicians, scientists, and engineers from many disciplines gathered to summarize contemporary experiences in brain surface recordings. The proceedings of this meeting serve as evidence of a very robust and transformative field but will yet again require revision to incorporate the advances that the following year will surely bring.

Signal distortion from microelectrodes in clinical EEG acquisition systems

Many centers are now using high-density microelectrodes during traditional intracranial electroencephalography (iEEG) both for research and clinical purposes. These microelectrodes are FDA-approved and integrate into clinical EEG acquisition systems. However, the electrical characteristics of these electrodes are poorly described and clinical systems were not designed to use them; thus, it is possible that this shift into clinical practice could have unintended consequences. In this study, we characterized the impedance of over 100 commercial macro- and microelectrodes using electrochemical impedance spectroscopy (EIS) to determine how electrode properties could affect signal acquisition and interpretation. The EIS data were combined with the published specifications of several commercial EEG systems to design digital filters that mimic the behavior of the electrodes and amplifiers. These filters were used to analyze simulated brain signals that contain a mixture of characteristic features commonly observed in iEEG. Each output was then processed with several common quantitative EEG measurements. Our results show that traditional macroelectrodes had low impedances and produced negligible distortion of the original signal. Brain tissue and electrical wiring also had negligible filtering effects. However, microelectrode impedances were much higher and more variable than the macroelectrodes. When connected to clinical amplifiers, higher impedance electrodes produced considerable distortion of the signal at low frequencies (<60 Hz), which caused significant changes in amplitude, phase, variance and spectral band power. In contrast, there were only minimal changes to the signal content for frequencies above 100 Hz. In order to minimize distortion with microelectrodes, we determined that an acquisition system should have an input impedance of at least 1 GΩ, which is much higher than most clinical systems. These results show that it is critical to account for variations in impedance when analyzing EEG from different-sized electrodes. Data from microelectrodes may yield misleading results unless recorded with high-impedance amplifiers.

What is the present-day EEG evidence for a preictal state?

EEG-based seizure prediction has undergone phases of optimism when analyses based on limited EEG samples suggested high sensitivity and specificity for several algorithms extracting features from raw preictal EEG data. When using long-term recordings, a more realistic view emerged which suggests that statistically significant predictions might be possible from surface and intracranial EEG, but no algorithm has yet demonstrated performance allowing for clinical application. Here, progress in EEG recording techniques, EEG analysis, and requirements for proper statistical validation of results are reported and discussed as they pertain to clinical implementation.