Increasing synchronization may promote seizure termination: Evidence from status epilepticus

Assessment of Fractal Synchronization during an Epileptic Seizure

In this paper, we define fractal synchronization (FS) based on the idea of stochastic synchronization and propose a mathematical apparatus for estimating FS. One major advantage of our proposed approach is that fractal synchronization makes it possible to estimate the aggregate strength of the connection on multiple time scales between two projections of the attractor, which are time series with a fractal structure. We believe that one of the promising uses of FS is the assessment of the interdependence of encephalograms. To demonstrate this approach in evaluating the cross-dependence between channels in a network of electroencephalograms, we evaluated the FS of encephalograms during an epileptic seizure. Fractal synchronization demonstrates the presence of desynchronization during an epileptic seizure.

Causal Connectivity Network Analysis of Ictal Electrocorticogram With Temporal Lobe Epilepsy Based on Dynamic Phase Transfer Entropy

Temporallobe epilepsy (TLE) has been conceptualized as a brain network disease, which generates brain connectivity dynamics within and beyond the temporal lobe structures in seizures. The hippocampus is a representative epileptogenic focus in TLE. Understanding the causal connectivity in terms of brain network during seizures is crucial in revealing the triggering mechanism of epileptic seizures originating from the hippocampus (HPC) spread to the lateral temporal cortex (LTC) by ictal electrocorticogram (ECoG), particularly in high-frequency oscillations (HFOs) bands. In this study, we proposed the unified-epoch dynamic causality analysis method to investigate the causal influence dynamics between two brain regions (HPC and LTC) at interictal and ictal phases in the frequency range of 1-500 Hz by introducing the phase transfer entropy (PTE) out/in-ratio and sliding window. We also proposed PTE-based machine learning algorithms to identify epileptogenic zone (EZ). Nine patients with a total of 26 seizures were included in this study. We hypothesized that: 1) HPC is the focus with the stronger causal connectivity than that in LTC in the ictal state at gamma and HFOs bands. 2) Causal connectivity in the ictal phase shows significant changes compared to that in the interictal phase. 3) The PTE out/in-ratio in the HFOs band can identify the EZ with the best prediction performance.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Electroconvulsive therapy for super refractory status epilepticus in pregnancy: case report and review of literature

OBJECTIVE

We aim to describe use of electroconvulsive therapy (ECT) to treat super refractory status epilepticus (SRSE) in pregnancy and review the literature regarding utility and safety of ECT in refractory status epilepticus.

BACKGROUND

Status epilepticus (SE) is a commonly encountered emergency in neuro-critical care world. Pharmacotherapy of status epilepticus in pregnancy is very challenging given the effect of the majority of antiepileptic drugs (AEDs) on fetal development. Although there has been growing evidence for use of ECT in status epilepticus, data about its utility in pregnancy is lacking.

DESIGN/METHOD

A twenty-one year old Caucasian female with history of epilepsy presented at 8 weeks of gestation as status epilepticus (SE) after abrupt discontinuation of her AEDs. Treatment was initiated with standard regimen of benzodiazepine and levetiracetam, which was progressively expanded to include approximately 10 anti-epileptic drugs over the course of 30 days. The status epilepticus was super refractory to sedation. She underwent ECT on day 31 with remarkable improvement in electroencephalogram (EEG) pattern and resolution of status epilepticus following a single ECT session. We reviewed PubMed and collated case reports involving the use of ECT in status epilepticus with emphasis on differences in various confounding factors esp. etiology of status and age group.

CONCLUSION

Our case is the first reported case of ECT for successful treatment of SRSE in pregnancy. While majority AEDs pose a significant maternal and fetal risk during pregnancy, ECT could be a potential frontline therapy for SE in pregnancy.

Functional network dynamics between the anterior thalamus and the cortex in deep brain stimulation for epilepsy

Owing to its unique connectivity profile with cortical brain regions, and its suggested role in the subcortical propagation of seizures, the anterior nucleus of the thalamus (ANT) has been proposed as a key deep brain stimulation (DBS) target in drug-resistant epilepsy. However, the spatio-temporal interaction dynamics of this brain structure, and the functional mechanisms underlying ANT DBS in epilepsy remain unknown. Here, we study how the ANT interacts with the neocortex in vivo in humans and provide a detailed neurofunctional characterization of mechanisms underlying the effectiveness of ANT DBS, aiming at defining intraoperative neural biomarkers of responsiveness to therapy, assessed at 6 months post-implantation as the reduction in seizure frequency. A cohort of 15 patients with drug-resistant epilepsy (n = 6 males, age = 41.6 ± 13.79 years) underwent bilateral ANT DBS implantation. Using intraoperative cortical and ANT simultaneous electrophysiological recordings, we found that the ANT is characterized by high amplitude θ (4-8 Hz) oscillations, mostly in its superior part. The strongest functional connectivity between the ANT and the scalp EEG was also found in the θ band in ipsilateral centro-frontal regions. Upon intraoperative stimulation in the ANT, we found a decrease in higher EEG frequencies (20-70 Hz) and a generalized increase in scalp-to-scalp connectivity. Crucially, we observed that responders to ANT DBS treatment were characterized by higher EEG θ oscillations, higher θ power in the ANT, and stronger ANT-to-scalp θ connectivity, highlighting the crucial role of θ oscillations in the dynamical network characterization of these structures. Our study provides a comprehensive characterization of the interaction dynamic between the ANT and the cortex, delivering crucial information to optimize and predict clinical DBS response in patients with drug-resistant epilepsy.

Intracortical functional connectivity dynamics induced by reflex seizures

Functional connectivity analysis is gaining more interest due to its promising clinical applications. To study network mechanisms underlying seizure termination and postictal depression, we explore dynamics of interhemispheric functional connectivity near the offset of focal and bilateral seizures in the experimental model of reflex audiogenic epilepsy. In the model, seizures and spreading depression are induced by sound stimulation of genetically predisposed rodents. We characterize temporal evolution of seizure-associated coupling dynamics in the frontoparietal cortex during late ictal, immediate postictal and interictal resting states, using two measures applied to local field potentials recorded in awake epileptic rats. Signals were analyzed with mean phase coherence index in delta (1-4 Hz), theta (4-10 Hz) beta (10-25 Hz) and gamma (25-50 Hz) frequency bands and mutual information function. The study shows that reflex seizures elicit highly dynamic changes in interhemispheric functional coupling with seizure-, region- and frequency-specific patterns of increased and decreased connectivity during late ictal and immediate postictal periods. Also, secondary generalization of recurrent seizures (kindling) is associated with pronounced alterations in resting-state functional connectivity - an early wideband decrease and a subsequent beta-gamma increase. The findings show that intracortical functional connectivity is dynamically modified in response to seizures on short and long timescales, suggesting the existence of activity-dependent plastic network alterations that may promote or prevent seizure propagation within the cortex and underlie postictal behavioral impairments.

Timing of cognitive effects on afterdischarge termination

OBJECTIVE

We previously studied efficacy of cognitive tasks on afterdischarge termination in patients undergoing cortical stimulation and found that diffuse wavelet cross-coherence changes on electrocorticography were associated with termination efficacy. We now report wavelet cross-coherence findings during different time segments of trials during which afterdischarges ended.

METHODS

For 12 patients with implanted subdural electrodes, we compared wavelet cross-coherence findings among several 1-second portions of cognitive tasks, reflecting task presentation, patient replies, and afterdischarge termination.

RESULTS

Coherence decreased significantly and progressively over time for 16.89, 22.53, and 30.03 Hz frequency ranges, but increased with afterdischarge termination. Coherence first increased, and then decreased for the 7.13 Hz frequency range.

CONCLUSIONS

The findings suggest that cumulative but non-specific factors, likely related primarily to attention, influence the coherence results throughout the task, with a separate effect due to resolution of the afterdischarges at the end.

SIGNIFICANCE

Task performance is well known to localize to specific brain regions and to be restricted in timing. In contrast, attention and overall mental activation might be due to emergent properties of brain as a whole and that are less circumscribed in space or time. Cognitive tasks might modify seizures and other neurological disorders.

High expectations on phase locking: Better quantifying the concentration of circular data

The degree to which unimodal circular data are concentrated around the mean direction can be quantified using the mean resultant length, a measure known under many alternative names, such as the phase locking value or the Kuramoto order parameter. For maximal concentration, achieved when all of the data take the same value, the mean resultant length attains its upper bound of one. However, for a random sample drawn from the circular uniform distribution, the expected value of the mean resultant length achieves its lower bound of zero only as the sample size tends to infinity. Moreover, as the expected value of the mean resultant length depends on the sample size, bias is induced when comparing the mean resultant lengths of samples of different sizes. In order to ameliorate this problem, here, we introduce a re-normalized version of the mean resultant length. Regardless of the sample size, the re-normalized measure has an expected value that is essentially zero for a random sample from the circular uniform distribution, takes intermediate values for partially concentrated unimodal data, and attains its upper bound of one for maximal concentration. The re-normalized measure retains the simplicity of the original mean resultant length and is, therefore, easy to implement and compute. We illustrate the relevance and effectiveness of the proposed re-normalized measure for mathematical models and electroencephalographic recordings of an epileptic seizure.

Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states

Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterized the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states. We then compared seizure network state occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. In most patients, the occurrence or duration of at least one seizure network state was associated with the time since implantation. Some patients had one or more seizure network states that were associated with phases of circadian and/or multidien spike rate cycles. A given seizure network state's occurrence and duration were usually not associated with the same timescale. Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a seizure network state's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures and similar principles likely apply to other neurological conditions.

Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures

In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks-in which edges indicate strong enough coupling between brain regions-are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy.

Interictal-period-activated neuronal ensemble in piriform cortex retards further seizure development

Epileptic networks are characterized as having two states, seizures or more prolonged interictal periods. However, cellular mechanisms underlying the contribution of interictal periods to ictal events remain unclear. Here, we use an activity-dependent labeling technique combined with genetically encoded effectors to characterize and manipulate neuronal ensembles recruited by focal seizures (FS-Ens) and interictal periods (IP-Ens) in piriform cortex, a region that plays a key role in seizure generation. Ca²⁺ activities and histological evidence reveal a disjointed correlation between the two ensembles during FS dynamics. Optogenetic activation of FS-Ens promotes further seizure development, while IP-Ens protects against it. Interestingly, both ensembles are functionally involved in generalized seizures (GS) due to circuit rearrangement. IP-Ens bidirectionally modulates FS but not GS by controlling coherence with hippocampus. This study indicates that the interictal state may represent a seizure-preventing environment, and the interictal-activated ensemble may serve as a potential therapeutic target for epilepsy.

Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study

Objective: Our aim was to investigate the dynamics of functional connectivity (FC) networks during seizure termination in patients with childhood absence epilepsy (CAE) using magnetoencephalography (MEG) and graph theory (GT) analysis.

Methods: MEG data were recorded from 22 drug-naïve patients diagnosed with CAE. FC analysis was performed to evaluate the FC networks in seven frequency bands of the MEG data. GT analysis was used to assess the topological properties of FC networks in different frequency bands.

Results: The patterns of FC networks involving the frontal cortex were altered significantly during seizure termination compared with those during the ictal period. Changes in the topological parameters of FC networks were observed in specific frequency bands during seizure termination compared with those in the ictal period. In addition, the connectivity strength at 250–500 Hz during the ictal period was negatively correlated with seizure frequency.

Conclusions: FC networks associated with the frontal cortex were involved in the termination of absence seizures. The topological properties of FC networks in different frequency bands could be used as new biomarkers to characterize the dynamics of FC networks related to seizure termination.

Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series

The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a set of simulation models with distinct characteristics, to assess how well they work in- as well as outside of their “comfort zone.” PDC and DTF perform best on systems with frequency-specific connections, while PMIME is the only one able to detect non-linear interactions. The varying performance depending on the system characteristics warrants the use of multiple measures and comparing their results to avoid errors. Furthermore, lags between coupled variables are inherent to real-world systems and could hold essential information on the network dynamics. They are however often not taken into account and we lack proper tools to estimate them. We propose three new methods for lag estimation in multivariate time series, based on autoregressive modelling and information theory. One of the autoregressive methods and the one based on information theory were able to reliably identify the correct lag value in different simulated systems. However, only the latter was able to maintain its performance in the case of non-linear interactions. As a clinical application, the same methods are also applied on an intracranial recording of an epileptic seizure. The combined knowledge from the causality measures and insights from the simulations, on how these measures perform under different circumstances and when to use which one, allow us to recreate a plausible network of the seizure propagation that supports previous observations of desynchronisation and synchronisation during seizure progression. The lag estimation results show absence of a relationship between connectivity strength and estimated lag values, which contradicts the line of thinking in connectivity shaped by the neuron doctrine.

Partial synchronization in the second-order Kuramoto model: An auxiliary system method

Partial synchronization emerges in an oscillator network when the network splits into clusters of coherent and incoherent oscillators. Here, we analyze the stability of partial synchronization in the second-order finite-dimensional Kuramoto model of heterogeneous oscillators with inertia. Toward this goal, we develop an auxiliary system method that is based on the analysis of a two-dimensional piecewise-smooth system whose trajectories govern oscillating dynamics of phase differences between oscillators in the coherent cluster. Through a qualitative bifurcation analysis of the auxiliary system, we derive explicit bounds that relate the maximum natural frequency mismatch, inertia, and the network size that can support stable partial synchronization. In particular, we predict threshold-like stability loss of partial synchronization caused by increasing inertia. Our auxiliary system method is potentially applicable to cluster synchronization with multiple coherent clusters and more complex network topology.

Intracranial EEG seizure onset and termination patterns and their association

OBJECTIVE

The study of seizure onset and termination patterns has the potential to enhance our understanding of the underlying mechanisms of seizure generation and cessation. It is largely unclear whether seizures with distinct onset patterns originate from varying network interactions and terminate through different termination pathways.

METHODS

We investigated the morphology and location of 103 intracranial EEG seizure onset and termination patterns from 20 patients with drug-resistant focal epilepsy. We determined if seizure onset patterns were associated with specific termination patterns. Finally, we looked at network interactions prior to the generation of distinct seizure onset patterns by calculating directed functional connectivity matrices.

RESULTS

We identified nine seizure onset and six seizure termination patterns. The most common onset pattern was Low-Voltage Fast Activity (36 %), and the most frequent termination pattern was Burst Suppression (44 %). All seizures with fast (>13 Hz) termination patterns had a fast (>13 Hz) onset pattern type. Almost any onset pattern could terminate with the Burst Suppression and rhythmic Spike/PolySpike and Wave (rSW/rPSW) termination patterns. Seizures with a fast activity onset had higher inflow to the seizure onset zone from other regions in the gamma and high gamma frequency ranges prior to their generation compared to seizures with a slow onset.

SIGNIFICANCE

Our observations suggest that different mechanisms underlie the generation of different seizure onset patterns although seizure onset patterns can share a common termination pattern. Possible mechanisms underlying these patterns are discussed.

Large-Scale Desynchronization During Interictal Epileptic Discharges Recorded With Intracranial EEG

It is increasingly recognized that deep understanding of epileptic seizures requires both localizing and characterizing the functional network of the region where they are initiated, i. e., the epileptic focus. Previous investigations of the epileptogenic focus' functional connectivity have yielded contrasting results, reporting both pathological increases and decreases during resting periods and seizures. In this study, we shifted paradigm to investigate the time course of connectivity in relation to interictal epileptiform discharges. We recruited 35 epileptic patients undergoing intracranial EEG (iEEG) investigation as part of their presurgical evaluation. For each patient, 50 interictal epileptic discharges (IEDs) were marked and iEEG signals were epoched around those markers. Signals were narrow-band filtered and time resolved phase-locking values were computed to track the dynamics of functional connectivity during IEDs. Results show that IEDs are associated with a transient decrease in global functional connectivity, time-locked to the peak of the discharge and specific to the high range of the gamma frequency band. Disruption of the long-range connectivity between the epileptic focus and other brain areas might be an important process for the generation of epileptic activity. Transient desynchronization could be a potential biomarker of the epileptogenic focus since 1) the functional connectivity involving the focus decreases significantly more than the connectivity outside the focus and 2) patients with good surgical outcome appear to have a significantly more disconnected focus than patients with bad outcomes.

The thalamic midline nucleus reuniens: potential relevance for schizophrenia and epilepsy

Anatomical, electrophysiological and behavioral studies in rodents have shown that the thalamic midline nucleus reuniens (RE) is a crucial link in the communication between hippocampal formation (HIP, i.e., CA1, subiculum) and medial prefrontal cortex (mPFC), important structures for cognitive and executive functions. A common feature in neurodevelopmental and neurodegenerative brain diseases is a dysfunctional connectivity/communication between HIP and mPFC, and disturbances in the cognitive domain. Therefore, it is assumed that aberrant functioning of RE may contribute to behavioral/cognitive impairments in brain diseases characterized by cortico-thalamo-hippocampal circuit dysfunctions. In the human brain the connections of RE are largely unknown. Yet, recent studies have found important similarities in the functional connectivity of HIP-mPFC-RE in humans and rodents, making cautious extrapolating experimental findings from animal models to humans justifiable. The focus of this review is on a potential involvement of RE in schizophrenia and epilepsy.

When three is a crowd: Chaos from clusters of Kuramoto oscillators with inertia

Modeling cooperative dynamics using networks of phase oscillators is common practice for a wide spectrum of biological and technological networks, ranging from neuronal populations to power grids. In this paper we study the emergence of stable clusters of synchrony with complex intercluster dynamics in a three-population network of identical Kuramoto oscillators with inertia. The populations have different sizes and can split into clusters where the oscillators synchronize within a cluster, but notably, there is a phase shift between the dynamics of the clusters. We extend our previous results on the bistability of synchronized clusters in a two-population network [I. V. Belykh et al., Chaos 26, 094822 (2016)CHAOEH1054-150010.1063/1.4961435] and demonstrate that the addition of a third population can induce chaotic intercluster dynamics. This effect can be captured by the old adage "two is company, three is a crowd," which suggests that the delicate dynamics of a romantic relationship may be destabilized by the addition of a third party, leading to chaos. Through rigorous analysis and numerics, we demonstrate that the intercluster phase shifts can stably coexist and exhibit different forms of chaotic behavior, including oscillatory, rotatory, and mixed-mode oscillations. We also discuss the implications of our stability results for predicting the emergence of chimeras and solitary states.

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy

Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.

Synchronizability of directed networks: The power of non-existent ties

The understanding of how synchronization in directed networks is influenced by structural changes in network topology is far from complete. While the addition of an edge always promotes synchronization in a wide class of undirected networks, this addition may impede synchronization in directed networks. In this paper, we develop the augmented graph stability method, which allows for explicitly connecting the stability of synchronization to changes in network topology. The transformation of a directed network into a symmetrized-and-augmented undirected network is the central component of this new method. This transformation is executed by symmetrizing and weighting the underlying connection graph and adding new undirected edges with consideration made for the mean degree imbalance of each pair of nodes. These new edges represent "non-existent ties" in the original directed network and often control the location of critical nodes whose directed connections can be altered to manipulate the stability of synchronization in a desired way. In particular, we show that the addition of small-world shortcuts to directed networks, which makes "non-existent ties" disappear, can worsen the synchronizability, thereby revealing a destructive role of small-world connections in directed networks. An extension of our method may open the door to studying synchronization in directed multilayer networks, which cannot be effectively handled by the eigenvalue-based methods.

A model for focal seizure onset, propagation, evolution, and progression

We developed a neural network model that can account for major elements common to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating usage-dependent exhaustion of inhibition in an adaptive neural network that receives global feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi-stable seizure end-points arise when stochastic noise is included. With the rapid advancement of clinical and experimental tools, we believe that this model can provide a roadmap and potentially an in silico testbed for future explorations of seizure mechanisms and clinical therapies.

Closed-Loop Control of Absence Seizures Inspired by Feedback Modulation of Basal Ganglia to the Corticothalamic Circuit

Basal ganglia (BG) has been demonstrated to play the role of modulation for absence seizure generated in the corticothalamic (CT) circuit. But it is unknown what the principle of modulation is and how to improve the modulation if BG fails to hold back the absence seizures. Although neurostimulation has been surgically employed to improve the clinical symptom of patients with epilepsy, the mechanism underlying the neurostimulation regulation is still unclear. In addition, it is not clear what sort of the spatiotemporal patterned stimulation protocols can effectively abate absence seizures with less side effect and energy consumption. Here, we address these issues on the previously proposed BG-CT model. In particular, we develop a reduced corticothalamic (RCT) moldel by viewing BG as a 2I:3O feedback modulator. By calculating the mean firing rate (MFR) and triggering mean firing rate (TMFR), we find that absence seizures can be induced or abated using the neurostimulations through driving the MFRs of the related neurons to fall into or be kicked out of the regions bounded by the TMFRs. In particular, closed-loop m:n ON-OFF anodic-cathodic-cathodic (ACC) triphase coordinated resetting stimulation (CRS) applied on the CT circuit and designed with the TMFR of subthalamic nucleus (STN) in BG could achieve the satisfying abatement effects of absence seizures with the least current consumption.

Inhibitory Network Bistability Explains Increased Interneuronal Activity Prior to Seizure Onset

Recent experimental literature has revealed that GABAergic interneurons exhibit increased activity prior to seizure onset, alongside additional evidence that such activity is synchronous and may arise abruptly. These findings have led some to hypothesize that this interneuronal activity may serve a causal role in driving the sudden change in brain activity that heralds seizure onset. However, the mechanisms predisposing an inhibitory network toward increased activity, specifically prior to ictogenesis, without a permanent change to inputs to the system remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyperexcitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony driven by a brief perturbation. This transition into synchrony brings about a corresponding increase in overall firing rate. We further show that perturbations driving this transition could arise in vivo from background excitatory synaptic activity in the cortex. Thus, we propose that bistability explains the increase in interneuron activity observed experimentally prior to seizure via a transition from incoherent to coherent dynamics. Moreover, bistability explains why inhibitory networks containing hyperexcitable interneurons are more vulnerable to this change in dynamics, and how such networks can undergo a transition without a permanent change in the drive. We note that while our comparisons are between networks of control and ictogenic neurons, the conclusions drawn specifically relate to the unusual dynamics that arise prior to seizure, and not seizure onset itself. However, providing a mechanistic explanation for this phenomenon specifically in a pro-ictogenic setting generates experimentally testable hypotheses regarding the role of inhibitory neurons in pre-ictal neural dynamics, and motivates further computational research into mechanisms underlying a newly hypothesized multi-step pathway to seizure initiated by inhibition.

Aberrant Connectivity During Pilocarpine-Induced Status Epilepticus

Status epilepticus (SE) is a common, life-threatening neurological disorder that may lead to permanent brain damage. In rodent models, SE is an acute phase of seizures that could be reproduced by injecting with pilocarpine and then induce chronic temporal lobe epilepsy (TLE) seizures. However, how SE disrupts brain activity, especially communications among brain regions, is still unclear. In this study, we aimed to identify the characteristic abnormalities of network connections among the frontal cortex, hippocampus and thalamus during the SE episodes in a pilocarpine model with functional and effective connectivity measurements. We showed that the coherence connectivity among these regions increased significantly during the SE episodes in almost all frequency bands (except the alpha band) and that the frequency band with enhanced connections was specific to different stages of SE episodes. Moreover, with the effective analysis, we revealed a closed neural circuit of bidirectional effective interactions between the frontal regions and the hippocampus and thalamus in both ictal and post-ictal stages, implying aberrant enhancement of communication across these brain regions during the SE episodes. Furthermore, an effective connection from the hippocampus to the thalamus was detected in the delta band during the pre-ictal stage, which shifted in an inverse direction during the ictal stage in the theta band and in the theta, alpha, beta and low-gamma bands during the post-ictal stage. This specificity of the effective connection between the hippocampus and thalamus illustrated that the hippocampal structure is critical for the initiation of SE discharges, while the thalamus is important for the propagation of SE discharges. Overall, our results demonstrated enhanced interaction among the frontal cortex, hippocampus and thalamus during the SE episodes and suggested the modes of information flow across these structures for the initiation and propagation of SE discharges. These findings may reveal an underlying mechanism of aberrant network communication during pilocarpine-induced SE discharges and deepen our knowledge of TLE seizures.

Seizure prediction - ready for a new era

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

Dynamic Neuromagnetic Network Changes of Seizure Termination in Absence Epilepsy: A Magnetoencephalography Study

Objective: With increasing efforts devoted to investigating the generation and propagation mechanisms of spontaneous spike and wave discharges (SWDs), little attention has been paid to network mechanisms associated with termination patterns of SWDs to date. In the current study, we aimed to identify the frequency-dependent neural network dynamics during the offset of absence seizures.

Methods: Fifteen drug-naïve patients with childhood absence epilepsy (CAE) were assessed with a 275-Channel Magnetoencephalography (MEG) system. MEG data were recorded during and between seizures at a sampling rate of 6,000 Hz and analyzed in seven frequency bands. Source localization was performed with accumulated source imaging. Granger causality analysis was used to evaluate effective connectivity networks of the entire brain at the source level.

Results: At the low-frequency (1–80 Hz) bands, activities were predominantly distributed in the frontal cortical and parieto–occipito–temporal junction at the offset transition periods. The high-frequency oscillations (HFOs, 80–500 Hz) analysis indicated significant source localization in the medial frontal cortex and deep brain areas (mainly thalamus) during both the termination transition and interictal periods. Furthermore, an enhanced positive cortico–thalamic effective connectivity was observed around the discharge offset at all of the seven analyzed bands, the direction of which was primarily from various cortical regions to the thalamus.

Conclusions: Seizure termination is a gradual process that involves both the cortices and the thalamus in CAE. Cortico–thalamic coupling is observed at the termination transition periods, and the cerebral cortex acts as the driving force.

Thalamocortical dysrhythmia in intraoperative recordings of focal epilepsy

Resonant interactions between the thalamus and cortex subserve a critical role for maintenance of consciousness as well as cognitive functions. In states of abnormal thalamic inhibition, thalamocortical dysrhythmia (TCD) has been described. The characteristics of TCD include a slowing of resting oscillations, ectopic high-frequency activity, and increased cross-frequency coupling. Here, we demonstrate the presence of TCD in four patients who underwent resective epilepsy surgery with chronically implanted electrodes under anesthesia, continuously recording activity from brain regions at the periphery of the epileptogenic zone before and after resection. Following resection, we report an acceleration of the large-scale network resting frequency coincident with decreases in cross-frequency phase-amplitude coupling. Interregional functional connectivity in the surrounding cortex was also increased following resection of the epileptogenic focus. These findings provide evidence for the presence of TCD in focal epilepsy and highlight the importance of reciprocal thalamocortical oscillatory interactions in defining novel biomarkers for resective surgeries.

NEW & NOTEWORTHY Thalamocortical dysrhythmia (TCD) occurs in the context of thalamic dysfacilitation and is characterized by slowing of resting oscillations, ectopic high-frequency activity, and cross-frequency coupling. We provide evidence for TCD in focal epilepsy by studying electrophysiological changes occurring at the periphery of the resection margin. We report acceleration of resting activity coincident with decreased cross-frequency coupling and increased functional connectivity. The study of TCD in epilepsy has implications as a biomarker and therapeutic target.

Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients

Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1-600 Hz), ripple (80-250 Hz), and fast-ripple (250-600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase toward seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

Phase Synchronization Dynamics of Neural Network during Seizures

Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods. Those results indicated that synchronization might participate in termination of seizures, and the network states transitions might not dominate the seizure evolution.

Collective excitability in a mesoscopic neuronal model of epileptic activity

At the mesoscopic scale, the brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we address this issue in a simplified situation by examining the effect of coupling between two cortical columns described via Jansen-Rit neural mass models. Our results show that coupling between the two neuronal populations gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events. For large enough coupling strengths, termination of these events results mainly from the emergence of synchronization between the columns, and thus it is controlled by coupling instead of noise. Stochastic triggering and noise-independent durations are characteristic of excitable dynamics, and thus we interpret our results in terms of collective excitability.

A procedure to increase the power of Granger-causal analysis through temporal smoothing

BACKGROUND

How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics.

NEW METHOD

We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model.

RESULTS

We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy.

COMPARISON WITH EXISTING METHOD

The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies.

CONCLUSIONS

The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.

A Computational Model of Deep-Brain Stimulation for Acquired Dystonia in Children

The mechanism by which deep brain stimulation (DBS) improves dystonia is not understood, partly heterogeneity of the underlying disorders leads to differing effects of stimulation in different locations. Similarity between the effects of DBS and the effects of lesions has led to biophysical models of blockade or reduced transmission of involuntary activity in individual cells in the pathways responsible for dystonia. Here, we expand these theories by modeling the effect of DBS on populations of neurons. We emphasize the important observation that the DBS signal itself causes surprisingly few side effects and does not normally appear in the electromyographic signal. We hypothesize that, at the population level, massively synchronous rhythmic firing caused by DBS is only poorly transmitted through downstream populations. However, the high frequency of stimulation overwhelms incoming dystonic activity, thereby substituting an ineffectively transmitted exogenous signal for the endogenous abnormal signal. Changes in sensitivity can occur not only at the site of stimulation, but also at downstream sites due to synaptic and homeostatic plasticity mechanisms. The mechanism is predicted to depend strongly on the stimulation frequency. We provide preliminary data from simultaneous multichannel recordings in basal ganglia and thalamus in children with secondary dystonia. We also provide illustrative simulations of the effect of stimulation frequency on the transmission of the DBS pulses through sequential populations of neurons in the dystonia pathway. Our experimental results and model provide a new hypothesis and computational framework consistent with the clinical features of DBS in childhood acquired dystonia.

Finite-Time State Estimation for Recurrent Delayed Neural Networks With Component-Based Event-Triggering Protocol

This paper deals with the event-based finite-time state estimation problem for a class of discrete-time stochastic neural networks with mixed discrete and distributed time delays. In order to mitigate the burden of data communication, a general component-based event-triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certain time-point according to a specific triggering condition. A new concept of finite-time boundedness in the mean square is put forward to quantify the estimation performance by introducing a settling-like time function. The objective of the addressed problem is to construct an event-based state estimator to estimate the neuron states such that, in the presence of both mixed time delays and external noise disturbances, the dynamics of the estimation error is finite-time bounded in the mean square with a prescribed error upper bound. Sufficient conditions are established, via stochastic analysis techniques, to guarantee the desired estimation performance. By solving an optimization problem with some inequality constraints, the explicit expression of the estimator gain matrix is characterized to minimize the settling-like time. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design scheme.

Predictability and Resetting in a Case of Convulsive Status Epilepticus

In this case study, we present evidence of resetting of brain dynamics following convulsive status epilepticus (SE) that was treated successfully with antiepileptic medications (AEDs). The measure of effective inflow (EI), a novel measure of network connectivity, was applied to the continuously recorded multichannel intracranial stereoelectroencephalographic (SEEG) signals before, during and after SE. Results from this analysis indicate trends of progressive reduction of EI over hours up to the onset of SE, mainly at sites of the epileptogenic focus with reversal of those trends upon successful treatment of SE by AEDs. The proposed analytical framework is promising for elucidation of the pathology of neuronal network dynamics that could lead to SE, evaluation of the efficacy of SE treatment strategies, as well as the development of biomarkers for susceptibility to SE.

Dynamics of Epileptiform Discharges Induced by Transcranial Magnetic Stimulation in Genetic Generalized Epilepsy

Objective: In patients with Genetic Generalized Epilepsy (GGE), transcranial magnetic stimulation (TMS) can induce epileptiform discharges (EDs) of varying duration. We hypothesized that (a) the ED duration is determined by the dynamic states of critical network nodes (brain areas) at the early post-TMS period, and (b) brain connectivity changes before, during and after the ED, as well as within the ED. Methods: EEG recordings from two GGE patients were analyzed. For hypothesis (a), the characteristics of the brain dynamics at the early ED stage are measured with univariate and multivariate EEG measures and the dependence of the ED duration on these measures is evaluated. For hypothesis (b), effective connectivity measures are combined with network indices so as to quantify the brain network characteristics and identify changes in brain connectivity. Results: A number of measures combined with specific channels computed on the first EEG segment post-TMS correlate with the ED duration. In addition, brain connectivity is altered from pre-ED to ED and post-ED and statistically significant changes were also detected across stages within the ED. Conclusion: ED duration is not purely stochastic, but depends on the dynamics of the post-TMS brain state. The brain network dynamics is significantly altered in the course of EDs.

When two wrongs make a right: synchronized neuronal bursting from combined electrical and inhibitory coupling

Synchronized cortical activities in the central nervous systems of mammals are crucial for sensory perception, coordination and locomotory function. The neuronal mechanisms that generate synchronous synaptic inputs in the neocortex are far from being fully understood. In this paper, we study the emergence of synchronization in networks of bursting neurons as a highly non-trivial, combined effect of electrical and inhibitory connections. We report a counterintuitive find that combined electrical and inhibitory coupling can synergistically induce robust synchronization in a range of parameters where electrical coupling alone promotes anti-phase spiking and inhibition induces anti-phase bursting. We reveal the underlying mechanism, which uses a balance between hidden properties of electrical and inhibitory coupling to act together to synchronize neuronal bursting. We show that this balance is controlled by the duty cycle of the self-coupled system which governs the synchronized bursting rhythm.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Optogenetic Low-Frequency Stimulation of Specific Neuronal Populations Abates Ictogenesis

Despite many advances made in understanding the pathophysiology of epileptic disorders, seizures remain poorly controlled in approximately one-third of patients with mesial temporal lobe epilepsy. Here, we established the efficacy of cell type-specific low-frequency stimulation (LFS) in controlling ictogenesis in the mouse entorhinal cortex (EC) in an in vitro brain slice preparation. Specifically, we used 1 Hz optogenetic stimulation of calcium/calmodulin-dependent protein kinase II-positive principal cells as well as of parvalbumin- or somatostatin-positive interneurons to study the effects of such repetitive activation on epileptiform discharges induced by 4-aminopyridine. We found that 1 Hz stimulation of any of these cell types reduced the frequency and duration of ictal discharges in some trials, while completely blocking them in others. The field responses evoked by the stimulation of each cell type revealed that their duration and amplitude were higher when principal cells were targeted. Furthermore, following a short period of silence ranging from 67 to 135 s, ictal discharges were re-established with similar duration and frequency as before stimulation; however, this period of silence was longer following principal cell stimulation compared with parvalbumin- or somatostatin-positive interneuron stimulation. Our results show that LFS of either excitatory or inhibitory cell networks in EC are effective in controlling ictogenesis. Although optogenetic stimulation of either cell type significantly reduced the occurrence of ictal discharges, principal cell stimulation resulted in a more prolonged suppression of ictogenesis, and, thus, it may constitute a better approach for controlling seizures.SIGNIFICANCE STATEMENT Epilepsy is a neurological disorder characterized by an imbalance between excitation and inhibition leading to seizures. Many epileptic patients do not achieve adequate seizure control using antiepileptic drugs. Low-frequency stimulation (LFS) is an alternative tool for controlling epileptiform activity in these patients. However, despite the temporal and spatial control offered by LFS, such a procedure lacks cell specificity, which may limit its efficacy. Using an optogenetic approach, we report here that LFS of two interneuron subtypes and, even more so, of principal cells can reliably shorten or abolish seizures in vitro Our work suggests that targeted LFS may constitute a reliable means for controlling seizures in patients presenting with focal seizures.

Pathophysiology of status epilepticus

Status epilepticus (SE) is the maximal expression of epilepsy with a high morbidity and mortality. It occurs due to the failure of mechanisms that terminate seizures. Both human and animal data indicate that the longer a seizure lasts, the less likely it is to stop. Recent evidence suggests that there is a critical transition from an ictal to a post-ictal state, associated with a transition from a spatio-temporally desynchronized state to a highly synchronized state, respectively. As SE continues, it becomes progressively resistant to drugs, in particular benzodiazepines due partly to NMDA receptor-dependent internalization of GABA(A) receptors. Moreover, excessive calcium entry into neurons through excessive NMDA receptor activation results in activation of nitric oxide synthase, calpains, and NADPH oxidase. The latter enzyme plays a critical part in the generation of seizure-dependent reactive oxygen species. Calcium also accumulates in mitochondria resulting in mitochondrial failure (decreased ATP production), and opening of the mitochondrial permeability transition pore. Together these changes result in status epilepticus-dependent neuronal death via several pathways. Multiple downstream mechanisms including inflammation, break down of the blood-brain barrier, and changes in gene expression can contribute to later pathological processes including chronic epilepsy and cognitive decline.

Reactivation of seizure-related changes to interictal spike shape and synchrony during postseizure sleep in patients

OBJECTIVE

Local field potentials (LFPs) arise from synchronous activation of millions of neurons, producing seemingly consistent waveform shapes and relative synchrony across electrodes. Interictal spikes (IISs) are LFPs associated with epilepsy that are commonly used to guide surgical resection. Recently, changes in neuronal firing patterns observed in the minutes preceding seizure onset were found to be reactivated during postseizure sleep, a process called seizure-related consolidation (SRC), due to similarities with learning-related consolidation. Because IISs arise from summed neural activity, we hypothesized that changes in IIS shape and relative synchrony would be observed in the minutes preceding seizure onset and would be reactivated preferentially during postseizure slow-wave sleep (SWS).

METHODS

Scalp and intracranial recordings were obtained continuously across multiple days from clinical macroelectrodes implanted in patients undergoing treatment for intractable epilepsy. Data from scalp electrodes were used to stage sleep. Data from intracranial electrodes were used to detect IISs using a previously established algorithm. Partial correlations were computed for sleep and wake periods before and after seizures as a function of correlations observed in the minutes preceding seizures. Magnetic resonance imaging (MRI) and computed tomography (CT) scans were co-registered with electroencephalography (EEG) to determine the location of the seizure-onset zone (SOZ).

RESULTS

Changes in IIS shape and relative synchrony were observed on a subset of macroelectrodes minutes before seizure onset, and these changes were reactivated preferentially during postseizure SWS. Changes in synchrony were greatest for pairs of electrodes where at least one electrode was located in the SOZ.

SIGNIFICANCE

These data suggest preseizure changes in neural activity and their subsequent reactivation occur across a broad spatiotemporal scale: from single neurons to LFPs, both within and outside the SOZ. The preferential reactivation of seizure-related changes in IISs during postseizure SWS adds to a growing body of literature suggesting that pathologic neural processes may utilize physiologic mechanisms of synaptic plasticity.

Interaction between Thalamus and Hippocampus in Termination of Amygdala-Kindled Seizures in Mice

The thalamus and hippocampus have been found both involved in the initiation, propagation, and termination of temporal lobe epilepsy. However, the interaction of these regions during seizures is not clear. The present study is to explore whether some regular patterns exist in their interaction during the termination of seizures. Multichannel in vivo recording techniques were used to record the neural activities from the cornu ammonis 1 (CA1) of hippocampus and mediodorsal thalamus (MDT) in mice. The mice were kindled by electrically stimulating basolateral amygdala neurons, and Racine's rank standard was employed to classify the stage of behavioral responses (stage 1~5). The coupling index and directionality index were used to investigate the synchronization and information flow direction between CA1 and MDT. Two main results were found in this study. (1) High levels of synchronization between the thalamus and hippocampus were observed before the termination of seizures at stage 4~5 but after the termination of seizures at stage 1~2. (2) In the end of seizures at stage 4~5, the information tended to flow from MDT to CA1. Those results indicate that the synchronization and information flow direction between the thalamus and the hippocampus may participate in the termination of seizures.

Which Brain Regions are Important for Seizure Dynamics in Epileptic Networks? Influence of Link Identification and EEG Recording Montage on Node Centralities

Nodes in large-scale epileptic networks that are crucial for seizure facilitation and termination can be regarded as potential targets for individualized focal therapies. Graph-theoretical approaches based on centrality concepts can help to identify such important nodes, however, they may be influenced by the way networks are derived from empirical data. Here we investigate evolving functional epileptic brain networks during 82 focal seizures with different anatomical onset locations that we derive from multichannel intracranial electroencephalographic recordings from 51 patients. We demonstrate how the various methodological steps (from the recording montage via node and link inference to the assessment of node centralities) affect importance estimation and discuss their impact on the interpretability of findings in the context of pathophysiological aspects of seizure dynamics.

Connectivity of epileptic brain regions in wake and sleep

Focal epileptic brain is characterized by a region of pathological tissue seizure onset zone (SOZ) - the pathologic tissue generating seizures. During the interictal period (nonseizure) the SOZ is characterized by epileptiform activity - interictal spikes & high-frequency oscillations (HFO). The SOZ also exhibits hyper-synchrony and functional disconnection from the surrounding areas. Recent studies have described the synchrony inside the SOZ and surrounding tissue for just small sets of patients (2-4) and without any distinction in behavioral states. Wake and sleep cycles can, however, have a significant influence on SOZ activity. Here we show the results of connectivity analysis in three fundamental areas of the epileptic brain - inside SOZ, outside SOZ and bridging areas in 7 patients during wake and sleep. We observed increased synchrony inside SOZ and decreased synchrony on its edges (bridging areas) in specific frequency bands. We also detected significant differences of synchrony levels between wake and sleep periods in HFO frequencies. Our results provide additional insight into the properties of SOZ connectivity. Knowledge of these principles may prove useful for SOZ localization and understanding epileptic brain function in general.