Evidence for consolidation of neuronal assemblies after seizures in humans

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

Review: seizure-related consolidation and the network theory of epilepsy

Epilepsy is a complex, multifaceted disease that affects patients in several ways in addition to seizures, including psychological, social, and quality of life issues, but epilepsy is also known to interact with sleep. Seizures often occur at the boundary between sleep and wake, patients with epilepsy often experience disrupted sleep, and the rate of inter-ictal epileptiform discharges increases during non-REM sleep. The Network Theory of Epilepsy did not address a role for sleep, but recent emphasis on the interaction between epilepsy and sleep suggests that post-seizure sleep may also be involved in the process by which seizures arise and become more severe with time ("epileptogenesis") by co-opting processes related to the formation of long-term memories. While it is generally acknowledged that recurrent seizures arise from the aberrant function of neural circuits, it is possible that the progression of epilepsy is aided by normal, physiological function of neural circuits during sleep that are driven by pathological signals. Studies recording multiple, single neurons prior to spontaneous seizures have shown that neural assemblies activated prior to the start of seizures were reactivated during post-seizure sleep, similar to the reactivation of behavioral neural assemblies, which is thought to be involved in the formation of long-term memories, a process known as Memory Consolidation. The reactivation of seizure-related neural assemblies during sleep was thus described as being a component of Seizure-Related Consolidation (SRC). These results further suggest that SRC may viewed as a network-related aspect of epilepsy, even in those seizures that have anatomically restricted neuroanatomical origins. As suggested by the Network Theory of Epilepsy as a means of interfering with ictogenesis, therapies that interfered with SRC may provide some anti-epileptogenic therapeutic benefit, even if the interference targeted structures that were not involved originally in the seizure. Here, we show how the Network Theory of Epilepsy can be expanded to include neural plasticity mechanisms associated with learning by providing an overview of Memory Consolidation, the mechanisms thought to underlie MC, their relation to Seizure-Related Consolidation, and suggesting novel, anti-epileptogenic therapies targeting interference with network activation in epilepsy following seizures during post-seizure sleep.

Case report: Bridging limbic network epilepsy with psychiatric, memory, and sleep comorbidities: case illustrations of reversible psychosis symptoms during continuous, high-frequency ANT-DBS

The network nature of focal epilepsy is exemplified by mesial temporal lobe epilepsy (mTLE), characterized by focal seizures originating from the mesial temporal neocortex, amygdala, and hippocampus. The mTLE network hypothesis is evident in seizure semiology and interictal comorbidities, both reflecting limbic network dysfunction. The network generating seizures also supports essential physiological functions, including memory, emotion, mood, and sleep. Pathology in the mTLE network often manifests as interictal behavioral disturbances and seizures. The limbic circuit is a vital network, and here we review one of the most common focal epilepsies and its comorbidities. We describe two people with drug resistant mTLE implanted with an investigational device enabling continuous hippocampal local field potential sensing and anterior nucleus of thalamus deep brain stimulation (ANT-DBS) who experienced reversible psychosis during continuous high-frequency stimulation. The mechanism(s) of psychosis remain poorly understood and here we speculate that the anti-epileptic effect of high frequency ANT-DBS may provide insights into the physiology of primary disorders associated with psychosis.

A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy

Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy, but the optimal stimulation parameters remain unclear. We developed a neurotechnology platform for tracking seizures and MMS to enable data streaming between an investigational brain sensing-stimulation implant, mobile devices, and a cloud environment. Artificial Intelligence algorithms provided accurate catalogs of seizures, interictal epileptiform spikes, and wake-sleep brain states. Remotely administered memory and mood assessments were used to densely sample cognitive and behavioral response during ANT-DBS. We evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. They both reduced seizures, but low-frequency ANT-DBS showed greater reductions and better sleep and memory. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapy.

Termination of convulsion seizures by destabilizing and perturbing seizure memory engrams

Epileptogenesis, arising from alterations in synaptic strength, shares mechanistic and phenotypic parallels with memory formation. However, direct evidence supporting the existence of seizure memory remains scarce. Leveraging a conditioned seizure memory (CSM) paradigm, we found that CSM enabled the environmental cue to trigger seizure repetitively, and activating cue-responding engram cells could generate CSM artificially. Moreover, cue exposure initiated an analogous process of memory reconsolidation driven by mammalian target of rapamycin-brain-derived neurotrophic factor signaling. Pharmacological targeting of the mammalian target of rapamycin pathway within a limited time window reduced seizures in animals and interictal epileptiform discharges in patients with refractory seizures. Our findings reveal a causal link between seizure memory engrams and seizures, which leads us to a deeper understanding of epileptogenesis and points to a promising direction for epilepsy treatment.

Wake slow waves in focal human epilepsy impact network activity and cognition

Slow waves of neuronal activity are a fundamental component of sleep that are proposed to have homeostatic and restorative functions. Despite this, their interaction with pathology is unclear and there is only indirect evidence of their presence during wakefulness. Using intracortical recordings from the temporal lobe of 25 patients with epilepsy, we demonstrate the existence of local wake slow waves (LoWS) with key features of sleep slow waves, including a down-state of neuronal firing. Consistent with a reduction in neuronal activity, LoWS were associated with slowed cognitive processing. However, we also found that LoWS showed signatures of a homeostatic relationship with interictal epileptiform discharges (IEDs): exhibiting progressive adaptation during the build-up of network excitability before an IED and reducing the impact of subsequent IEDs on network excitability. We therefore propose an epilepsy homeostasis hypothesis: that slow waves in epilepsy reduce aberrant activity at the price of transient cognitive impairment.

The broad-spectrum activity of perampanel: state of the art and future perspective of AMPA antagonism beyond epilepsy

Glutamate is the brain's main excitatory neurotransmitter. Glutamatergic neurons primarily compose basic neuronal networks, especially in the cortex. An imbalance of excitatory and inhibitory activities may result in epilepsy or other neurological and psychiatric conditions. Among glutamate receptors, AMPA receptors are the predominant mediator of glutamate-induced excitatory neurotransmission and dictate synaptic efficiency and plasticity by their numbers and/or properties. Therefore, they appear to be a major drug target for modulating several brain functions. Perampanel (PER) is a highly selective, noncompetitive AMPA antagonist approved in several countries worldwide for treating different types of seizures in various epileptic conditions. However, recent data show that PER can potentially address many other conditions within epilepsy and beyond. From this perspective, this review aims to examine the new preclinical and clinical studies-especially those produced from 2017 onwards-on AMPA antagonism and PER in conditions such as mesial temporal lobe epilepsy, idiopathic and genetic generalized epilepsy, brain tumor-related epilepsy, status epilepticus, rare epileptic syndromes, stroke, sleep, epilepsy-related migraine, cognitive impairment, autism, dementia, and other neurodegenerative diseases, as well as provide suggestions on future research agenda aimed at probing the possibility of treating these conditions with PER and/or other AMPA receptor antagonists.

Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.

Feasibility, efficacy, and functional relevance of automated auditory closed-loop suppression of slow-wave sleep in humans

Slow-wave sleep (SWS) is a fundamental physiological process, and its modulation is of interest for basic science and clinical applications. However, automatised protocols for the suppression of SWS are lacking. We describe the development of a novel protocol for the automated detection (based on the whole head topography of frontal slow waves) and suppression of SWS (through closed-loop modulated randomised pulsed noise), and assessed the feasibility, efficacy and functional relevance compared to sham stimulation in 15 healthy young adults in a repeated-measure sleep laboratory study. Auditory compared to sham stimulation resulted in a highly significant reduction of SWS by 30% without affecting total sleep time. The reduction of SWS was associated with an increase in lighter non-rapid eye movement sleep and a shift of slow-wave activity towards the end of the night, indicative of a homeostatic response and functional relevance. Still, cumulative slow-wave activity across the night was significantly reduced by 23%. Undisturbed sleep led to an evening to morning reduction of wake electroencephalographic theta activity, thought to reflect synaptic downscaling during SWS, while suppression of SWS inhibited this dissipation. We provide evidence for the feasibility, efficacy, and functional relevance of a novel fully automated protocol for SWS suppression based on auditory closed-loop stimulation. Future work is needed to further test for functional relevance and potential clinical applications.

Dual orexin antagonist normalized sleep homeostatic drive, enhanced GABAergic inhibition, and suppressed seizures after traumatic brain injury

STUDY OBJECTIVES

Traumatic brain injury (TBI) can result in posttraumatic epilepsy (PTE) and sleep disturbances. We hypothesized that treatment with sleep aids after TBI can ameliorate PTE.

METHODS

CD-1 mice underwent controlled cortical impact (CCI), sham injury, or no craniotomy. Sham and CCI groups underwent a monthlong daily treatment with sleep aids including a dual orexin antagonist (DORA-22) or THIP (gaboxadol) or a respective vehicle starting on the day of CCI. We performed continuous EEG (electroencephalography) recordings at week 1 and months 1, 2, and 3 for ~1 week each time. Seizure analysis occurred at all-time points and sleep analysis occurred in week 1 and month-1/2 in all groups. Subsets of CCI and sham groups were subjected to voltageclamp experiments in hippocampal slices to evaluate GABAergic synaptic inhibition.

RESULTS

DORA-22 treatment suppressed seizures in month 1-3 recordings. TBI reduced the amplitude and frequency of miniature inhibitory synaptic currents (mIPSCs) in dentate granule cells and these changes were rescued by DORA-22 treatment. Sleep analysis showed that DORA-22 increased nonrapid eye movement (NREM) sleep during lights-off whereas THIP increased REM sleep during lights-on in week 1. Both treatments displayed subtle changes in time spent in NREM or REM at month-1/2 as well. TBI not only increased normalized EEG delta power (NΔ) at week-1 and month-1 but also resulted in the loss of the homeostatic diurnal oscillation of NΔ, which was restored by DORA-22 but not THIP treatment.

CONCLUSIONS

Dual orexin antagonists may have a therapeutic potential in suppressing PTE potentially by enhancing GABAergic inhibition and impacting sleep homeostatic drive.

Louis EK, Gregg NM, Lundstrom BN, Crowe CM, Attia TP, Crepeau D, Balzekas I, Marks VS, Wheeler LP, Cimbalnik J, Cook M, Janca R, Sturges BK, Leyde K, Miller KJ, Van Gompel JJ, Denison T, Worrell GA, Kremen V. Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

Barbeau E, Pariente J, Dahan L, Valton L. Sleep: The Tip of the Iceberg in the Bidirectional Link Between Alzheimer's Disease and Epilepsy

The observation that a pathophysiological link might exist between Alzheimer's disease (AD) and epilepsy dates back to the identification of the first cases of the pathology itself and is now strongly supported by an ever-increasing mountain of literature. An overwhelming majority of data suggests not only a higher prevalence of epilepsy in Alzheimer's disease compared to healthy aging, but also that AD patients with a comorbid epileptic syndrome, even subclinical, have a steeper cognitive decline. Moreover, clinical and preclinical investigations have revealed a marked sleep-related increase in the frequency of epileptic activities. This characteristic might provide clues to the pathophysiological pathways underlying this comorbidity. Furthermore, the preferential sleep-related occurrence of epileptic events opens up the possibility that they might hasten cognitive decline by interfering with the delicately orchestrated synchrony of oscillatory activities implicated in sleep-related memory consolidation. Therefore, we scrutinized the literature for mechanisms that might promote sleep-related epileptic activity in AD and, possibly dementia onset in epilepsy, and we also aimed to determine to what degree and through which processes such events might alter the progression of AD. Finally, we discuss the implications for patient care and try to identify a common basis for methodological considerations for future research and clinical practice.

Epileptic seizures and link to memory processes

Epileptogenesis is a complex and not well understood phenomenon. Here, we explore the hypothesis that epileptogenesis could be "hijacking" normal memory processes, and how this hypothesis may provide new directions for epilepsy treatment. First, we review similarities between the hypersynchronous circuits observed in epilepsy and memory consolidation processes involved in strengthening neuronal connections. Next, we describe the kindling model of seizures and its relation to long-term potentiation model of synaptic plasticity. We also examine how the strengthening of epileptic circuits is facilitated during the physiological slow wave sleep, similarly as episodic memories. Furthermore, we present studies showing that specific memories can directly trigger reflex seizures. The neuronal hypersynchrony in early stages of Alzheimer's disease, and the use of anti-epileptic drugs to improve the cognitive symptoms in this disease also suggests a connection between memory systems and epilepsy. Given the commonalities between memory processes and epilepsy, we propose that therapies for memory disorders might provide new avenues for treatment of epileptic patients.

Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.

Sleep fragmentation and decreased REM sleep in a primate model of diurnal cortical seizures

Many people with epilepsy suffer from comorbid sleep disorders and sleep fragmentation. While the disruptive nature of seizures on sleep is well documented, it is unclear how diurnal seizures impact sleep quality and for how long these changes persist during the following nights. To better understand this relationship, the sleep architecture of two rhesus macaques were studied before and several nights after penicillin-induced diurnal seizures. These focal seizures stopped naturally, and none occurred at night. We scored sleep-stage during the nights immediately following the seizures, as well as several nights after seizure induction. We noted a significant increase in movement along with a decrease in sleep efficiency, both limited to the night of seizure induction. For both animals, we observed a significant decrease in the number of REM periods that manifested as a decrease in total REM sleep duration, and this phenomenon persisted up to 2 nights after the seizures. We also found a significant increase in the probability to transition from stage N2 to stage N1 on the night of the seizures. This study shows for the first time that the NHP model of penicillin-induced cortical seizures exhibits significant changes in sleep architecture, including an increase in nocturnal movement, change in sleep architecture and a prolonged decrease in REM activity. The prolonged decrease in REM periods compared to the temporary enhanced movement and reduction of sleep efficiency suggest that these seizures may affect two neural circuits, one controlling REM sleep entry and the other controlling nocturnal wakefulness.

6-year course of sleep homeostasis in a case with epilepsy-aphasia spectrum disorder

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The overnight change of the slope of SSW as EEG marker for nocturnal regeneration.

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Reorganization of brain networks can rescue cognitive functions at least partially.

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Corticosteroids lead to a stabilization of the homeostasis of slope of SSW.

The epilepsy-aphasia spectrum consists of epilepsies with a strong activation of epileptic discharges during non-rapid-eye-movement (NREM) sleep, variable seizure burden and language problems. The homeostatic decrease of slow waves (SW) during NREM sleep (i.e. their amplitude/slope and power) has been related to brain recovery and cognitive function. Epileptic discharges during NREM-sleep were related to an impairment of the decrease of the slope of SW and to cognitive deficits. In this longitudinal case study, we aim to relate this electrophysiological marker, i.e. overnight change of slope of SW, to imaging and behavior.

We report a young girl with a fluctuating course in the epilepsy-aphasia spectrum, ranging from the benign end with self-limited childhood epilepsy with centrotemporal spikes (SLECTS) to the severe end with epileptic encephalopathy with continuous spike waves during sleep (CSWS) with two phases of cognitive regression. She was documented over a period of six years including 12 PSGs, six language fMRIs and seven neuropsychological assessments. We longitudinally studied focal and total spike wave index (SWI), detected SW during NREM sleep, calculated their slopes (first and last hour of NREM sleep and overnight change).

Deterioration of overnight decrease of the slope of SW was paralleled by the occurrence of the EEG picture of bilateral synchronous electrical status epilepticus during sleep (ESES) and neuropsychological deficits, and this impairment was reversible with resolution of ESES and was accompanied by cognitive improvement. A laterality switch from left to right sided language dominance occurred during recovery from the second regression phase. This might reflect a compensating process. Later, the laterality switched back to the left, possibly facilitated by a low SWI on the left hemisphere.

The qualitative analysis of this case supports the view that the longitudinal course of the overnight change of the slope of SW, as an objective, quantitative EEG measure, is related to the course of cognitive function and functional language MR analysis.

Epilepsy as a dynamical system, a most needed paradigm shift in epileptology

The idea of the epileptic brain being highly excitable and facilitated to synchronic activity has guided pharmacological treatment since the early twentieth century. Although tackling epilepsy's seizure-prone feature, by tonically modifying overall circuit excitability and/or connectivity, the last 50 years of drug development has not seen a substantial improvement in seizure suppression of refractory epilepsies. This review presents a new conceptual framework for epilepsy in which the temporal dynamics of the disease plays a more critical role in both its understanding and therapeutic strategies. The repetitive epileptiform pattern (characteristic during ictal activity) and other well-defined electrographic signatures (i.e., present during the interictal period) are discussed in terms of the sequential activation of the circuit motifs. Lessons learned from the physiological activation of neural circuitry are used to further corroborate the argument and explore the transition from proper function to a state of instability. Furthermore, the review explores how interfering in the temporally dependent abnormal connectivity between circuits may work as a therapeutic approach. We also review the use of probing stimulation to access network connectivity and evaluate its power to determine transitional states of the dynamical system as it moves towards regions of instability, especially when conventional electrographic monitoring is proven inefficient. Unorthodox cases, with little or no scalp electrographic correlate, in which ictogenic circuitry and/or seizure spread is temporally restricted to neurovegetative, cognitive, and motivational areas are shown as possible explanations for sudden death in epilepsy (SUDEP) and other psychiatric comorbidities. In short, this review presents a paradigm shift in the way that we address the disease and is aimed to encourage debate rather than narrow the rationale epilepsy is currently engaged in. This article is part of the Special Issue "NEWroscience 2018".

Hijacking of hippocampal-cortical oscillatory coupling during sleep in temporal lobe epilepsy

Memory impairment is the most common cognitive deficit in patients with temporal lobe epilepsy (TLE). This type of epilepsy is currently regarded as a network disease because of its brain-wide alterations in functional connectivity between temporal and extra-temporal regions. In patients with TLE, network dysfunctions can be observed during ictal states, but are also described interictally during rest or sleep. Here, we examined the available literature supporting the hypothesis that hippocampal-cortical coupling during sleep is hijacked in TLE. First, we look at studies showing that the coordination between hippocampal sharp-wave ripples (100-200 Hz), corticothalamic spindles (9-16 Hz), and cortical delta waves (1-4 Hz) during nonrapid eye movement (NREM) sleep is critical for spatial memory consolidation. Then, we reviewed studies showing that animal models of TLE display precise coordination between hippocampal interictal epileptiform discharges (IEDs) and spindle oscillations in the prefrontal cortex. This aberrant oscillatory coupling seems to surpass the physiological ripple-delta-spindle coordination, which could underlie memory consolidation impairments. We also discuss the role of rapid eye movement (REM) sleep for local synaptic plasticity and memory. Sleep episodes of REM provide windows of opportunity for reactivation of expression of immediate early genes (i.e., zif-268 and Arc). Besides, hippocampal theta oscillations during REM sleep seem to be critical for memory consolidation of novel object place recognition task. However, it is still unclear which extend this particular phase of sleep is affected in TLE. In this context, we show some preliminary results from our group, suggesting that hippocampal theta-gamma phase-amplitude coupling is exacerbated during REM in a model of basolateral amygdala fast kindling. In conclusion, there is an increasing body of evidence suggesting that circuits responsible for memory consolidation during sleep seem to be gradually coopted and degraded in TLE. This article is part of the Special Issue "NEWroscience 2018".

Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation

Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+STM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+STM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy.

The Reciprocal Interaction Between Sleep and Alzheimer's Disease

It is becoming increasingly recognized that patients with a variety of neurodegenerative diseases exhibit disordered sleep/wake patterns. While sleep impairments have typically been thought of as sequelae of underlying neurodegenerative processes in sleep-wake cycle regulating brain regions, including the brainstem, hypothalamus, and basal forebrain, emerging evidence now indicates that sleep deficits may also act as pathophysiological drivers of brain-wide disease progression. Specifically, recent work has indicated that impaired sleep can impact on neuronal activity, brain clearance mechanisms, pathological build-up of proteins, and inflammation. Altered sleep patterns may therefore be novel (potentially reversible) dynamic functional markers of proteinopathies and modifiable targets for early therapeutic intervention using non-invasive stimulation and behavioral techniques. Here we highlight research describing a potentially reciprocal interaction between impaired sleep and circadian patterns and the accumulation of pathological signs and features in Alzheimer's disease, the most prevalent neurodegenerative disease in the elderly.

Sleep and Epilepsy: a Focused Review of Pathophysiology, Clinical Syndromes, Co-morbidities, and Therapy

A healthy brain requires balancing of waking and sleeping states. The normal changes in waking and sleeping states result in neurophysiological conditions that either increase or decrease the tendency of seizures and interictal discharges to occur. This article reviews the manifold and complex relationships between sleep and epilepsy and discusses treatment of the sleep-related epilepsies. Several forms of epilepsy predominantly or exclusively manifest during sleep and seizures tend to arise especially from light NREM sleep. Diagnostic interictal epileptiform discharges on the electroencephalogram are also most likely to be activated during deep NREM sleep stage N3. Epileptiform discharges and antiepileptic medications may in turn detrimentally impact sleep. Co-morbid sleep disorders also have the potential to worsen seizure control. Sleep has an important key association with sudden unexpected death in epilepsy (SUDEP). Further research is necessary to understand the complex relationships between sleep and epileptic disorders and their treatments.

Seizure Onset Zone Lateralization Using a Non-linear Analysis of Micro vs. Macro Electroencephalographic Recordings During Seizure-Free Stages of the Sleep-Wake Cycle From Epilepsy Patients

The application of non-linear signal analysis techniques to biomedical data is key to improve our knowledge about complex physiological and pathological processes. In particular, the use of non-linear techniques to study electroencephalographic (EEG) recordings can provide an advanced characterization of brain dynamics. In epilepsy these dynamics are altered at different spatial scales of neuronal organization. We therefore apply non-linear signal analysis to EEG recordings from epilepsy patients derived with intracranial hybrid electrodes, which are composed of classical macro contacts and micro wires. Thereby, these electrodes record EEG at two different spatial scales. Our aim is to test the degree to which the analysis of the EEG recorded at these different scales allows us to characterize the neuronal dynamics affected by epilepsy. For this purpose, we retrospectively analyzed long-term recordings performed during five nights in three patients during which no seizures took place. As a benchmark we used the accuracy with which this analysis allows determining the hemisphere that contains the seizure onset zone, which is the brain area where clinical seizures originate. We applied the surrogate-corrected non-linear predictability score (ψ), a non-linear signal analysis technique which was shown previously to be useful for the lateralization of the seizure onset zone from classical intracranial EEG macro contact recordings. Higher values of ψ were found predominantly for signals recorded from the hemisphere containing the seizure onset zone as compared to signals recorded from the opposite hemisphere. These differences were found not only for the EEG signals recorded with macro contacts, but also for those recorded with micro wires. In conclusion, the information obtained from the analysis of classical macro EEG contacts can be complemented by the one of micro wire EEG recordings. This combined approach may therefore help to further improve the degree to which quantitative EEG analysis can contribute to the diagnostics in epilepsy patients.

Microelectrode recordings in human epilepsy: a case for clinical translation

With their 'all-or-none' action potential responses, single neurons (or units) are accepted as the basic computational unit of the brain. There is extensive animal literature to support the mechanistic importance of studying neuronal firing as a way to understand neuronal microcircuits and brain function. Although most studies have emphasized physiology, there is increasing recognition that studying single units provides novel insight into system-level mechanisms of disease. Microelectrode recordings are becoming more common in humans, paralleling the increasing use of intracranial electroencephalography recordings in the context of presurgical evaluation in focal epilepsy. In addition to single-unit data, microelectrode recordings also record local field potentials and high-frequency oscillations, some of which may be different to that recorded by clinical macroelectrodes. However, microelectrodes are being used almost exclusively in research contexts and there are currently no indications for incorporating microelectrode recordings into routine clinical care. In this review, we summarize the lessons learnt from 65 years of microelectrode recordings in human epilepsy patients. We cover the electrode constructs that can be utilized, principles of how to record and process microelectrode data and insights into ictal dynamics, interictal dynamics and cognition. We end with a critique on the possibilities of incorporating single-unit recordings into clinical care, with a focus on potential clinical indications, each with their specific evidence base and challenges.

Controversies on the network theory of epilepsy: Debates held during the ICTALS 2019 conference

Debates on six controversial topics on the network theory of epilepsy were held during two debate sessions, as part of the International Conference for Technology and Analysis of Seizures, 2019 (ICTALS 2019) convened at the University of Exeter, UK, September 2-5 2019. The debate topics were (1) From pathologic to physiologic: is the epileptic network part of an existing large-scale brain network? (2) Are micro scale recordings pertinent for defining the epileptic network? (3) From seconds to years: do we need all temporal scales to define an epileptic network? (4) Is it necessary to fully define the epileptic network to control it? (5) Is controlling seizures sufficient to control the epileptic network? (6) Does the epileptic network want to be controlled? This article, written by the organizing committee for the debate sessions and the debaters, summarizes the arguments presented during the debates on these six topics.

Acute Focal Seizures Start As Local Synchronizations of Neuronal Ensembles

Understanding seizure formation and spread remains a critical goal of epilepsy research. We used fast in vivo two-photon calcium imaging in male mouse neocortex to reconstruct, with single-cell resolution, the dynamics of acute (4-aminopyridine) focal cortical seizures as they originate within a spatially confined seizure initiation site (intrafocal region), and subsequently propagate into neighboring cortical areas (extrafocal region). We find that seizures originate as local neuronal ensembles within the initiation site. This abnormal hyperactivity engages increasingly larger areas in a saltatory fashion until it breaks into neighboring cortex, where it proceeds smoothly and is then detected electrophysiologically (LFP). Interestingly, PV inhibitory interneurons have spatially heterogeneous activity in intrafocal and extrafocal territories, ruling out a simple role of inhibition in seizure formation and spread. We propose a two-step model for the progression of focal seizures, where neuronal ensembles activate first, generating a microseizure, followed by widespread neural activation in a traveling wave through neighboring cortex during macroseizures.SIGNIFICANCE STATEMENT We have used calcium imaging in mouse sensory cortex in vivo to reconstruct the onset of focal seizures elicited by local injection of the chemoconvulsant 4-aminopyridine. We demonstrate at cellular resolution that acute focal seizures originate as increasingly synchronized local neuronal ensembles. Because of its spatial confinement, this process may at first be undetectable even by nearby LFP electrodes. Further, we establish spatial footprints of local neural subtype activity that correspond to consecutive steps of seizure microprogression. Such footprints could facilitate determining the recording location (e.g., inside/outside an epileptogenic focus) in high-resolution studies, even in the absence of a priori knowledge about where exactly a seizure started.

Uncovering the Pharmacological Mechanism of Chaibei Zhixian Decoction on Epilepsy by Network Pharmacology Analysis

Objective

Epilepsy is a neuronal disorder that is characterized by epileptic seizures and linked with abnormal neural functioning in the brain. Traditional Chinese medicine (TCM) formula Chaibei Zhixian decoction (CZD) has been widely used for epilepsy in China while the pharmacological mechanisms are still unclear. In the present study, systematic and comprehensive network pharmacology was utilized for the first time to reveal the potential pharmacological mechanisms of CZD on epilepsy.

Methods

Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and analysis platform was utilized for the development of an ingredients-targets database. After identifying epileptic targets of CZD, their interaction with other proteins was estimated based on protein-protein interaction network created from STITCH and gene ontology (GO) enrichment analysis utilizing Cytoscape-ClueGO plugin.

Results

CZD formula was found to have 643 chemical ingredients, and the potential protein targets of these ingredients were 5230, as retrieved from TCMSP database. Twenty-six protein targets were found to be associated with epilepsy. Thirteen hub genes were regulated by CZD in epilepsy, including estradiol, ESR1, ESR2, SRC, CTNNB1, EP300, MAPK1, MAPK3, SP1, BRCA1, NCOA3, CHRM1, and GSK3B. The results of GO terms analysis showed that 8 GO terms were recovered in the form of 3 clusters, including negative regulation of protein kinase B signaling, positive regulation of interleukin-1 production, and microvillus assembly.

Conclusions

Network pharmacology approach provides better understanding of the underlying pharmacological mechanisms of CZD on epilepsy. Estradiol, ESR1, ESR2, CTNNB1, EP300, MAPK1, MAPK3, BRCA1, and GSK3B are likely to be important molecules regulated by CZD in treatment of epilepsy. Negative regulation of protein kinase B signaling may play vital roles in the treatment of epilepsy by CZD.

Deep brain stimulation probing performance is enhanced by pairing stimulus with epileptic seizure

The unpredictability of spontaneous and recurrent seizures significantly impairs the quality of life of patients with epilepsy. Probing neural network excitability with deep brain electrical stimulation (DBS) has shown promising results predicting pathological shifts in brain states. This work presents a proof-of-principal that active electroencephalographic (EEG) probing, as a seizure predictive tool, is enhanced by pairing DBS and the electrographic seizure itself. The ictogenic model used consisted of inducing seizures by continuous intravenous infusion of pentylenetetrazol (PTZ - 2.5 mg/ml/min) while a probing DBS was delivered to the thalamus (TH) or amygdaloid complex to detect changes prior to seizure onset. Cortical electrophysiological recordings were performed before, during, and after PTZ infusion. Thalamic DBS probing, but not amygdaloid, was able to predict seizure onset without any observable proconvulsant effects. However, previously pairing amygdaloid DBS and epileptic polyspike discharges (day-1) elicited distinct preictal cortically recorded evoked response (CRER) (day-2) when compared with control groups that received the same amount of electrical pulses at different moments of the ictogenic progress at day-1. In conclusion, our results have demonstrated that the pairing strategy potentiated the detection of an altered brain state prior to the seizure onset. The EEG probing enhancement method opens many possibilities for both diagnosis and treatment of epilepsy.

Automated unsupervised behavioral state classification using intracranial electrophysiology

OBJECTIVE

Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography.

APPROACH

Data from eight patients undergoing evaluation for epilepsy surgery (age [Formula: see text], three female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1-235 Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3.

MAIN RESULTS

Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%).

SIGNIFICANCE

Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.

Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson’s disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

Expanding Brain-Computer Interfaces for Controlling Epilepsy Networks: Novel Thalamic Responsive Neurostimulation in Refractory Epilepsy

Seizures have traditionally been considered hypersynchronous excitatory events and epilepsy has been separated into focal and generalized epilepsy based largely on the spatial distribution of brain regions involved at seizure onset. Epilepsy, however, is increasingly recognized as a complex network disorder that may be distributed and dynamic. Responsive neurostimulation (RNS) is a recent technology that utilizes intracranial electroencephalography (EEG) to detect seizures and delivers stimulation to cortical and subcortical brain structures for seizure control. RNS has particular significance in the clinical treatment of medically refractory epilepsy and brain–computer interfaces in epilepsy. Closed loop RNS represents an important step forward to understand and target nodes in the seizure network. The thalamus is a central network node within several functional networks and regulates input to the cortex; clinically, several thalamic nuclei are safe and feasible targets. We highlight the network theory of epilepsy, potential targets for neuromodulation in epilepsy and the first reported use of RNS as a first generation brain–computer interface to detect and stimulate the centromedian intralaminar thalamic nucleus in a patient with bilateral cortical onset of seizures. We propose that advances in network analysis and neuromodulatory techniques using brain–computer interfaces will significantly improve outcomes in patients with epilepsy. There are numerous avenues of future direction in brain–computer interface devices including multi-modal sensors, flexible electrode arrays, multi-site targeting, and wireless communication.

Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank

The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.

Neuronal Assemblies Evidence Distributed Interactions within a Tactile Discrimination Task in Rats

Accumulating evidence suggests that neural interactions are distributed and relate to animal behavior, but many open questions remain. The neural assembly hypothesis, formulated by Hebb, states that synchronously active single neurons may transiently organize into functional neural circuits-neuronal assemblies (NAs)-and that would constitute the fundamental unit of information processing in the brain. However, the formation, vanishing, and temporal evolution of NAs are not fully understood. In particular, characterizing NAs in multiple brain regions over the course of behavioral tasks is relevant to assess the highly distributed nature of brain processing. In the context of NA characterization, active tactile discrimination tasks with rats are elucidative because they engage several cortical areas in the processing of information that are otherwise masked in passive or anesthetized scenarios. In this work, we investigate the dynamic formation of NAs within and among four different cortical regions in long-range fronto-parieto-occipital networks (primary somatosensory, primary visual, prefrontal, and posterior parietal cortices), simultaneously recorded from seven rats engaged in an active tactile discrimination task. Our results first confirm that task-related neuronal firing rate dynamics in all four regions is significantly modulated. Notably, a support vector machine decoder reveals that neural populations contain more information about the tactile stimulus than the majority of single neurons alone. Then, over the course of the task, we identify the emergence and vanishing of NAs whose participating neurons are shown to contain more information about animal behavior than randomly chosen neurons. Taken together, our results further support the role of multiple and distributed neurons as the functional unit of information processing in the brain (NA hypothesis) and their link to active animal behavior.

Slow Activity in Focal Epilepsy During Sleep and Wakefulness

INTRODUCTION

We aimed to test differences between healthy subjects and patients with respect to slow wave activity during wakefulness and sleep.

METHODS

Fifteen patients affected by nonlesional focal epilepsy originating within temporal areas and fourteen matched controls underwent a 24-hour EEG recording. We studied the EEG power spectral density during wakefulness and sleep in delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (12-15 Hz), and beta (16-20 Hz) bands.

RESULTS

During sleep, patients with focal epilepsy showed higher power from delta to beta frequency bands compared with controls. The effect was widespread for alpha band and above, while localized over the affected hemisphere for delta (sleep cycle 1, P = .006; sleep cycle 2, P = .008; sleep cycle 3, P = .017). The analysis of interhemispheric differences showed that the only frequency band stronger over the affected regions was the delta band during the first 2 sleep cycles (sleep cycle 1, P = .014; sleep cycle 2, P = .002). During wakefulness, patients showed higher delta/theta activity over the affected regions compared with controls.

CONCLUSIONS

Patients with focal epilepsy showed a pattern of power increases characterized by a selective slow wave activity enhancement over the epileptic regions during daytime and sleep. This phenomenon was stronger and asymmetric during the first sleep cycles.

Altered sleep homeostasis correlates with cognitive impairment in patients with focal epilepsy

In animal studies, both seizures and interictal spikes induce synaptic potentiation. Recent evidence suggests that electroencephalogram slow wave activity during sleep reflects synaptic potentiation during wake, and that its homeostatic decrease during the night is associated with synaptic renormalization and its beneficial effects. Here we asked whether epileptic activity induces plastic changes that can be revealed by high-density electroencephalography recordings during sleep in 15 patients with focal epilepsy and 15 control subjects. Compared to controls, patients with epilepsy displayed increased slow wave activity power during non-rapid eye movement sleep over widespread, bilateral scalp regions. This global increase in slow wave activity power was positively correlated with the frequency of secondarily generalized seizures in the 3-5 days preceding the recordings. Individual patients also showed local increases in sleep slow wave activity power at scalp locations matching their seizure focus. This local increase in slow wave activity power was positively correlated with the frequency of interictal spikes during the last hour of wakefulness preceding sleep. By contrast, frequent interictal spikes during non-rapid eye movement sleep predicted a reduced homeostatic decrease in the slope of sleep slow waves during the night, which in turn predicted reduced daytime learning. Patients also showed an increase in sleep spindle power, which was negatively correlated with intelligence quotient. Altogether, these findings suggest that both seizures and interictal spikes may induce long-lasting changes in the human brain that can be sensitively detected by electroencephalographic markers of sleep homeostasis. Furthermore, abnormalities in sleep markers are correlated with cognitive impairment, suggesting that not only seizures, but also interictal spikes can have negative consequences.

Behavioral state classification in epileptic brain using intracranial electrophysiology

OBJECTIVE

Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery.

APPROACH

Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier.

MAIN RESULTS

Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature.

SIGNIFICANCE

Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

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.

Slow wave sleep and accelerated forgetting

We investigated whether the benefit of slow wave sleep (SWS) for memory consolidation typically observed in healthy individuals is disrupted in people with accelerated long-term forgetting (ALF) due to epilepsy. SWS is thought to play an active role in declarative memory in healthy individuals and, furthermore, electrographic epileptiform activity is often more prevalent during SWS than during wakefulness or other sleep stages. We studied the relationship between SWS and the benefit of sleep for memory retention using a word-pair associates task. In both the ALF and the healthy control groups, sleep conferred a memory benefit. However, the relationship between the amount of SWS and sleep-related memory benefits differed significantly between the groups. In healthy participants, the amount of SWS correlated positively with sleep-related memory benefits. In stark contrast, the more SWS, the smaller the sleep-related memory benefit in the ALF group. Therefore, contrary to its role in healthy people, SWS-associated brain activity appears to be deleterious for memory in patients with ALF.

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.

Exploring human epileptic activity at the single-neuron level

Today, localization of the seizure focus heavily relies on EEG monitoring (scalp or intracranial). However, current technology enables much finer resolutions. The activity of hundreds of single neurons in the human brain can now be simultaneously explored before, during, and after a seizure or in association with an interictal discharge. This technology opens up new horizons to understanding epilepsy at a completely new level. This review therefore begins with a brief description of the basis of the technology, the microelectrodes, and the setup for their implantation in patients with epilepsy. Using these electrodes, recent studies provide novel insights into both the time domain and firing patterns of epileptic activity of single neurons. In the time domain, seizure-related activity may occur even minutes before seizure onset (in its current, EEG-based definition). Seizure-related neuronal interactions exhibit complex heterogeneous dynamics. In the seizure-onset zone, changes in firing patterns correlate with cell loss; in the penumbra, neurons maintain their spike stereotypy during a seizure. Hence, investigation of the extracellular electrical activity is expected to provide a better understanding of the mechanisms underlying the disease; it may, in the future, serve for a more accurate localization of the seizure focus; and it may also be employed to predict the occurrence of seizures prior to their behavioral manifestation in order to administer automatic therapeutic interventions.

The functional organization of human epileptic hippocampus

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex.

Framing Neuro-Glia Coupling in Antiepileptic Drug Design

We delineate perspectives for the design and discovery of antiepileptic drugs (AEDs) with fewer side effects by focusing on astroglial modulation of spatiotemporal seizure dynamics. It is now recognized that the major inhibitory neurotransmitter of the brain, γ-aminobutyric acid (GABA), can be released through the reversal of astroglial GABA transporters. Synaptic spillover and subsequent glutamate (Glu) uptake in neighboring astrocytes evoke replacement of extracellular Glu for GABA, driving neurons away from the seizure threshold. Attenuation of synaptic signaling by this negative feedback through the interplay of Glu and GABA transporters of adjacent astroglia can result in shortened seizures. By contrast, long-range activation of astroglia through gap junctions may promote recurrent seizures on the model of pharmacoresistant temporal lobe epilepsy. From their first detection to our current understanding, we identify various targets that shape both short- and long-range neuro-astroglia coupling, as these are manifest in epilepsy phenomena and in the associated research promotions of AED.

The emergence of single neurons in clinical neurology

Single neuron actions and interactions are the sine qua non of brain function, and nearly all diseases and injuries of the CNS trace their clinical sequelae to neuronal dysfunction or failure. Remarkably, discussion of neuronal activity is largely absent in clinical neuroscience. Advances in neurotechnology and computational capabilities, accompanied by shifts in theoretical frameworks, have led to renewed interest in the information represented by single neurons. Using direct interfaces with the nervous system, millisecond-scale information will soon be extracted from single neurons in clinical environments, supporting personalized treatment of neurologic and psychiatric disease. In this Perspective, we focus on single-neuronal activity in restoring communication and motor control in patients suffering from devastating neurological injuries. We also explore the single neuron's role in epilepsy and movement disorders, surgical anesthesia, and in cognitive processes disrupted in neurodegenerative and neuropsychiatric disease. Finally, we speculate on how technological advances will revolutionize neurotherapeutics.

Is There a Relation between EEG-Slow Waves and Memory Dysfunction in Epilepsy? A Critical Appraisal

Is there a relationship between peri-ictal slow waves, loss of consciousness, memory, and slow-wave sleep, in patients with different forms of epilepsy? We hypothesize that mechanisms, which result in peri-ictal slow-wave activity as detected by the electroencephalogram, could negatively affect memory processes. Slow waves (≤4 Hz) can be found in seizures with impairment of consciousness and also occur in focal seizures without impairment of consciousness but with inhibited access to memory functions. Peri-ictal slow waves are regarded as dysfunctional and are probably caused by mechanisms, which are essential to disturb the consolidation of memory entries in these patients. This is in strong contrast to physiological slow-wave activity during deep sleep, which is thought to group memory-consolidating fast oscillatory activity. In patients with epilepsy, slow waves may not only correlate with the peri-ictal clouding of consciousness, but could be the epiphenomenon of mechanisms, which interfere with normal brain function in a wider range. These mechanisms may have transient impacts on memory, such as temporary inhibition of memory systems, altered patterns of hippocampal-neocortical interactions during slow-wave sleep, or disturbed cross-frequency coupling of slow and fast oscillations. In addition, repeated tonic-clonic seizures over the years in uncontrolled chronic epilepsy may cause a progressive cognitive decline. This hypothesis can only be assessed in long-term prospective studies. These studies could disentangle the reversible short-term impacts of seizures, and the impacts of chronic uncontrolled seizures. Chronic uncontrolled seizures lead to irreversible memory impairment. By contrast, short-term impacts do not necessarily lead to a progressive cognitive decline but result in significantly impaired peri-ictal memory performance.