How can we identify ictal and interictal abnormal activity?

A scalable platform for acquisition of high-fidelity human intracranial EEG with minimal clinical burden

Human neuroscience research has been significantly advanced by neuroelectrophysiological studies from people with refractory epilepsy-the only routine clinical intervention that acquires multi-day, multi-electrode human intracranial electroencephalography (iEEG). While a sampling rate below 2 kHz is sufficient for manual iEEG review by epileptologists, computational methods and research studies may benefit from higher resolution, which requires significant technical development. At adult and pediatric Stanford hospitals, research ports of commercial clinical acquisition systems were configured to collect 10 kHz iEEG of up to 256 electrodes simultaneously with the clinical data. The research digital stream was designed to be acquired post-digitization, resulting in no loss in clinical signal quality. This novel framework implements a near-invisible research platform to facilitate the secure, routine collection of high-resolution iEEG that minimizes research hardware footprint and clinical workflow interference. The addition of a pocket-sized router in the patient room enabled an encrypted tunnel to securely transmit research-quality iEEG across hospital networks to a research computer within the hospital server room, where data was coded, de-identified, and uploaded to cloud storage. Every eligible patient undergoing iEEG clinical evaluation at both hospitals since September 2017 has been recruited; participant recruitment is ongoing. Over 350+ terabytes (representing 1000+ days) of neuroelectrophysiology were recorded across 200+ participants of diverse demographics. To our knowledge, this is the first report of such a research integration within a hospital setting. It is a promising approach to promoting equitable participant enrollment and building comprehensive data repositories with consistent, high-fidelity specifications towards new discoveries in human neuroscience.

EEG findings and clinical severity and quality of life in non-epileptic patients with autism spectrum disorders

Electroencephalogram (EEG) abnormalities could be seen in up to 60% of non-epileptic children with autism spectrum disorder (ASD). They have been used as biomarkers of ASD severity. The objective of our study is to identify EEG abnormalities in children with different degrees of ASD severity based on the Autism Treatment Evaluation Checklist (ATEC). We also want to assess the quality of life for children with ASD. All of the children underwent at least one hour of sleep-deprived EEG. Forty-five children were enrolled, of whom 42 were male. EEG abnormalities were found in 10 (22.2%) children, predominantly in the bilateral frontal areas. There were no differences in EEG findings among the mild, moderate, and severe ASD groups. The severity of ASD was associated with female sex (p-value = 0.013), ASD with attention deficit hyperactivity disorder (ADHD) (p-value = 0.032), ASD children taking medications (p-value = 0.048), and a lower Pediatric Quality of Life Inventory (PedsQL) (p-value <0.001). Social and emotional domains were the most problematic for health-related quality of life in ASD children, according to parent reports of PedsQL. Further studies with a larger sample size will help to clarify the potential associations between EEG abnormalities and the severity of ASD, as well as the impact on quality of life.

Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network

OBJECTIVE

Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity.

METHODS

We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification.

RESULTS

Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling.

SIGNIFICANCE

The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

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.

Neuronal K+-Cl- cotransporter KCC2 as a promising drug target for epilepsy treatment

Epilepsy is a prevalent neurological disorder characterized by unprovoked seizures. γ-Aminobutyric acid (GABA) serves as the primary fast inhibitory neurotransmitter in the brain, and GABA binding to the GABAA receptor (GABAAR) regulates Cl- and bicarbonate (HCO3-) influx or efflux through the channel pore, leading to GABAergic inhibition or excitation, respectively. The neuron-specific K+-Cl- cotransporter 2 (KCC2) is essential for maintaining a low intracellular Cl- concentration, ensuring GABAAR-mediated inhibition. Impaired KCC2 function results in GABAergic excitation associated with epileptic activity. Loss-of-function mutations and altered expression of KCC2 lead to elevated [Cl-]i and compromised synaptic inhibition, contributing to epilepsy pathogenesis in human patients. KCC2 antagonism studies demonstrate the necessity of limiting neuronal hyperexcitability within the brain, as reduced KCC2 functioning leads to seizure activity. Strategies focusing on direct (enhancing KCC2 activation) and indirect KCC2 modulation (altering KCC2 phosphorylation and transcription) have proven effective in attenuating seizure severity and exhibiting anti-convulsant properties. These findings highlight KCC2 as a promising therapeutic target for treating epilepsy. Recent advances in understanding KCC2 regulatory mechanisms, particularly via signaling pathways such as WNK, PKC, BDNF, and its receptor TrkB, have led to the discovery of novel small molecules that modulate KCC2. Inhibiting WNK kinase or utilizing newly discovered KCC2 agonists has demonstrated KCC2 activation and seizure attenuation in animal models. This review discusses the role of KCC2 in epilepsy and evaluates its potential as a drug target for epilepsy treatment by exploring various strategies to regulate KCC2 activity.

Recovery of a learned behavior despite partial restoration of neuronal dynamics after chronic inactivation of inhibitory neurons

Maintaining motor skills is crucial for an animal's survival, enabling it to endure diverse perturbations throughout its lifespan, such as trauma, disease, and aging. What mechanisms orchestrate brain circuit reorganization and recovery to preserve the stability of behavior despite the continued presence of a disturbance? To investigate this question, we chronically silenced a fraction of inhibitory neurons in a brain circuit necessary for singing in zebra finches. Song in zebra finches is a complex, learned motor behavior and central to reproduction. This manipulation altered brain activity and severely perturbed song for around two months, after which time it was precisely restored. Electrophysiology recordings revealed abnormal offline dynamics, resulting from chronic inhibition loss, some aspects of which returned to normal as the song recovered. However, even after the song had fully recovered, the levels of neuronal firing in the premotor and motor areas did not return to a control-like state. Single-cell RNA sequencing revealed that chronic silencing of interneurons led to elevated levels of microglia and MHC I, which were also observed in normal juveniles during song learning. These experiments demonstrate that the adult brain can overcome extended periods of abnormal activity, and precisely restore a complex behavior, without recovering normal neuronal dynamics. These findings suggest that the successful functional recovery of a brain circuit after a perturbation can involve more than mere restoration to its initial configuration. Instead, the circuit seems to adapt and reorganize into a new state capable of producing the original behavior despite the persistence of some abnormal neuronal dynamics.

Pitfalls in scalp EEG: Current obstacles and future directions

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

Improving the accuracy of epileptogenic zone localization in stereo EEG with machine learning algorithms

The precise identification of the epileptogenic zone (EZ) is paramount in the presurgical evaluation of epilepsy patients to ensure successful surgical outcomes. The analysis of Stereo EEG, an instrumental tool for EZ localization, poses considerable challenges even for experienced epileptologists. Consequently, the development of machine learning (ML)-based computational tools for enhanced EZ localization is imperative. In this investigation, we developed ML models utilizing Stereo EEG from 15 patients, who remained seizure-free (Engel 1 a-d) following EZ resection, over an average follow-up period of 44.4 months. Utilizing Delphos and MNI detectors, spikes and High Frequency Oscillations (HFOs) were identified from Stereo EEG in Resected Zone (RZ) and non-Resected Zone (non-RZ). Linear and non-linear features were estimated from each modality using MATLAB. A total of 27,744 spikes, 7,790 ripples, and 7,632 fast ripples, along with their combinations, were employed to train the ML models. The Gradient Boosting classifier demonstrated the highest prediction accuracy of 98.5% for EZ localization in Mesial Temporal Lobe Epilepsy (MTLE) when trained with features derived from the spike-ripple combination. In the case of Neocortical Epilepsy (NE), the Extra Trees classifier achieved an accuracy of 87.6% when utilizing features from fast ripples. The Random Forest, Extra Trees, and Gradient Boosting algorithms were the most effective for predicting the RZ. Linear features outperformed non-linear features in predicting epileptogenic zones within the epileptic brain. Our study establishes the capability of ML methodologies in localizing epileptogenic zones with high accuracy. Future studies that focus on increasing the training sample size and incorporating more advanced machine learning (ML) algorithms have the potential to significantly improve the accuracy of these models in pinpointing epileptogenic networks. Additionally, implementing this ML approach across multiple research centers would contribute to the broader validation and generalizability of this technique.

Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data

Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.

Advances in Image Processing for Epileptogenic Zone Detection with MRI

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits

In the mammalian brain information processing and storage rely on the complex coding and decoding events performed by neuronal networks. These actions are based on the computational ability of neurons and their functional engagement in neuronal assemblies where precise timing of action potential firing is crucial. Neuronal circuits manage a myriad of spatially and temporally overlapping inputs to compute specific outputs that are proposed to underly memory traces formation, sensory perception, and cognitive behaviors. Spike-timing-dependent plasticity (STDP) and electrical brain rhythms are suggested to underlie such functions while the physiological evidence of assembly structures and mechanisms driving both processes continues to be scarce. Here, we review foundational and current evidence on timing precision and cooperative neuronal electrical activity driving STDP and brain rhythms, their interactions, and the emerging role of glial cells in such processes. We also provide an overview of their cognitive correlates and discuss current limitations and controversies, future perspectives on experimental approaches, and their application in humans.

Interictal discharges in the human brain are travelling waves arising from an epileptogenic source

While seizure activity may be electrographically widespread, increasing evidence has suggested that ictal discharges may in fact represent travelling waves propagated from a focal seizure source. Interictal epileptiform discharges (IEDs) are an electrographic manifestation of excessive hypersynchronization of cortical activity that occur between seizures and are considered a marker of potentially epileptogenic tissue. The precise relationship between brain regions demonstrating IEDs and those involved in seizure onset, however, remains poorly understood. Here, we hypothesize that IEDs likewise reflect the receipt of travelling waves propagated from the same regions which give rise to seizures. Forty patients from our institution who underwent invasive monitoring for epilepsy, proceeded to surgery and had at least one year of follow-up were included in our study. Interictal epileptiform discharges were detected using custom software, validated by a clinical epileptologist. We show that IEDs reach electrodes in sequences with a consistent temporal ordering, and this ordering matches the timing of receipt of ictal discharges, suggesting that both types of discharges spread as travelling waves. We use a novel approach for localization of ictal discharges, in which time differences of discharge receipt at nearby electrodes are used to compute source location; similar algorithms have been used in acoustics and geophysics. We find that interictal discharges co-localize with ictal discharges. Moreover, interictal discharges tend to localize to the resection territory in patients with good surgical outcome and outside of the resection territory in patients with poor outcome. The seizure source may originate at, and also travel to, spatially distinct IED foci. Our data provide evidence that interictal discharges may represent travelling waves of pathological activity that are similar to their ictal counterparts, and that both ictal and interictal discharges emerge from common epileptogenic brain regions. Our findings have important clinical implications, as they suggest that seizure source localizations may be derived from interictal discharges, which are much more frequent than seizures.

Effect of Dexmedetomidine versus Propofol on Intraoperative Seizure Onset During Awake Craniotomy: A Retrospective Study

OBJECTIVE

The effect of dexmedetomidine (DEX) compared with propofol on intraoperative seizures (IOSs) detected using electrocorticography during awake craniotomy for resection of brain tumors is unknown. This investigation aimed to compare IOS rate in patients receiving DEX versus propofol as sedative agent.

METHODS

In this retrospective single-center study, awake craniotomies performed from January 2014 to December 2019 were analyzed. All IOSs detected by electrocorticography along with vital signs were recorded.

RESULTS

Of 168 adults enrolled in the study, 58 were administered DEX and 110 were administered propofol. IOSs occurred more frequently in the DEX group (22%) versus the propofol group (11%) (P = 0.046). A higher incidence of bradycardia was also observed in the DEX group (P < 0.001). Higher incidence of hypertension and a higher mean heart rate were recorded in the propofol group (P = 0.006 and P < 0.001, respectively). No serious adverse events requiring active drug administration were noted in either group. At univariate regression analysis, DEX demonstrated a tendency to favor IOS onset but without statistical significance (odds ratio = 2.36, P = 0.051). Patients in both groups had a similar epilepsy outcome at the 1-year postoperative follow-up.

CONCLUSIONS

IOSs detected with electrocorticography during awake craniotomy occurred more frequently in patients receiving DEX than propofol. However, patients receiving DEX were not shown to be at a statistically significant greater risk for IOS onset. DEX is a valid alternative to propofol during awake craniotomy in patients affected by tumor-related epilepsy.

Guanfacine inhibits interictal epileptiform events and sodium currents in prefrontal cortex pyramidal neurons

BACKGROUND

Guanfacine (an alpha-2A receptor agonist) is a commonly used drug with recognized efficacy in the treatment of attention deficit hyperactivity disorder (ADHD). This study aimed to assess the effects of guanfacine on short-lasting (interictal) epileptiform discharges in cortical neurons. Moreover, we assessed the effects of guanfacine on voltage-gated sodium currents.

METHODS

We conducted patch-clamp recordings in prefrontal cortex pyramidal neurons obtained from young rats. Interictal epileptiform events were evoked in cortical slices in a zero magnesium proepileptic extracellular solution with an elevated concentration of potassium ions.

RESULTS

Interictal epileptiform discharges were spontaneous depolarisations, which triggered action potentials. Guanfacine (10 and 100 µM) inhibited the frequency of epileptiform discharges. The effect of guanfacine on interictal events persisted in the presence of alpha-2 adrenergic receptor antagonist idazoxan. The tested drug inhibited neuronal excitability. Tonic NMDA currents were not influenced by guanfacine. Recordings from dispersed neurons showed that the tested drug (10 and 100 µM) inhibited persistent and fast inactivating voltage-gated sodium currents.

CONCLUSIONS

This study shows that guanfacine inhibits interictal discharges in cortical neurons independently of alpha-2A adrenergic receptors. This effect may be mediated by voltage-gated sodium currents. Inhibition of interictal activity by guanfacine may be of clinical importance because interictal events often occur in patients with ADHD and may contribute to symptoms of this disease.

Machine learning seizure prediction: one problematic but accepted practice

Objective.Epilepsy is one of the most common neurological disorders and can have a devastating effect on a person's quality of life. As such, the search for markers which indicate an upcoming seizure is a critically important area of research which would allow either on-demand treatment or early warning for people suffering with these disorders. There is a growing body of work which uses machine learning methods to detect pre-seizure biomarkers from electroencephalography (EEG), however the high prediction rates published do not translate into the clinical setting. Our objective is to investigate a potential reason for this.Approach.We conduct an empirical study of a commonly used data labelling method for EEG seizure prediction which relies on labelling small windows of EEG data in temporal groups then selecting randomly from those windows to validate results. We investigate a confound for this approach for seizure prediction and demonstrate the ease at which it can be inadvertently learned by a machine learning system.Main results.We find that non-seizure signals can create decision surfaces for machine learning approaches which can result in false high prediction accuracy on validation datasets. We prove this by training an artificial neural network to learn fake seizures (fully decoupled from biology) in real EEG.Significance.The significance of our findings is that many existing works may be reporting results based on this confound and that future work should adhere to stricter requirements in mitigating this confound. The problematic, but commonly accepted approach in the literature for seizure prediction labelling is potentially preventing real advances in developing solutions for these sufferers. By adhering to the guidelines in this paper future work in machine learning seizure prediction is more likely to be clinically relevant.

Risk Factors for Intraoperative Seizures in Glioma Surgery: Electrocorticography Matters

PURPOSE

Few and contradictory data are available regarding intraoperative seizures during surgery for low-grade gliomas. Aim of this study was to evaluate possible risk factors for the occurrence of IOS.

METHODS

The authors performed a retrospective analysis of 155 patients affected by low-grade gliomas and tumor-related epilepsy, who underwent surgery in our Department, between 2007 and 2018. A statistical analysis was performed by means of univariate and multivariate regression to evaluate any possible correlation between seizure occurrence and several demographic, clinical, neurophysiological, and histopathological features.

RESULTS

Intraoperative seizure occurred in 39 patients (25.16%) with a total of 62 seizure events recorded. Focal seizures were the prevalent seizure type: among them, 39 seizures did not show motor signs, being those with only electrographic and/or with cognitive features the most represented subtypes. Twenty-six seizures occurring during surgery were not spontaneous: direct cortical stimulation with Penfield paradigm was the most prevalent evoking factor. The univariate analysis showed that the following prognostic factors were statistically associated with the occurrence of intraoperative seizure: the awake technique ( P = 0.01) and the interictal epileptiform discharges detected on the baseline electrocorticography (ECoG) ( P < 0.001). After controlling for confounding factors with multivariate analysis, the awake surgery and the epileptic ECoG pattern kept statistical significance.

CONCLUSIONS

The awake surgery procedure and the epileptic ECoG pattern are risk factors for intraoperative seizure. ECoG is mandatory to detect electrographic seizures or seizures without motor signs.

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients' health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.

Localizing epileptogenic network from SEEG using non-linear correlation, mutual information and graph theory analysis

The key challenge in epilepsy surgery is precise localization and removal of the epileptogenic zone (EZ) from the brain. Localization of the epileptogenic network by visual analysis of intracranial EEG is extremely difficult. In this retrospective study, we used interictal connectivity and graph theory analysis on intracranial EEG to better delineate the epileptogenic zone. Patients who underwent surgery for drug-refractory mesial temporal and neocortical epilepsy were included. Computational measures, such as h2 nonlinear correlation and mutual information, were used to estimate the interdependency of intracranial EEGs. We observed that the Out-Degree, Out-Strength, and Betweenness centrality (graph properties) were the best predictors of EZ. From the results, we also found that graph properties with a normalized value above 0.75 were found to be a useful measure to localize the EZ with a sensitivity of 87.88 and a specificity of 87.13. Our results also validate that frequently occurring types of interictal fast discharges (IFD) with connectivity measures and graph properties can better localize the EZ. We foresee graph theory analysis of interictal intracranial EEG data can help precise localization of EZ for cortical resection as well as in minimally invasive radiofrequency ablation of epileptogenic hubs. Further, prospective validation is required for clinical use.

Epilepsy and the gut: Perpetrator or victim?

The brain and the gut are linked together with a complex, bi-path link known as the gut-brain axis through the central and enteric nervous systems. So, the brain directly affects and controls the gut through various neurocrine and endocrine processes, and the gut impacts the brain via different mechanisms. Epilepsy is a central nervous system (CNS) disorder with abnormal brain activity, causing repeated seizures due to a transient excessive or synchronous alteration in the brain’s electrical activity. Due to the strong relationship between the enteric and the CNS, gastrointestinal dysfunction may increase the risk of epilepsy. Meanwhile, about 2.5% of patients with epilepsy were misdiagnosed as having gastrointestinal disorders, especially in children below the age of one year. Gut dysbiosis also has a significant role in epileptogenesis. Epilepsy, in turn, affects the gastrointestinal tract in different forms, such as abdominal aura, epilepsy with abdominal pain, and the adverse effects of medications on the gut and the gut microbiota. Epilepsy with abdominal pain, a type of temporal lobe epilepsy, is an uncommon cause of abdominal pain. Epilepsy also can present with postictal states with gastrointestinal manifestations such as postictal hypersalivation, hyperphagia, or compulsive water drinking. At the same time, antiseizure medications have many gastrointestinal side effects. On the other hand, some antiseizure medications may improve some gastrointestinal diseases. Many gut manipulations were used successfully to manage epilepsy. Prebiotics, probiotics, synbiotics, postbiotics, a ketogenic diet, fecal microbiota transplantation, and vagus nerve stimulation were used successfully to treat some patients with epilepsy. Other manipulations, such as omental transposition, still need more studies. This narrative review will discuss the different ways the gut and epilepsy affect each other.

Alzheimer's Disease with Epileptiform EEG Activity: Abnormal Cortical Sources of Resting State Delta Rhythms in Patients with Amnesic Mild Cognitive Impairment

BACKGROUND

Patients with amnesic mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show a "slowing" of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that "slowing."

OBJECTIVE

Here we tested the hypothesis that the "slowing" of rsEEG rhythms is related to EEA in ADMCI patients.

METHODS

Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals.

RESULTS

EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aβ 42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (<4 Hz) rsEEG source activities as compared to the ADMCI-noEEA and Nold groups. Those source activities showed moderate accuracy (AUROCC = 0.70-0.75) in the discrimination between ADMCI-noEEA versus ADMCI-EEA individuals.

CONCLUSION

It can be speculated that in ADMCI-EEA patients, AD-related amyloid neuropathology may be related to an over-excitation in neurophysiological low-frequency (delta) oscillatory mechanisms underpinning cortical arousal and quiet vigilance.

Pre- and Post-surgical Poor Seizure Control as Hallmark of Malignant Progression in Patients With Glioma?

Background

Regarding brain tumor-related epilepsy (BTRE), there is an increasing number of evidence about a relationship between epileptogenesis and oncogenesis. A recent study suggests a role of post-surgery seizure outcome on the survival of patients with low-grade glioma (LGG), underlying the need for a targeted and aggressive epilepsy treatment.

Objective

This study aims at investigating the possible correlation between pre- and post-surgical seizure control and tumor progression in patients who underwent surgery for LGG.

Methods

We performed a retrospective analysis of patients affected by LGGs and BTRE, in a single high-volume neurosurgical center. Seizure control was assessed before surgery and at 3 years of follow-up. Patients with histological progression in high-grade glioma (HGG) have been evaluated. Clinical features, pre-surgical electroencephalograms (EEGs), and electrocorticography (ECoG) have been analyzed.

Results

Among 154 subjects, we collected 32 patients who presented a tumor progression in HGG during the follow-up period. The majority had poor seizure control both pre- and post-surgery, never being in Engel class Ia throughout the whole history of their disease. Almost all patients with poor seizure control had pathological ECoG recording. Clinical features of seizures did not correlate with seizure outcome. On the univariate analysis, the age, the post-operative Engel class, and the extent of resection (EOR) were the prognostic factors significantly associated with oncological outcome; nevertheless, on multivariate analysis, Engel class significance was not confirmed, and the only predicting factor were age and EOR.

Conclusions

Although not confirmed on multivariate analysis, post-surgical seizure control could be a relevant factor to consider during follow-up of BRTE, in particular, when gross total resection is not achieved. Pathological findings on the ECoG may suggest a “hidden” propensity to malignant progression, strictly related to the persistent neuronal hyper-excitability. Further studies with longer follow-up period are needed to confirm our observations.

Effects of Sublethal Organophosphate Toxicity and Anti-cholinergics on Electroencephalogram and Respiratory Mechanics in Mice

Organophosphates are used in agriculture as insecticides but are potentially toxic to humans when exposed at high concentrations. The mechanism of toxicity is through antagonism of acetylcholinesterase, which secondarily causes excess activation of cholinergic receptors leading to seizures, tremors, respiratory depression, and other physiological consequences. Here we investigated two of the major pathophysiological effects, seizures and respiratory depression, using subcutaneous injection into mice of the organophosphate diisopropylfluorophosphate (DFP) at sublethal concentrations (2.1 mg/Kg) alone and co-injected with current therapeutics atropine (50 mg/Kg) or acetylcholinesterase reactivator HI6 (3 mg/Kg). We also tested a non-specific cholinergic antagonist dequalinium chloride (2 mg/Kg) as a novel treatment for organophosphate toxicity. Electroencephalogram (EEG) recordings revealed that DFP causes focal delta frequency (average 1.4 Hz) tonic spikes in the parietal region that occur transiently (lasting an average of 171 ± 33 min) and a more sustained generalized theta frequency depression in both parietal and frontal electrode that did not recover the following 24 h. DFP also caused behavioral tremors that partially recovered the following 24 h. Using whole body plethysmography, DFP revealed acute respiratory depression, including reduced breathing rates and tidal volumes, that partially recover the following day. Among therapeutic treatments, dequalinium chloride had the most potent effect on all physiological parameters by reducing acute EEG abnormalities and promoting a full recovery after 24 h from tremors and respiratory depression. Atropine and HI6 had distinct effects on EEGs. Co-treatment with atropine converted the acute 1.4 Hz tonic spikes to 3 Hz tonic spikes in the parietal electrode and promoted a partial recovery after 24 h from theta frequency and respiratory depression. HI6 fully removed the parietal delta spike increase and promoted a full recovery in theta frequency and respiratory depression. In summary, while all anticholinergic treatments promoted survival and moderated symptoms of DFP toxicity, the non-selective anti-cholinergic dequalinium chloride had the most potent therapeutic effects in reducing EEG abnormalities, moderating tremors and reducing respiratory depression.

Multimodal Characterization of Seizures in Zebrafish Larvae

Epilepsy accounts for a significant proportion of the world's disease burden. Indeed, many research efforts are produced both to investigate the basic mechanism ruling its genesis and to find more effective therapies. In this framework, the use of zebrafish larvae, owing to their peculiar features, offers a great opportunity. Here, we employ transgenic zebrafish larvae expressing GCaMP6s in all neurons to characterize functional alterations occurring during seizures induced by pentylenetetrazole. Using a custom two-photon light-sheet microscope, we perform fast volumetric functional imaging of the entire larval brain, investigating how different brain regions contribute to seizure onset and propagation. Moreover, employing a custom behavioral tracking system, we outline the progressive alteration of larval swim kinematics, resulting from different grades of seizures. Collectively, our results show that the epileptic larval brain undergoes transitions between diverse neuronal activity regimes. Moreover, we observe that different brain regions are progressively recruited into the generation of seizures of diverse severity. We demonstrate that midbrain regions exhibit highest susceptibility to the convulsant effects and that, during periods preceding abrupt hypersynchronous paroxysmal activity, they show a consistent increase in functional connectivity. These aspects, coupled with the hub-like role that these regions exert, represent important cues in their identification as epileptogenic hubs.

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Objective

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls.

Methods

This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve.

Results

The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction.

Conclusions

Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

Differential Electrographic Signatures Generated by Mechanistically-Diverse Seizurogenic Compounds in the Larval Zebrafish Brain

We assessed similarities and differences in the electrographic signatures of local field potentials (LFPs) evoked by different pharmacological agents in zebrafish larvae. We then compared and contrasted these characteristics with what is known from electrophysiological studies of seizures and epilepsy in mammals, including humans. Ultimately, our aim was to phenotype neurophysiological features of drug-induced seizures in larval zebrafish for expanding knowledge on the translational potential of this valuable alternative to mammalian models. LFPs were recorded from the midbrain of 4-d-old zebrafish larvae exposed to a pharmacologically diverse panel of seizurogenic compounds, and the outputs of these recordings were assessed using frequency domain analysis. This included analysis of changes occurring within various spectral frequency bands of relevance to mammalian CNS circuit pathophysiology. From these analyses, there were clear differences in the frequency spectra of drug-exposed LFPs, relative to controls, many of which shared notable similarities with the signatures exhibited by mammalian CNS circuits. These similarities included the presence of specific frequency components comparable to those observed in mammalian studies of seizures and epilepsy. Collectively, the data presented provide important information to support the value of larval zebrafish as an alternative model for the study of seizures and epilepsy. These data also provide further insight into the electrophysiological characteristics of seizures generated in nonmammalian species by the action of neuroactive drugs.

Fast whole-brain imaging of seizures in zebrafish larvae by two-photon light-sheet microscopy

Light-sheet fluorescence microscopy (LSFM) enables real-time whole-brain functional imaging in zebrafish larvae. Conventional one-photon LSFM can however induce undesirable visual stimulation due to the use of visible excitation light. The use of two-photon (2P) excitation, employing near-infrared invisible light, provides unbiased investigation of neuronal circuit dynamics. However, due to the low efficiency of the 2P absorption process, the imaging speed of this technique is typically limited by the signal-to-noise-ratio. Here, we describe a 2P LSFM setup designed for non-invasive imaging that enables quintuplicating state-of-the-art volumetric acquisition rate of the larval zebrafish brain (5 Hz) while keeping low the laser intensity on the specimen. We applied our system to the study of pharmacologically-induced acute seizures, characterizing the spatial-temporal dynamics of pathological activity and describing for the first time the appearance of caudo-rostral ictal waves (CRIWs).

In vitro Oscillation Patterns Throughout the Hippocampal Formation in a Rodent Model of Epilepsy

Specific oscillatory patterns are considered biomarkers of pathological neuronal network in brain diseases, such as epilepsy. However, the dynamics of underlying oscillations during the epileptogenesis throughout the hippocampal formation in the temporal lobe epilepsy is not clear. Here, we characterized in vitro oscillatory patterns within the hippocampal formation of epileptic rats, under 4-aminopyridine (4-AP)-induced hyperexcitability and during the spontaneous network activity, at two periods of epileptogenesis. First, at the beginning of epileptic chronic phase, 30 days post-pilocarpine-induced Status Epilepticus (SE). Second, at the established epilepsy, 60 days post-SE. The 4-AP-bathed slices from epileptic rats had increased susceptibility to ictogenesis in CA1 at 30 days post-SE, and in entorhinal cortex and dentate gyrus at 60 days post-SE. Higher power and phase coherence were detected mainly for gamma and/or high frequency oscillations (HFOs), in a region- and stage-specific manner. Interestingly, under spontaneous network activity, even without 4-AP-induced hyperexcitability, slices from epileptic animals already exhibited higher power of gamma and HFOs in different areas of hippocampal formation at both periods of epileptogenesis, and higher phase coherence in fast ripples at 60 days post-SE. These findings reinforce the critical role of gamma and HFOs in each one of the hippocampal formation areas during ongoing neuropathological processes, tuning the neuronal network to epilepsy.

Heart rate variability in neonatal seizures: Investigation and implications for management

Many factors acting during the neonatal period can affect neurological development of the infant. Neonatal seizures (NS) that frequently occur in the immature brain may influence autonomic maturation and lead to detectable cardiovascular signs. These autonomic manifestations can also have significant diagnostic and prognostic value. The analysis of Heart Rate Variability (HRV) represents the most used and feasible method to evaluate cardiac autonomic regulation. This narrative review summarizes studies investigating HRV dynamics in newborns with seizures, with the aim of highlighting the potential utility of HRV measures for seizure detection and management. While HRV analysis in critically ill newborns is influenced by many potential confounders, we suggest that it can enhance the ability to better diagnose seizures in the clinical setting. We present potential applications of the analysis of HRV, which could have a useful future role, beyond the research setting.

Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study

Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.

EEG Characteristics to Hyperventilation by Age and Sex in Patients With Various Neurological Disorders

Introduction: Hyperventilation provocation test(s) (HPT) concomitant to electroencephalography (EEG) may detect hidden disorders of the nervous system (CNS). There are various types of abnormal EEG in responses to HPT that provoke different interpretations. However, it is not evident how the onset time of pathological EEG responses to hyperventilation (PERH) reveals dysfunction of the CNS in humans. It is also not clear if age and biological sex affect EEG characteristics in response to HPT. Our previous studies have revealed three types of PERH (disorganization of basic rhythm, paroxysmal discharges, epileptiform activity) concerning the manifestation time of first, second, and third minutes. The current work aims to classify the PERH with regards to age (3–6, 7–12, 13–18, 19–30, 31–50, 50 > year) and the biological sex of the patients.

Methods: This study examined the EEG of 985 outpatients with various functional disorders of the CNS. The patients were assigned to one of three experimental groups based on the time occurrence of PERH in response to the HPT.

Results: The disorganized basic EEG rhythm in the first, second, third minute of HPT was observed across all age and sex groups. All three types of PERH in the first minute were comparable for both sexes. However, some discrepancies between females compared to males were observed in the second and third minutes. All three types of PERH in the first and the second minutes were found only in women. The second type of PERH has revealed at the second minute of PHT in 13–18-year-old five girls.

Conclusion: The three main types of PERH were detected at the first minute in all age groups and sex in patients with various CNS dysfunctions. It is diagnostically informative should be used as a marker during the monitoring of treatment. The specific activity of the brain's response to HPT depends on time, age, sex. The data indicate that taking into account sex differences and age during HPT leads to better results. The sensitivity and severity of the NS reaction toward hypocapnia, stress, and emotion increase in women. Therefore, in such cases should not be recommended to expand functional loads.

Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations

Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.

Long-term outcome in a noninvasive rat model of birth asphyxia with neonatal seizures: Cognitive impairment, anxiety, epilepsy, and structural brain alterations

OBJECTIVE

Birth asphyxia is a major cause of hypoxic-ischemic encephalopathy (HIE) in neonates and often associated with mortality, neonatal seizures, brain damage, and later life motor, cognitive, and behavioral impairments and epilepsy. Preclinical studies on rodent models are needed to develop more effective therapies for preventing HIE and its consequences. Thus far, the most popular rodent models have used either exposure of intact animals to hypoxia-only, or a combination of hypoxia and carotid occlusion, for the induction of neonatal seizures and adverse outcomes. However, such models lack systemic hypercapnia, which is a fundamental constituent of birth asphyxia with major effects on neuronal excitability. Here, we use a recently developed noninvasive rat model of birth asphyxia with subsequent neonatal seizures to study later life adverse outcome.

METHODS

Intermittent asphyxia was induced for 30 min by exposing male and female postnatal day 11 rat pups to three 7 + 3-min cycles of 9% and 5% O₂ at constant 20% CO₂ . All pups exhibited convulsive seizures after asphyxia. A set of behavioral tests were performed systematically over 14 months following asphyxia, that is, a large part of the rat's life span. Video-electroencephalographic (EEG) monitoring was used to determine whether asphyxia led to the development of epilepsy. Finally, structural brain alterations were examined.

RESULTS

The animals showed impaired spatial learning and memory and increased anxiety when tested at an age of 3-14 months. Video-EEG at ~10 months showed an abundance of spontaneous seizures, which was paralleled by neurodegeneration in the hippocampus and thalamus, and by aberrant mossy fiber sprouting.

SIGNIFICANCE

The present model of birth asphyxia recapitulates several of the later life consequences associated with human HIE. This model thus allows evaluation of the efficacy of novel therapies designed to prevent HIE and seizures following asphyxia, and of how such therapies might alleviate long-term adverse consequences.

Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography

Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electroencephalography has been known to be effective for the detection of seizure liability, no established methods are available for the detection of seizure auras. We developed a method for detecting seizure auras through machine learning using frequency-characteristic images of electroencephalograms. Histograms of frequency-intensity distribution prepared from electroencephalograms of rats analyzed during seizures induced with 4-aminopyridine (6 mg/kg), strychnine (3 mg/kg), and pilocarpine (400 mg/kg), were used to create an artificial intelligence (AI) system that learned the features of frequency-characteristic images during seizures. The AI system detected seizure states learned in advance with 100% accuracy induced even by convulsants acting through different mechanisms, and the risk of seizure before a seizure was detected in general observation. The developed AI system determined that the unlearned convulsant Tramadol (150 mg/kg) was the risk of seizure and the negative compounds aspirin and vehicle were negative. Moreover, the AI system detected seizure liability even in electroencephalography data associated with the use of 4-aminopyridine (3 mg/kg), strychnine (1 mg/kg), and pilocarpine (150 mg/kg), which did not induce seizures detectable in general observation. These results suggest that the AI system developed herein is an effective means for electroencephalographic detection of seizure auras, raising expectations for its practical use as a new analytical method that allows for the sensitive detection of seizure liability of drugs that has been overlooked previously in preclinical studies.

Amygdaloid complex anatomopathological findings in animal models of status epilepticus

Temporal lobe epileptic seizures are one of the most common and well-characterized types of epilepsies. The current knowledge on the pathology of temporal lobe epilepsy relies strongly on studies of epileptogenesis caused by experimentally induced status epilepticus (SE). Although several temporal lobe structures have been implicated in the epileptogenic process, the hippocampal formation is the temporal lobe structure studied in the greatest amount and detail. However, studies in human patients and animal models of temporal lobe epilepsy indicate that the amygdaloid complex can be also an important seizure generator, and several pathological processes have been shown in the amygdala during epileptogenesis. Therefore, in the present review, we systematically selected, organized, described, and analyzed the current knowledge on anatomopathological data associated with the amygdaloid complex during SE-induced epileptogenesis. Amygdaloid complex participation in the epileptogenic process is evidenced, among others, by alterations in energy metabolism, circulatory, and fluid regulation, neurotransmission, immediate early genes expression, tissue damage, cell suffering, inflammation, and neuroprotection. We conclude that major efforts should be made in order to include the amygdaloid complex as an important target area for evaluation in future research on SE-induced epileptogenesis. This article is part of the Special Issue "NEWroscience 2018".

Peripheral blood mononuclear cell activation sustains seizure activity

OBJECTIVE

The influx of immune cells and serum proteins from the periphery into the brain due to a dysfunctional blood-brain barrier (BBB) has been proposed to contribute to the pathogenesis of seizures in various forms of epilepsy and encephalitis. We evaluated the pathophysiological impact of activated peripheral blood mononuclear cells (PBMCs) and serum albumin on neuronal excitability in an in vitro brain preparation.

METHODS

A condition of mild endothelial activation induced by arterial perfusion of lipopolysaccharide (LPS) was induced in the whole brain preparation of guinea pigs maintained in vitro by arterial perfusion. We analyzed the effects of co-perfusion of human recombinant serum albumin with human PBMCs activated with concanavalin A on neuronal excitability, BBB permeability (measured by FITC-albumin extravasation), and microglial activation.

RESULTS

Bioplex analysis in supernatants of concanavalin A-stimulated PBMCs revealed increased levels of several inflammatory mediators, in particular interleukin (IL)-1β, tumor necrosis factor (TNF)-α, interferon (INF)-γ, IL-6, IL-10, IL-17A, and MIP3α. LPS and human albumin arterially co-perfused with either concanavalin A-activated PBMCs or the cytokine-enriched supernatant of activated PBMCs (1) modulated calcium-calmodulin-dependent protein kinase II at excitatory synapses, (2) enhanced BBB permeability, (3) induced microglial activation, and (4) promoted seizure-like events. Separate perfusions of either nonactivated PBMCs or concanavalin A-activated PBMCs without LPS/human albumin (hALB) failed to induce inflammatory and excitability changes.

SIGNIFICANCE

Activated peripheral immune cells, such as PBMCs, and the extravasation of serum proteins in a condition of BBB impairment contribute to seizure generation.

Prediction of Seizure Recurrence. A Note of Caution

Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.

Differential acute impact of therapeutically effective and overdose concentrations of lithium on human neuronal single cell and network function

Lithium salts are used as mood-balancing medication prescribed to patients suffering from neuropsychiatric disorders, such as bipolar disorder and major depressive disorder. Lithium salts cross the blood-brain barrier and reach the brain parenchyma within few hours after oral application, however, how lithium influences directly human neuronal function is unknown. We applied patch-clamp and microelectrode array technology on human induced pluripotent stem cell (iPSC)-derived cortical neurons acutely exposed to therapeutic (<1 mM) and overdose concentrations (>1 mM) of lithium chloride (LiCl) to assess how therapeutically effective and overdose concentrations of LiCl directly influence human neuronal electrophysiological function at the synapse, single-cell, and neuronal network level. We describe that human iPSC-cortical neurons exposed to lithium showed an increased neuronal activity under all tested concentrations. Furthermore, we reveal a lithium-induced, concentration-dependent, transition of regular synchronous neuronal network activity using therapeutically effective concentration (<1 mM LiCl) to epileptiform-like neuronal discharges using overdose concentration (>1 mM LiCl). The overdose concentration lithium-induced epileptiform-like activity was similar to the epileptiform-like activity caused by the GABA_A-receptor antagonist. Patch-clamp recordings reveal that lithium reduces action potential threshold at all concentrations, however, only overdose concentration causes increased frequency of spontaneous AMPA-receptor mediated transmission. By applying the AMPA-receptor antagonist and anti-epileptic drug Perampanel, we demonstrate that Perampanel suppresses lithium-induced epileptiform-like activity in human cortical neurons. We provide insights in how therapeutically effective and overdose concentration of lithium directly influences human neuronal function at synapse, a single neuron, and neuronal network levels. Furthermore, we provide evidence that Perampanel suppresses pathological neuronal discharges caused by overdose concentrations of lithium in human neurons.

Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network

Background: Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS). Methods: Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. Accuracy of seizure identification was tested in two scenarios: patients with seizures occurring following a period of chronic recording (scenario 1) and patients with seizures occurring immediately following implantation (scenario 2). The accuracy of the CNN in identifying RNS-recorded iEEG ictal patterns was evaluated against human neurophysiology expertise. Statistical performance was assessed via the area-under-precision-recall curve (AUPRC). Results: In scenario 1, the CNN achieved a maximum mean binary classification AUPRC of 0.84 ± 0.19 (95%CI, 0.72-0.93) and mean regression accuracy of 6.3 ± 1.0 s (95%CI, 4.3-8.5 s) at 30 seed samples. In scenario 2, maximum mean AUPRC was 0.80 ± 0.19 (95%CI, 0.68-0.91) and mean regression accuracy was 6.3 ± 0.9 s (95%CI, 4.8-8.3 s) at 20 seed samples. We obtained near-maximum accuracies at seed size of 10 in both scenarios. CNN classification failures can be explained by ictal electro-decrements, brief seizures, single-channel ictal patterns, highly concentrated interictal activity, changes in the sleep-wake cycle, and progressive modulation of electrographic ictal features. Conclusions: We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient's seizures.

Etiology and Clinical Impact of Interictal Periodic Discharges on the Routine Outpatient Scalp EEG

PURPOSE

Periodic discharges (PDs) are common in acute structural or metabolic brain lesions, but their occurrence during follow-up of epileptic patients in an outpatient setting is rare. Aim of this article was to study whether PDs on the routine outpatient scalp EEG of patients with epilepsy, as compared with nonperiodic epileptiform discharges, are associated with drug refractoriness and the decompensation of epilepsy and particular etiologies.

METHODS

A retrospective case-control study. EEG reports were screened for PDs and their variants. The inclusion criteria were as follows: a diagnosis of epilepsy, epileptogenic lesion on imaging, or a normal 3-T MRI. Inpatient EEGs or EEGs performed in patients with acute cerebral lesions were excluded. Age- and sex-matched controls presenting with other epileptiform EEG abnormalities were selected, and similar selection criteria were applied.

RESULTS

Forty-one patients with PDs and 82 controls were selected. There were no significant differences between the cases and controls in the rates of epilepsy decompensation at the time of EEG collection or drug refractoriness. Stroke, hippocampal sclerosis, and malformations of cortical development were the most frequent etiologies, without significant differences between the groups.

CONCLUSIONS

By performing a case-control study, the authors have shown that PDs are not a marker of epilepsy decompensation and drug refractoriness and that the finding of PDs is not suggestive of particular epilepsy etiologies, when compared with other epileptiform abnormalities.

Mapping region-specific seizure-like patterns in the in vitro isolated guinea pig brain

Specific neurophysiological seizure patterns in patients with focal epilepsy depend on cerebral location and the underlying neuropathology. Location-specific patterns have been also reported in experimental models. Two focal seizure patterns, named p-type and l-type, typical of neocortical and mesial temporal regions were identified in both patients explored with intracerebral EEG and in animal models. These two patterns were recorded in the olfactory regions and in the entorhinal cortex after either 4AP or BMI administration. Here we mapped epileptiform activities in other cortices to verify the existence of specific epileptiform patterns. Field potentials were simultaneously recorded at multiple locations in olfactory, limbic and neocortical regions of the isolated guinea pig brain after arterial administration of either 4AP or BMI. Most neocortical areas did not generate new distinctive focal seizure-like event (SLE), beside the p-type and l-type patterns. Spiking activity was typically recorded after BMI in all new analyzed regions, whereas SLEs were commonly observed during 4AP perfusion. We confirmed the presence of reproducible region-specific epileptiform patterns in all explored cortical areas and demonstrated that strongly inter-connected areas generate similar SLEs. Our study suggests that p- and l-type SLE represent the most common focal seizure patterns during acute manipulations with pro-epileptic compounds.

Deletion of the Na-K-2Cl cotransporter NKCC1 results in a more severe epileptic phenotype in the intrahippocampal kainate mouse model of temporal lobe epilepsy

Increased neuronal expression of the Na-K-2Cl cotransporter NKCC1 has been implicated in the generation of seizures and epilepsy. However, conclusions from studies on the NKCC1-specific inhibitor, bumetanide, are equivocal, which is a consequence of the multiple potential cellular targets and poor brain penetration of this drug. Here, we used Nkcc1 knockout (KO) and wildtype (WT) littermate control mice to study the ictogenic and epileptogenic effects of intrahippocampal injection of kainate. Kainate (0.23 μg in 50 nl) induced limbic status epilepticus (SE) in both KO and WT mice with similar incidence, latency to SE onset, and SE duration, but the number of intermittent generalized convulsive seizures during SE was significantly higher in Nkcc1 KO mice, indicating increased SE severity. Following SE, spontaneous recurrent seizures (SRS) were recorded by continuous (24/7) video/EEG monitoring at 0-1, 4-5, and 12-13 weeks after kainate, using depth electrodes in the ipsilateral hippocampus. Latency to onset of electrographic SRS and the incidence of electrographic SRS were similar in WT and KO mice. However, the frequency of electrographic seizures was lower whereas the frequency of electroclinical seizures was higher in Nkcc1 KO mice, indicating a facilitated progression from electrographic to electroclinical seizures during chronic epilepsy, and a more severe epileptic phenotype, in the absence of NKCC1. The present findings suggest that NKCC1 is dispensable for the induction, progression and manifestation of epilepsy, and they do not support the widely held notion that inhibition of NKCC1 in the brain is a useful strategy for preventing or modifying epilepsy.

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

•

Complexity measures are formulated to enhance classical time-domain statistics of EEG

•

The detection algorithm does not need ad-hoc data preprocessing to address artifacts

•

Focal seizures are detected 95% of the time with less than four false alarms per day

•

The approach offers a visual representation of a seizure as a time-evolving network

Hippocampal low-frequency stimulation prevents seizure generation in a mouse model of mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy (MTLE) is the most common form of focal, pharmacoresistant epilepsy in adults and is often associated with hippocampal sclerosis. Here, we established the efficacy of optogenetic and electrical low-frequency stimulation (LFS) in interfering with seizure generation in a mouse model of MTLE. Specifically, we applied LFS in the sclerotic hippocampus to study the effects on spontaneous subclinical and evoked generalized seizures. We found that stimulation at 1 Hz for 1 hr resulted in an almost complete suppression of spontaneous seizures in both hippocampi. This seizure-suppressive action during daily stimulation remained stable over several weeks. Furthermore, LFS for 30 min before a pro-convulsive stimulus successfully prevented seizure generalization. Finally, acute slice experiments revealed a reduced efficacy of perforant path transmission onto granule cells upon LFS. Taken together, our results suggest that hippocampal LFS constitutes a promising approach for seizure control in MTLE.

Abnormalities of Cortical Sources of Resting State Delta Electroencephalographic Rhythms Are Related to Epileptiform Activity in Patients With Amnesic Mild Cognitive Impairment Not Due to Alzheimer's Disease

In the present exploratory and retrospective study, we hypothesized that cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms might be more abnormal in patients with epileptiform EEG activity (spike-sharp wave discharges, giant spikes) and amnesic mild cognitive impairment not due to Alzheimer's disease (noADMCI-EEA) than matched noADMCI patients without EEA (noADMCI-noEEA). Clinical, neuroimaging, neuropsychological, and rsEEG data in 32 noADMCI and 30 normal elderly (Nold) subjects were available in a national archive. Age, gender, and education were carefully matched among them. No subject had received a clinical diagnosis of epilepsy. Individual alpha frequency peak (IAF) was used to determine the delta, theta, and alpha frequency bands of rsEEG rhythms. Fixed beta and gamma bands were also considered. Regional rsEEG cortical sources were estimated by eLORETA freeware. Area under receiver operating characteristic (AUROC) curves indexed the accuracy of eLORETA solutions in the classification between noADMCI-EEA and noADMCI-noEEA individuals. As novel findings, EEA was observed in 41% of noADMCI patients. Furthermore, these noADMCI-EEA patients showed higher temporal delta source activities as compared to noADMCI-no EEA patients and Nold subjects. Those activities discriminated individuals of the two NoADMCI groups with an accuracy of about 70%. The significant percentage of noADMCI-EEA patients showing EEA and marked abnormalities in temporal rsEEG rhythms at delta frequencies suggest a substantial role of underlying neural hypersynchronization mechanisms in their brain dysfunctions.