Neuronal networks in epileptic encephalopathies with CSWS

Individualized morphological covariation network aberrance associated with seizure relapse after antiseizure medication withdrawal

This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups. Relative to the Control, the SF-group exhibited lower local efficiency, while the SR-group displayed lower global and local efficiency and longer characteristic path length. Both patient groups displayed reduced centrality in certain subcortical and cortical nodes than the Control, with a more pronounced reduction in the SR-group. Additionally, the SR-group exhibited lower centrality of the right pallidum than the SF-group. Decreased subcortical-cortical connectivity was found in both patient groups than the Control, with a more extensive decrease in the SR-group. Furthermore, an edge connecting the right pallidum and left middle temporal gyrus exhibited decreased connectivity in the SR-group than in the SF-group. A weaker small-worldization network upon medication withdrawal, potentially underpinned by node decentralization and subcortical-cortical decoupling, may elevate the risk of seizure relapse.

Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning

Introduction

Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.

Method

The study proposes a novel EEG-based classification approach, focusing on effective connectivity (EC) derived from resting-state EEG signals in combination with support vector machine (SVM) algorithms. EC estimation is performed using the partial directed coherence (PDC) technique. The analysis is conducted on an EEG dataset comprising 35 individuals with AUD and 35 healthy controls (HCs). The methodology evaluates the efficacy of connectivity features in distinguishing between AUD and HC and subsequently develops and assesses an EEG classification technique using EC matrices and SVM.

Result

The proposed methodology demonstrated promising performance, achieving a peak accuracy of 94.5% and an area under the curve (AUC) of 0.988, specifically using frequency bands 29, 36, 45, 46, and 52. Additionally, feature reduction techniques applied to the PDC adjacency matrices in the gamma band further improved classification outcomes. The SVM-based classification achieved an accuracy of 96.37 ± 0.45%, showcasing enhanced performance through the utilization of reduced PDC adjacency matrices.

Discussion

These results highlight the potential of the developed algorithm as a robust diagnostic tool for AUD detection, enhancing precision beyond subjective methods. Incorporating EC features derived from EEG signals can inform tailored treatment strategies, contributing to improved management of AUD.

Hypotheses of pathophysiological mechanisms in epileptic encephalopathies: A review

INTRODUCTION

Epileptic encephalopathy (EE) is a serious clinical issue that manifests as part of developmental and epileptic encephalopathy (DEE), particularly in childhood epilepsy. In EE, neurocognitive functions and behavior are impaired by intense epileptiform electroencephalogram (EEG) activity. Hypotheses of pathophysiological mechanisms behind EE are reviewed to contribute to an effective solution for EE.

REVIEW

Current hypotheses are as follows: 1) neuronal dysfunction based on genetic abnormalities that may affect neurocognitive functions and epilepsy separately; 2) impairment of synaptic homeostasis during sleep that may be responsible for DEE/EE with spike-and-wave activation in sleep; 3) abnormal subcortical regulation of the cerebral cortex; 4) abnormal cortical metabolism and hemodynamics with impairment of the neural network including default mode network; 5) neurotransmitter imbalance and disordered neural excitability; 6) the effects of neuroinflammation that may be caused by epileptic seizures and in turn aggravate epileptogenesis; 7) the interaction between physiological and pathological high-frequency EEG activity; etc. The causal relationship between epileptiform EEG activity and neurocognitive dysfunctions is small in DEE based on genetic abnormalities and it is largely unestablished in the other hypothetical mechanisms.

CONCLUSION

We have not yet found answers to the question of whether the single-central or multiple derangements are present and what seizures and intense epileptiform EEG abnormalities mean in EE. We need to continue our best efforts in both aspects to elucidate the pathophysiological mechanisms of DEE/EE and further develop epilepsy treatment and precision medicine.

Modulation index predicts the effect of ethosuximide on developmental and epileptic encephalopathy with spike-and-wave activation in sleep

PURPOSE

In developmental and epileptic encephalopathy with spike-and-wave activation in sleep (DEE-SWAS), the thalamocortical network is suggested to play an important role in the pathophysiology of the progression from focal epilepsy to DEE-SWAS. Ethosuximide (ESM) exerts effects by blocking T-type calcium channels in thalamic neurons. With the thalamocortical network in mind, we studied the prediction of ESM effectiveness in DEE-SWAS treatment using phase-amplitude coupling (PAC) analysis.

METHODS

We retrospectively enrolled children with DEE-SWAS who had an electroencephalogram (EEG) recorded between January 2009 and September 2022 and were prescribed ESM at Okayama University Hospital. Only patients whose EEG showed continuous spike-and-wave during sleep were included. We extracted 5-min non-rapid eye movement sleep stage N2 segments from EEG recorded before starting ESM. We calculated the modulation index (MI) as the measure of PAC in pair combination comprising one of two fast oscillation types (gamma, 40-80 Hz; ripples, 80-150 Hz) and one of five slow-wave bands (delta, 0.5-1, 1-2, 2-3, and 3-4 Hz; theta, 4-8 Hz), and compared it between ESM responders and non-responders.

RESULTS

We identified 20 children with a diagnosis of DEE-SWAS who took ESM. Fifteen were ESM responders. Regarding gamma oscillations, significant differences were seen only in MI with 0.5-1 Hz slow waves in the frontal pole and occipital regions. Regarding ripples, ESM responders had significantly higher MI in coupling with all slow waves in the frontal pole region, 0.5-1, 3-4, and 4-8 Hz slow waves in the frontal region, 3-4 Hz slow waves in the parietal region, 0.5-1, 2-3, 3-4, and 4-8 Hz slow waves in the occipital region, and 3-4 Hz slow waves in the anterior-temporal region.

SIGNIFICANCE

High MI in a wider area of the brain may represent the epileptic network mediated by the thalamus in DEE-SWAS and may be a predictor of ESM effectiveness.

Continuous Spike-Waves during Slow Sleep Today: An Update

In the context of childhood epilepsy, the concept of continuous spike-waves during slow sleep (CSWS) includes several childhood-onset heterogeneous conditions that share electroencephalograms (EEGs) characterized by a high frequency of paroxysmal abnormalities during sleep, which have negative effects on the cognitive development and behavior of the child. These negative effects may have the characteristics of a clear regression or of a slowdown in development. Seizures are very often present, but not constantly. The above makes it clear why CSWS have been included in epileptic encephalopathies, in which, by definition, frequent EEG paroxysmal abnormalities have an unfavorable impact on cognitive functions, including socio-communicative skills, causing autistic features, even regardless of the presence of clinically overt seizures. Although several decades have passed since the original descriptions of the electroclinical condition of CSWS, there are still many areas that are little-known and deserve to be further studied, including the EEG diagnostic criteria, the most effective electrophysiological parameter for monitoring the role of the thalamus in CSWS pathogenesis, its long-term evolution, the nosographic location of Landau-Kleffner syndrome, standardized neuropsychological and behavioral assessments, and pharmacological and non-pharmacological therapies.

Functional connectivity of the thalamocortical circuit in patients with seizure relapse after antiseizure medication withdrawal

OBJECTIVE

To characterize the features of thalamocortical functional connectivity during seizure recurrence at the time of antiseizure medication (ASM) withdrawal.

METHODS

Patients with chronic epilepsy who attempted to discontinue medications were prospectively registered and followed up; 19 patients remained seizure-free (SF-group), 18 patients had seizure relapses (SR-group) after ASM withdrawal, and 28 healthy controls were recruited. Resting-state functional magnetic resonance imaging was performed before ASM withdrawal. Thalamus subdivisions were set as seeds to calculate voxelwise functional connectivity. Partial correlation analysis between functional connectivity and clinical variables was performed. A support vector machine was used to assess the predictive ability of the specific functional connectivity for seizure relapse.

RESULTS

The within-group comparison indicated that the SR-group had more extensive functional connectivity than the SF-group; the left inferior pulvinar, left medial pulvinar, and right anterior pulvinar showed a significantly stronger functional connection with the precuneus in the SR-group than in the SF-group (Gaussian random field correction, voxel-level p < .001 and cluster-level p < .05). In the SR-group, a positive correlation was found between the left inferior pulvinar-precuneus connectivity and the active period (r = .46, p = .05), seizure-free period (r = .67, p = .002), and disease duration (r = .53, p = .02), and between the left medial pulvinar-precuneus connectivity and the seizure-free period (r = .58, p = .01). The combination of these thalamocortical connections showed a high predictive ability, with an area under the curve of .92 and accuracy of .90 (p = .01).

SIGNIFICANCE

This study determined distinct features of thalamocortical functional connectivity at the time of ASM withdrawal in patients with and without seizure relapse, showing a potential for predicting seizure outcomes following ASM withdrawal.

Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter

BACKGROUND AND OBJECTIVE

The human brain displays rich and complex patterns of interaction within and among brain networks that involve both cortical and subcortical brain regions. Due to the limited spatial resolution of surface electroencephalography (EEG), EEG source imaging is used to reconstruct brain sources and investigate their spatial and temporal dynamics. The majority of EEG source imaging methods fail to detect activity from subcortical brain structures. The reconstruction of subcortical sources is a challenging task because the signal from these sources is weakened and mixed with artifacts and other signals from cortical sources. In this proof-of-principle study we present a novel EEG source imaging method, the regional spatiotemporal Kalman filter (RSTKF), that can detect deep brain activity.

METHODS

The regional spatiotemporal Kalman filter (RSTKF) is a generalization of the spatiotemporal Kalman filter (STKF), which allows for the characterization of different regional dynamics in the brain. It is based on state-space modeling with spatially heterogeneous dynamical noise variances, since models with spatial and temporal homogeneity fail to describe the dynamical complexity of brain activity. First, RSTKF is tested using simulated EEG data from sources in the frontal lobe, putamen, and thalamus. After that, it is applied to non-averaged interictal epileptic spikes from a presurgical epilepsy patient with focal epileptic activity in the amygdalo-hippocampal complex. The results of RSTKF are compared to those of low-resolution brain electromagnetic tomography (LORETA) and of standard STKF.

RESULTS

Only RSTKF is successful in consistently and accurately localizing the sources in deep brain regions. Additionally, RSTKF shows improved spatial resolution compared to LORETA and STKF.

CONCLUSIONS

RSTKF is a generalization of STKF that allows for accurate, focal, and consistent localization of sources, especially in the deeper brain areas. In contrast to standard source imaging methods, RSTKF may find application in the localization of the epileptogenic zone in deeper brain structures, such as mesial frontal and temporal lobe epilepsies, especially in EEG recordings for which no reliable averaged spike shape can be obtained due to lack of the necessary number of spikes required to reach a certain signal-to-noise ratio level after averaging.

What are the epileptic encephalopathies?

PURPOSE OF REVIEW

To review the evolution of the concept of epileptic encephalopathy during the course of past years and analyze how the current definition might impact on both clinical practice and research.

RECENT FINDINGS

Developmental delay in children with epilepsy could be the expression of the cause, consequence of intense epileptiform activity (seizures and EEG abnormalities), or because of the combination of both factors. Therefore, the current International League Against Epilepsy classification identified three electroclinical entities that are those of developmental encephalopathy, epileptic encephalopathy, and developmental and epileptic encephalopathy (DEE). Many biological pathways could be involved in the pathogenesis of DEEs. DNA repair, transcriptional regulation, axon myelination, metabolite and ion transport, and peroxisomal function could all be involved in DEE. Also, epilepsy and epileptiform discharges might impact on cognition via several mechanisms, although they are not fully understood.

SUMMARY

The correct and early identification of cause in DEE might increase the chances of a targeted treatment regimen. Interfering with neurobiological processes of the disease will be the most successful way in order to improve both the cognitive disturbances and epilepsy that are the key features of DEE.

A qualitative awake EEG score for the diagnosis of continuous spike and waves during sleep (CSWS) syndrome in self-limited focal epilepsy (SFE): A case-control study

PURPOSE

To determine whether awake EEG criteria can differentiate epileptic encephalopathy with continuous spike and waves during sleep (EE-CSWS) at the time of cognitive regression from typical, self-limited focal epilepsy (SFE).

METHODS

This retrospective case-control study was based on the analysis of awake EEGs and included 15 patients with EE-CSWS and 15 age-matched and sex-matched patients with typical SFE. The EEGs were anonymised and scored by four independent readers. The following qualitative and quantitative EEG indices were analysed: slow-wave index (SLWI), spike-wave index (SWI), spike-wave frequency (SWF), long spike-wave clusters (CLSW) and EEG score (between grades 0 and 4). Sensitivity and specificity were assessed using receiver operating characteristic (ROC) curves and their reproducibility with a kappa test.

RESULTS

Based on a highly sensitive cut-off, EE-CSWS patients were 8.4 times more likely than those with SFE to have an SLWI > 6%, 15 times more likely to have an SWI > 10 % and six times more likely to have a CLSW of ≥ 1 s. There was substantial agreement between readers (with kappa values of 0.64, 0.69 and 0.67). EE-CSWS patients were 13 times more likely to have an SWF of > 11 % and 149 times more likely to have an EEG score of ≥ 3 than typical SFE patients. Agreement about these ratings was almost perfect (kappa 0.91 and 0.86).

CONCLUSION

An EEG score of ≥ 3 on a 20-min awake EEG differentiates typical SFE from EE-CSWS at the time of cognitive regression, with good reliability across readers with different levels of expertise.

Sleep slow-wave homeostasis and cognitive functioning in children with electrical status epilepticus in sleep

STUDY OBJECTIVES

Encephalopathy with electrical status epilepticus in sleep (ESES) is characterized by non-rapid eye movement (non-REM)-sleep-induced epileptiform activity and acquired cognitive deficits. The synaptic homeostasis hypothesis describes the process of daytime synaptic potentiation balanced by synaptic downscaling in non-REM-sleep and is considered crucial to retain an efficient cortical network. We aimed to study the overnight decline of slow waves, an indirect marker of synaptic downscaling, in patients with ESES and explore whether altered downscaling relates to neurodevelopmental and behavioral problems.

METHODS

Retrospective study of patients with ESES with at least one whole-night electroencephalogram (EEG) and neuropsychological assessment (NPA) within 4 months. Slow waves in the first and last hour of non-REM-sleep were analyzed. Differences in slow-wave slope (SWS) and overnight slope course between the epileptic focus and non-focus electrodes and relations to neurodevelopment and behavior were analyzed.

RESULTS

A total of 29 patients with 44 EEG ~ NPA combinations were included. Mean SWS decreased from 357 to 327 µV/s (-8%, p < 0.001) across the night and the overnight decrease was less pronounced in epileptic focus than in non-focus electrodes (-5.6% vs. -8.7%, p = 0.003). We found no relation between SWS and neurodevelopmental test results in cross-sectional and longitudinal analyses. Patients with behavioral problems showed less SWS decline than patients without and the difference was most striking in the epileptic focus (-0.9% vs. -8.8%, p = 0.006).

CONCLUSIONS

Slow-wave homeostasis-a marker of synaptic homeostasis-is disturbed by epileptiform activity in ESES. Behavioral problems, but not neurodevelopmental test results, were related to severity of this disturbance.

Cortical Excitability, Synaptic Plasticity, and Cognition in Benign Epilepsy With Centrotemporal Spikes: A Pilot TMS-EMG-EEG Study

PURPOSE

Children with benign epilepsy with centrotemporal spikes have rare seizures emerging from the motor cortex, which they outgrow in adolescence, and additionally may have language deficits of unclear etiology. We piloted the use of transcranial magnetic stimulation paired with EMG and EEG (TMS-EMG, TMS-EEG) to test the hypotheses that net cortical excitability decreases with age and that use-dependent plasticity predicts learning.

METHODS

We assessed language and motor learning in 14 right-handed children with benign epilepsy with centrotemporal spikes. We quantified two TMS metrics of left motor cortex excitability: the resting motor threshold (measure of neuronal membrane excitability) and amplitude of the N100-evoked potential (an EEG measure of GABAergic tone). To test plasticity, we applied 1 Hz repetitive TMS to the motor cortex to induce long-term depression-like changes in EMG- and EEG-evoked potentials.

RESULTS

Children with benign epilepsy with centrotemporal spikes tolerate TMS; no seizures were provoked. Resting motor threshold decreases with age but is elevated above maximal stimulator output for half the group. N100 amplitude decreases with age after controlling for resting motor threshold. Motor cortex plasticity correlates significantly with language learning and at a trend level with motor learning.

CONCLUSIONS

Transcranial magnetic stimulation is safe and feasible for children with benign epilepsy with centrotemporal spikes, and TMS-EEG provides more reliable outcome measures than TMS-EMG in this group because many children have unmeasurably high resting motor thresholds. Net cortical excitability decreases with age, and motor cortex plasticity predicts not only motor learning but also language learning, suggesting a mechanism by which motor cortex seizures may interact with language development.

The epileptic network and cognition: What functional connectivity is teaching us about the childhood epilepsies

Our objective was to summarize and evaluate the rapidly expanding body of literature studying functional connectivity in childhood epilepsy. In the self-limited childhood epilepsies, awareness of cognitive comorbidities has been steadily increasing, and recent advances in our understanding of the network effects of these disorders promise insights into the underlying neurobiology. We reviewed publications addressing functional connectivity in children with epilepsy with an emphasis on studies of children with self-limited childhood epilepsies. The majority of studies have been published in the past 10 years and predominantly examine childhood epilepsy with centrotemporal spikes and childhood absence epilepsy. Cognitive network alterations are commonly observed across the childhood epilepsies. Some of these effects appear to be nonspecific to epilepsy syndrome or even to category of neurological disorder. Other patterns, such as changes in the connectivity of cortical language areas in childhood epilepsy with centrotemporal spikes, provide clues to the underlying cognitive deficits seen in affected children. The literature to date is dominated by general observations of connectivity patterns without a priori hypotheses. These data-driven studies build an important foundation for hypothesis generation and are already providing useful insights into the neuropathology of the childhood epilepsies. Future work should emphasize hypothesis-driven approaches and rigorous clinical correlations to better understand how the knowledge of network alterations can be applied to guidance and treatment for the children in our clinics.

Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity

The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.

[Continuous spike-waves during slow-wave sleep: Experience during 20 years]

INTRODUCTION

Continuous spikes and waves during slow sleep (CSWS) is an EEG pattern that appears during childhood, and is often associated with cognitive impairment. It can appear in the course of epileptic syndromes, as well as in benign epilepsy. The aim of this study is to analyse epidemiological and clinical characteristic of patients with CSWS, in order to describe possible predictive factors in their outcome.

METHODS

A retrospective study was conducted on paediatric patients with CSWS treated in a third-level hospital from November 1997 to November 2017.

RESULTS

The study included 25 patients (68% male), of whom 76% had abnormalities in the neuroimaging or suffered from psychomotor development disorder (secondary CSWS). The rest were healthy, or diagnosed with idiopathic epilepsy. The mean age of onset of CSWS was 6.7 years, but earlier in the secondary CSWS cases. Symptoms were present during the CSWS episode in 72% of cases. All of them were treated with antiepileptic drugs, which were effective in 36%. CSWS stopped in 72%, and remission was longer if the CSWS onset occurred at an older age. One-third (33%) presented with sequelae, mostly cognitive and behavioural alterations. Outcome was poorer in those with secondary CSWS and, in those whose CSWS started at an earlier age and lasted longer.

CONCLUSION

The CSWS pattern, although rare, is still a therapeutic challenge. A close follow-up of the patients with epilepsy is important, especially if associated with cognitive impairment, in order to establish an early diagnosis and treatment.

Network dynamics in the healthy and epileptic developing brain

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

Does Epileptiform Activity Represent a Failure of Neuromodulation to Control Central Pattern Generator-Like Neocortical Behavior?

Rhythmic motor patterns in invertebrates are often driven by specialized “central pattern generators” (CPGs), containing small numbers of neurons, which are likely to be “identifiable” in one individual compared with another. The dynamics of any particular CPG lies under the control of modulatory substances, amines, or peptides, entering the CPG from outside it, or released by internal constituent neurons; consequently, a particular CPG can generate a given rhythm at different frequencies and amplitudes, and perhaps even generate a repertoire of distinctive patterns. The mechanisms exploited by neuromodulators in this respect are manifold: Intrinsic conductances (e.g., calcium, potassium channels), conductance state of postsynaptic receptors, degree of plasticity, and magnitude and kinetics of transmitter release can all be affected. The CPG concept has been generalized to vertebrate motor pattern generating circuits (e.g., for locomotion), which may contain large numbers of neurons – a construct that is sensible, if there is enough redundancy: that is, the large number of neurons consists of only a small number of classes, and the cells within any one class act stereotypically. Here we suggest that CPG and modulator ideas may also help to understand cortical oscillations, normal ones, and particularly transition to epileptiform pathology. Furthermore, in the case illustrated, the mechanism of the transition appears to be an exaggerated form of a normal modulatory action used to influence sensory processing.