Intracranial electroencephalographic seizure-onset patterns: effect of underlying pathology

Systematic review of seizure-onset patterns in stereo-electroencephalography: Current state and future directions

OBJECTIVE

Increasing evidence suggests that the seizure-onset pattern (SOP) in stereo-electroencephalography (SEEG) is important for localizing the "true" seizure onset. Specifically, SOPs with low-voltage fast activity (LVFA) are associated with seizure-free outcome (Engel I). However, several classifications and various terms corresponding to the same pattern have been reported, challenging its use in clinical practice.

METHOD

Following the Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guideline, we performed a systematic review of studies describing SOPs along with accompanying figures depicting the reported SOP in SEEG.

RESULTS

Of 1799 studies, 22 met the selection criteria. Among the various SOPs, we observed that the terminology for low frequency periodic spikes exhibited the most variability, whereas LVFA is the most frequently used term of this pattern. Some SOP terms were inconsistent with standard EEG terminology. Finally, there was a significant but weak association between presence of LVFA and seizure-free outcome.

CONCLUSION

Divergent terms were used to describe the same SOPs and some of these terms showed inconsistencies with the standard EEG terminology. Additionally, our results confirmed the link between patterns with LVFA and seizure-free outcomes. However, this association was not strong.

SIGNIFICANCE

These results underline the need for standardization of SEEG terminology.

Characterizing Seizure-Onset Patterns With the Responsive Neurostimulation System

PURPOSE

The responsive neurostimulation system (RNS) aims to improve seizures by delivering electrical stimulation in response to epileptiform patterns detected by electrocorticograms. Seizure-onset patterns (SOPs) correspond to outcomes in intracranial EEG (IC-EEG), although whether this is true for RNS is unknown. This study characterizes common RNS SOPs and correlates them with seizure outcomes.

METHODS

Among 40 patients with RNS implants, long-episode electrocorticogram characteristics of each patient's seizures were classified by visual analysis as one of the eight patterns previously described in IC-EEG. Correlation between each type of SOP and eventual seizure outcome was analyzed, with ≥50% improvement in a number of patient-reported seizure counts defined as a favorable outcome.

RESULTS

Across 263 LEs analyzed, the most common SOP observed was low-voltage fast activity. There was no difference between the distribution of RNS SOPs and that of IC-EEG SOPs described in the literature (Kolmogorov-Smirnov test, P = 0.98). Additionally, there was no correlation between any particular SOP and favorable outcomes (Fisher's omnibus test, P = 0.997).

CONCLUSION

This initial description of RNS SOPs finds them to be similar to previously described IC-EEG SOPs, which suggests similar prognostic/therapeutic potential. However, we found that RNS efficacy is independent of patient SOP, suggesting that RNS is likely an equally effective treatment for all SOPs. Future research on stimulation parameters for particular RNS SOPs and correlation with IC-EEG SOPs in the same patients would be instrumental in guiding personalized neurostimulation.

Preictal dysfunctions of inhibitory interneurons paradoxically lead to their rebound hyperactivity and to low-voltage-fast onset seizures in Dravet syndrome

Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials, and hippocampal single-unit neuronal activities in Scn1a+/- and Scn1aRH/+ DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single-unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients' EEG, which are consistent with the dysfunctions that we observed in single neurons and that allowed seizure prediction. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Preictal spectral features may be used as predictive seizure biomarkers.

Grading system for assessing the confidence in the epileptogenic zone reported in published studies: A Delphi consensus study

OBJECTIVE

This study was undertaken to develop a standardized grading system based on expert consensus for evaluating the level of confidence in the localization of the epileptogenic zone (EZ) as reported in published studies, to harmonize and facilitate systematic reviews in the field of epilepsy surgery.

METHODS

We conducted a Delphi study involving 22 experts from 18 countries, who were asked to rate their level of confidence in the localization of the EZ for various theoretical clinical scenarios, using different scales. Information provided in these scenarios included one or several of the following data: magnetic resonance imaging (MRI) findings, invasive electroencephalography summary, and postoperative seizure outcome.

RESULTS

The first explorative phase showed an overall interrater agreement of .347, pointing to large heterogeneity among experts' assessments, with only 17% of the 42 proposed scenarios associated with a substantial level of agreement. A majority showed preferences for the simpler scale and single-item scenarios. The successive Delphi voting phases resulted in a majority consensus across experts, with more than two thirds of respondents agreeing on the rating of each of the tested single-item scenarios. High or very high levels of confidence were ascribed to patients with either an Engel class I or class IA postoperative seizure outcome, a well-delineated EZ according to all available invasive EEG (iEEG) data, or a well-delineated focal epileptogenic lesion on MRI. MRI signs of hippocampal sclerosis or atrophy were associated with a moderate level of confidence, whereas a low level was ascribed to other MRI findings, a poorly delineated EZ according to iEEG data, or an Engel class II-IV postoperative seizure outcome.

SIGNIFICANCE

The proposed grading system, based on an expert consensus, provides a simple framework to rate the level of confidence in the EZ reported in published studies in a structured and harmonized way, offering an opportunity to facilitate and increase the quality of systematic reviews and guidelines in the field of epilepsy surgery.

Distributed source modeling of stereoencephalographic measurements of ictal activity

OBJECTIVES

Stereoelectroencephalography (SEEG) can define the epileptogenic zone (EZ). However, SEEG is susceptible to the sampling bias, where no SEEG recording is taken within a circumscribed EZ.

METHODS

Nine patients with medically refractory epilepsy underwent SEEG recording, and brain resection got positive outcomes. Ictal neuronal currents were estimated by distributed source modeling using the SEEG data and individual's anatomical magnetic resonance imaging. Using a retrospective leave-one-out data sub-sampling, we evaluated the sensitivity and specificity of the current estimates using MRI after surgical resection or radio-frequency ablation.

RESULTS

The sensitivity and specificity in detecting the EZ were indistinguishable from either the data from all electrodes or the sub-sampled data (rank sum test: rank sum = 23719, p = 0.13) when at least one remaining electrode contact was no more than 20 mm away.

CONCLUSIONS

The distributed neuronal current estimates of ictal SEEG data can mitigate the challenge of delineating the boundary of the EZ in cases of missing an electrode implanted within the EZ and a required second SEEG exploration.

SIGNIFICANCE

Distributed source modeling can be a tool for clinicians to infer the EZ by allowing for more flexible planning of the electrode implantation route and minimizing the number of electrodes.

A scoping review of seizure onset pattern in SEEG and a proposal for morphological classification

BACKGROUND

Seizure onset pattern (SOP) represents an alteration of electroencephalography (EEG) morphology at the beginning of seizure activity in epilepsy. With stereotactic electroencephalography (SEEG), a method for intracranial EEG evaluation, many morphological SOP classifications have been reported without established consensus. These inconsistent classifications with ambiguous terminology present difficulties to communication among epileptologists.

METHODS

We reviewed SOP in SEEG by searching the PubMed database. Reported morphological classifications and the ambiguous terminology used were collected. After thoroughly reviewing all reports, we reconsidered the definitions of these terms and explored a more consistent and simpler morphological SOP classification.

RESULTS

Of the 536 studies initially found, 14 studies were finally included after screening and excluding irrelevant studies. We reconsidered the definitions of EEG onset, period for determining type of SOP, core electrode and other terms in SEEG. We proposed a more consistent and simpler morphological SOP classification comprising five major types with two special subtypes.

CONCLUSIONS

A scoping review of SOP in SEEG was performed. Our classification may be suitable for describing SOP morphology.

Seizure onset and offset pattern determine the entrainment of the cortex and substantia nigra in the nonhuman primate model of focal temporal lobe seizures

Temporal lobe (TL) epilepsy is the most common form of drug-resistant epilepsy. A major focus of human and animal studies on TLE network has been the limbic circuit and the structures composing the temporal lobe. However, there is also evidence suggesting an active role of the basal ganglia in the propagation and control of temporal lobe seizures. Evidence suggests that the network involved in temporal lobe seizure may depend on their onset and offset pattern but studies on the relationship between the patterns and extralimbic activity are limited. Here, we characterize the involvement of the substantia nigra (SN) and somatosensory cortex (SI) during temporal lobe seizures induced in two nonhuman primates (NHP). The seizure onset and offset patterns were manually classified and spectral power and coherence were calculated. We then analyzed the three first and last seconds of the seizure as well as 3-second segments of recorded in pre-ictal and post-ictal periods and compared the changes based on the seizure onset and offset patterns. Our results demonstrated an involvement of the SN and SI dependent on the seizure onset and offset pattern. We found that seizures with both low amplitude fast activity (LAF) and high amplitude slow activity (HAS) onset patterns were associated with an increase in activity of the SN while the change in activity was limited to LAF seizures in the SI. However, the increase of HPC/SI coherence was similar for both type of onset, while the increase in HPC/SN coherence was specific to the farther-spreading LAF onset pattern. As for the role of the SN in seizure cessation, we observed that the coherence between the HPC/SN was reduced during burst suppression (BS) compared to other termination phases. Additionally, we found that this coherence returned to normal levels after the seizure ended, with no significant difference in post-ictal periods among the three types of seizure offsets. This result suggests that the SN might be involved differently in the termination of the BS seizure pattern. This study constitutes the first demonstration of temporal lobe seizures entraining the SN in the primate brain. Moreover, these findings provide evidence that this entrainment is dependent on the seizure onset pattern and support the hypothesis that the SN might play a role in the maintenance and termination of some specific temporal lobe seizure.

Stereo-EEG features of temporal and frontal lobe seizures with loss of consciousness

The loss of consciousness (LOC) during seizures is one of the most striking features that significantly impact the quality of life, even though the neuronal network involved is not fully comprehended. We analyzed the intracerebral patterns in patients with focal drug-resistant epilepsy, both with and without LOC. We assessed the localization, lateralization, stereo electroencephalography (SEEG) patterns, seizure duration, and the quantification of contacts exhibiting electrical discharge. The degree of LOC was quantified using the Consciousness Seizure Scale. Thirteen patients (40 seizures) with focal drug-resistant epilepsy underwent SEEG. In cases of temporal lobe epilepsy (TLE, 6 patients and 15 seizures), LOC occurred more frequently in seizures with mesial rather than lateral temporal lobe onset. On the other hand, in cases of frontal lobe epilepsy (7 patients; 25 seizures), LOC was associated with pre-frontal onset, a higher number of contacts with epileptic discharge compared to the onset count and longer seizure durations. Our study revealed distinct characteristics during LOC depending on the epileptogenic zone. For temporal lobe seizures, LOC was associated with mesial seizure onset, whereas in frontal lobe epilepsy, seizure with LOC has a significant increase in contact showing epileptiform discharge and a pre-frontal onset. This phenomenon may be correlated with the broad neural network required to maintain consciousness, which can be affected in different ways, resulting in LOC.

Correspondence between scalp-EEG and stereoelectroencephalography seizure-onset patterns in patients with MRI-negative drug-resistant focal epilepsy

OBJECTIVE

Our objective was to evaluate the relationship between scalp-EEG and stereoelectroencephalography (SEEG) seizure-onset patterns (SOP) in patients with MRI-negative drug-resistant focal epilepsy.

METHODS

We analyzed retrospectively 41 patients without visible lesion on brain MRI who underwent video-EEG followed by SEEG. We defined five types of SOPs on scalp-EEG and eight types on SEEG. We examined how various clinical variables affected scalp-EEG SOPs.

RESULTS

The most prevalent scalp SOPs were rhythmic sinusoidal activity (56.8%), repetitive epileptiform discharges (22.7%), and paroxysmal fast activity (15.9%). The presence of paroxysmal fast activity on scalp-EEG was always seen without delay from clinical onset and correlated with the presence of low-voltage fast activity in SEEG (sensitivity = 22.6%, specificity = 100%). The main factor explaining the discrepancy between the scalp and SEEG SOPs was the delay between clinical and scalp-EEG onset. There was a correlation between the scalp and SEEG SOPs when the scalp onset was simultaneous with the clinical onset (p = 0.026). A significant delay between clinical and scalp discharge onset was observed in 25% of patients and featured always with a rhythmic sinusoidal activity on scalp, corresponding to similar morphology of the discharge on SEEG. The presence of repetitive epileptiform discharges on scalp was associated with an underlying focal cortical dysplasia (sensitivity = 30%, specificity = 90%). There was no significant association between the scalp SOP and the epileptogenic zone location (deep or superficial), or surgical outcome.

SIGNIFICANCE

In patients with MRI-negative focal epilepsy, scalp SOP could suggest the SEEG SOP and some etiology (focal cortical dysplasia) but has no correlation with surgical prognosis. Scalp SOP correlates with the SEEG SOP in cases of simultaneous EEG and clinical onset; otherwise, scalp SOP reflects the propagation of the SEEG discharge.

PLAIN LANGUAGE SUMMARY

We looked at the correspondence between the electrical activity recorded during the start of focal seizure using scalp and intracerebral electrodes in patients with no visible lesion on MRI. If there is a fast activity on scalp, it reflects similar activity inside the brain. We found a good correspondence between scalp and intracerebral electrical activity for cases without significant delay between clinical and scalp electrical onset (seen in 75% of the cases we studied). Visualizing repetitive epileptic activity on scalp could suggest a particular cause of the epilepsy: a subtype of brain malformation called focal cortical dysplasia.

Dynamical modulation of hypersynchronous seizure onset with transcranial magneto-acoustic stimulation in a hippocampal computational model

Hypersynchronous (HYP) seizure onset is one of the frequently observed seizure-onset patterns in temporal lobe epileptic animals and patients, often accompanied by hippocampal sclerosis. However, the exact mechanisms and ion dynamics of the transition to HYP seizures remain unclear. Transcranial magneto-acoustic stimulation (TMAS) has recently been proposed as a novel non-invasive brain therapy method to modulate neurological disorders. Therefore, we propose a biophysical computational hippocampal network model to explore the evolution of HYP seizure caused by changes in crucial physiological parameters and design an effective TMAS strategy to modulate HYP seizure onset. We find that the cooperative effects of abnormal glial uptake strength of potassium and excessive bath potassium concentration could produce multiple discharge patterns and result in transitions from the normal state to the HYP seizure state and ultimately to the depolarization block state. Moreover, we find that the pyramidal neuron and the PV+ interneuron in HYP seizure-onset state exhibit saddle-node-on-invariant-circle/saddle homoclinic (SH) and saddle-node/SH at onset/offset bifurcation pairs, respectively. Furthermore, the response of neuronal activities to TMAS of different ultrasonic waveforms revealed that lower sine wave stimulation can increase the latency of HYP seizures and even completely suppress seizures. More importantly, we propose an ultrasonic parameter area that not only effectively regulates epileptic rhythms but also is within the safety limits of ultrasound neuromodulation therapy. Our results may offer a more comprehensive understanding of the mechanisms of HYP seizure and provide a theoretical basis for the application of TMAS in treating specific types of seizures.

Impact of intracranial subclinical seizures on seizure outcomes after SLAH in patients with mesial temporal lobe epilepsy

OBJECTIVE

To investigate the association between subclinical seizures detected on intracranial electroencephalographic (i-SCSs)recordings and mesial temporal sclerosis (MTS), as well as their impact on surgical outcomes of stereotactic laser amygdalohippocampotomy (SLAH).

METHODS

A retrospective review was conducted on 27 patients with drug-resistant mesial temporal lobe epilepsy (MTLE) who underwent SLAH. The number of seizures detected on scalp EEG and iEEG was assessed. Patients were followed for a minimum of 3 years after SLAH.

RESULTS

Of the 1715 seizures recorded from mesial temporal regions, 1640 were identified as i-SCSs. Patients with MTS were associated with favorable short- and long-term surgical outcomes. Patients with MTS had a higher number of i-SCSs compared to patients without MTS. The numbers of i-SCSs were higher in patients with Engel I-II outcomes, but no significant statistical difference was found. However, it was observed that patients with MTS who achieved Engel I-II classification had higher numbers of i-SCSs than patients without MTS (P < 0.05).

CONCLUSION

Patients with MTS exhibited favorable short-term and long-term surgical outcome after SLAH. A higher number of i-SCSs was significantly associated with MTS in patients with MTLE. The number of i-SCSs tended to be higher in patients with Engel Ⅰ-Ⅱ surgical outcomes.

SIGNIFICANCE

The association between i-SCSs, MTS, and surgical outcomes in MTLE patients undergoing SLAH has significant implications for understanding the underlying mechanisms and identifying potential therapeutic targets to enhance surgical outcomes.

Bifurcations and bursting in the Epileptor

The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor's parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.

Stimulation-Induced Seizures in Children Undergoing Stereo-EEG Evaluation

PURPOSE

This study reports our center's initial experience with the use of low-frequency stimulation in provoking stimulation-induced seizures (SIS) in children with drug-resistant epilepsy undergoing stereo-EEG evaluations.

METHODS

This retrospective study enrolled children aged 2 to 18 years with drug-resistant focal epilepsy who underwent stereo-EEG evaluation and extraoperative direct electrical cortical stimulation to elicit seizures. The low-frequency stimulation parameters consisted of biphasic square waveforms at frequency of 1 Hz, pulse width 1 millisecond, current 1 to 3 mA, and train duration of 20 seconds. Various epilepsy-related, imaging, neurophysiology, and surgery-related variables were collected and summarized.

RESULTS

Fourteen children (mean age 13 years; 57.1% girls) were included, 10 of whom had unilateral stereo-EEG coverage. Cortical stimulation for provoking seizures was performed after a median of 5 days after electrode implantation. The median number of electrode-contacts stimulated per patient was 42. Four patients (28.6%) experienced habitual SIS (all extratemporal). The etiology in three patients was focal cortical dysplasia. Interictal high-frequency oscillations at electrode-contacts provoking SIS were observed in three cases (75%). Two of these individuals (50%) had class 1 International League Against Epilepsy seizure outcome at last follow-up, after the resection of the brain regions generating SIS.

CONCLUSIONS

Low-frequency (1-Hz) stimulation could provoke habitual SIS in nearly one-fourth of children with focal epilepsy undergoing stereo-EEG monitoring. This study provides a limited pediatric experience with the low-frequency cortical stimulation and SIS.

Permutation entropy-derived parameters to estimate the epileptogenic zone network

OBJECTIVE

Quantification of the epileptogenic zone network (EZN) most frequently implies analysis of seizure onset. However, important information can also be obtained from the postictal period, characterized by prominent changes in the EZN. We used permutation entropy (PE), a measure of signal complexity, to analyze the peri-ictal stereoelectroencephalography (SEEG) signal changes with emphasis on the postictal state. We sought to determine the best PE-derived parameter (PEDP) for identifying the EZN.

METHODS

Several PEDPs were computed retrospectively on SEEG-recorded seizures of 86 patients operated on for drug-resistant epilepsy: mean baseline preictal entropy, minimum ictal entropy, maximum postictal entropy, the ratio between the maximum postictal and the minimum ictal entropy, and the ratio between the maximum postictal and the baseline preictal entropy. The performance of each biomarker was assessed by comparing the identified epileptogenic contacts or brain regions against the EZN defined by clinical analysis incorporating the Epileptogenicity Index and the connectivity epileptogenicity index methods (EZNc), using the receiver-operating characteristic and precision-recall.

RESULTS

The ratio between the maximum postictal and the minimum ictal entropy (defined as the Permutation Entropy Index [PEI]) proved to be the best-performing PEDP to identify the EZNC . It demonstrated the highest area under the curve (AUC) and F1 score at the contact level (AUC 0.72; F1 0.39) and at the region level (AUC 0.78; F1 0.47). PEI values gradually decreased between the EZN, the propagation network, and the non-involved regions. PEI showed higher performance in patients with slow seizure-onset patterns than in those with fast seizure-onset patterns. The percentage of resected epileptogenic regions defined by PEI was significantly correlated with surgical outcome.

SIGNIFICANCE

PEI is a promising tool to improve the delineation of the EZN. PEI combines ease and robustness in a routine clinical setting with high sensitivity for seizures without fast activity at seizure onset.

Learn how to interpret and use intracranial EEG findings

Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.

Anti-seizure gene therapy for focal cortical dysplasia

Focal cortical dysplasias are a common subtype of malformation of cortical development, which frequently presents with a spectrum of cognitive and behavioural abnormalities as well as pharmacoresistant epilepsy. Focal cortical dysplasia type II is typically caused by somatic mutations resulting in mammalian target of rapamycin (mTOR) hyperactivity, and is the commonest pathology found in children undergoing epilepsy surgery. However, surgical resection does not always result in seizure freedom, and is often precluded by proximity to eloquent brain regions. Gene therapy is a promising potential alternative treatment and may be appropriate in cases that represent an unacceptable surgical risk. Here, we evaluated a gene therapy based on overexpression of the Kv1.1 potassium channel in a mouse model of frontal lobe focal cortical dysplasia. An engineered potassium channel (EKC) transgene was placed under control of a human promoter that biases expression towards principal neurons (CAMK2A) and packaged in an adeno-associated viral vector (AAV9). We used an established focal cortical dysplasia model generated by in utero electroporation of frontal lobe neural progenitors with a constitutively active human Ras homolog enriched in brain (RHEB) plasmid, an activator of mTOR complex 1. We characterized the model by quantifying electrocorticographic and behavioural abnormalities, both in mice developing spontaneous generalized seizures and in mice only exhibiting interictal discharges. Injection of AAV9-CAMK2A-EKC in the dysplastic region resulted in a robust decrease (∼64%) in the frequency of seizures. Despite the robust anti-epileptic effect of the treatment, there was neither an improvement nor a worsening of performance in behavioural tests sensitive to frontal lobe function. AAV9-CAMK2A-EKC had no effect on interictal discharges or behaviour in mice without generalized seizures. AAV9-CAMK2A-EKC gene therapy is a promising therapy with translational potential to treat the epileptic phenotype of mTOR-related malformations of cortical development. Cognitive and behavioural co-morbidities may, however, resist an intervention aimed at reducing circuit excitability.

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI was collected from 52 children with RE who had subsequently undergone ic-EEG and then, if indicated, surgery for seizure control (n = 25). The resting state functional connectomics data were previously independently classified by two expert epileptologists, as indicative of measurement noise, typical resting state network connectivity, or SOZ. An expert knowledge integrated deep network was trained on functional connectomics data to identify SOZ. Expert knowledge integrated with DL showed a SOZ localization accuracy of 84.8 ± 4.5% and F1 score, harmonic mean of positive predictive value and sensitivity, of 91.7 ± 2.6%. Conversely, a DL only model yielded an accuracy of <50% (F1 score 63%). Activations that initiate in gray matter, extend through white matter, and end in vascular regions are seen as the most discriminative expert-identified SOZ characteristics. Integration of expert knowledge of functional connectomics can not only enhance the performance of DL in localizing SOZ in RE but also lead toward potentially useful explanations of prevalent co-activation patterns in SOZ. RE with surgical outcomes and preoperative rs-fMRI studies can yield expert knowledge most salient for SOZ identification.

Differentiation of subclinical and clinical electrographic events in long-term electroencephalographic recordings

OBJECTIVE

With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated electroencephalographic (EEG) recordings covering only selected brain regions attracts considerable interest. In this context, the question arises of whether detected ictal EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, that is, subclinical events.

METHODS

EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring were analyzed. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent, and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20 system EEG recordings and from a subset of two temporal electrode contacts.

RESULTS

Correct separation of clinically manifest and purely electrographic events based on 10-20 system EEG recordings was possible in up to 83.8% of events, depending on the combination of features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy; 74.4% and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, although classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals.

SIGNIFICANCE

A correct classification of subclinical versus clinical EEG events was possible in 74%-83% of events based on full EEG recordings, and in 74%-78% when considering only a subset of two electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome, suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intraindividual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.

Dynamic evolution of the anterior cingulate-insula network during seizures

OBJECTIVES

In physiological situations, the anterior cingulate cortex (ACC) and anterior insular cortex (AIC) are prone to coactivation. The functional connectivity and interaction between ACC and AIC in the context of epilepsy remain unclear. This study aimed to investigate the dynamic coupling between these two brain regions during seizures.

METHODS

Patients who underwent stereoelectroencephalography (SEEG) recording were included in this study. The SEEG data were visually inspected and quantitatively analyzed. The narrowband oscillations and aperiodic components at seizure onset were parameterized. The frequency-specific non-linear correlation analysis was applied to the functional connectivity. The excitation/inhibition ratio (E:I ratio) reflected by the aperiodic slope was performed to evaluate the excitability.

RESULTS

Twenty patients were included in the study, with 10 diagnosed with anterior cingulate epilepsy and 10 with anterior insular epilepsy. In both types of epilepsy, the correlation coefficient (h2 ) between the ACC and AIC at seizure onset exhibited a significantly higher value than that during interictal and preictal periods (p < 0.05). The direction index (D) showed a significant increase at seizure onset, serving as an indicator for the direction of information flow between these two brain regions with up to 90% accuracy. The E:I ratio increased significantly at seizure onset, with the seizure-onset zone (SOZ) demonstrating a more pronounced increase compared to non-SOZ (p < 0.05). For seizures originating from AIC, the E:I ratio was significantly higher in the AIC than in the ACC (p = 0.0364).

CONCLUSIONS

In the context of epilepsy, the ACC and AIC are dynamically coupled during seizures. The functional connectivity and excitability exhibit a significant increase at seizure onset. By analyzing connectivity and excitability, the SOZ in ACC and AIC can be identified. The direction index (D) serves as an indicator for the direction of information flow from SOZ to non-SOZ. Notably, the excitability of SOZ changes more significantly than that of non-SOZ.

Lack of spontaneous typical seizures during intracranial monitoring with stereo-electroencephalography

OBJECTIVE

In the presurgical evaluation of patients with drug-resistant epilepsy (DRE), occasionally, patients do not experience spontaneous typical seizures (STS) during a stereo-electroencephalography (SEEG) study, which limits its effectiveness. We sought to identify risk factors for patients who did not have STS during SEEG and to analyze the clinical outcomes for this particular set of patients.

METHODS

We conducted a retrospective analysis of all patients with DRE who underwent depth electrode implantation and SEEG recordings between January 2013 and December 2018.

RESULTS

SEEG was performed in 155 cases during this period. 11 (7.2%) did not experience any clinical seizures (non-STS group), while 143 experienced at least one patient-typical seizure during admission (STS group). No significant differences were found between STS and non-STS groups in terms of patient demographics, lesional/non-lesional epilepsy ratio, pre-SEEG seizure frequency, number of ASMs used, electrographic seizures or postoperative seizure outcome in those who underwent resective surgery. Statistically significant differences were found in the average number of electrodes implanted (7.0 in the non-STS group vs. 10.2 in STS), days in Epilepsy Monitoring Unit (21.8 vs. 12.8 days) and the number of cases that underwent resective surgery following SEEG (27.3% vs. 60.8%), respectively. The three non-STS patients (30%) who underwent surgery, all had their typical seizures triggered during ECS studies. Three cases were found to have psychogenic non-epileptic seizures. None of the patients in the non-STS group were offered neurostimulation devices. Five of the non-STS patients experienced transient seizure improvement following SEEG.

SIGNIFICANCE

We were unable to identify any factors that predicted lack of seizures during SEEG recordings. Resective surgery was only offered in cases where ECS studies replicated patient-typical seizures. Larger datasets are required to be able to identify factors that predict which patients will fail to develop seizures during SEEG.

Organization of the epileptogenic zone and signal analysis at seizure onset in patients with drug-resistant epilepsy due to focal cortical dysplasia with mTOR pathway gene mutations-An SEEG study

Epilepsy surgery in genetic drug-resistant epilepsy is a debated subject as more histological and molecular data are available. We retrospectively collected data from focal drug-resistant epilepsy patients that underwent stereoelectroencephalography (SEEG) invasive recordings. Patients with nonlesional brain imaging or in whom a first epilepsy surgery failed to control seizures were selected. We computed and displayed the intracranial ictal onset activity pattern on structural imaging. Patients underwent epilepsy gene panel testing, next generation sequencing-NGS. Of 113 patients, 13 underwent genetic testing, and in 6 patients, a mechanistic target of rapamycin pathway gene germline mutation (mTOR) was identified. Brain imaging was nonlesional except for one patient in whom two abnormalities suggestive of focal cortical dysplasia (FCD) were found. Patients underwent tailored brain surgery based on SEEG data, tissue analysis revealed FCD and postsurgical outcome was favorable. Our findings are similar to previous case series suggesting that epilepsy surgery can be a treatment option in patients with mTOR pathway mutation. In patients with mTOR pathway mutation, the postsurgical outcome is favorable if complete resection of the epileptogenic zone is performed. Electrophysiological seizure onset patterns in FCDs associated with mTOR pathway mutations display low-voltage fast activity as previously described.

Differential patterns of very high-frequency oscillations in two seizure types of the pilocarpine-induced TLE model

AIMS

Very high-frequency oscillations (VHFOs, >500 Hz) are considered a highly sensitive biomarker of seizures. We hypothesized that VHFOs may exhibit specificity towards hypersynchronous (HYP) seizures and low-voltage fast (LVF) seizures in temporal lobe epilepsy (TLE).

METHODS

Local field potentials were recorded from the hippocampal network in TLE mice induced by pilocarpine. Subsequently, we analyzed the VHFO features, including their temporal-frequency characteristics and VHFO/theta coupling, during three states: baseline, preictal, and postictal for both HYP- and LVF-seizure groups.

RESULTS

Significant changes in most of the VHFO features were observed during the preictal state in both seizure groups. In the postictal state, VHFO features in the HYP-seizure group exhibited inverse alterations and appeared to align with those observed during baseline conditions. However, such phenomena were not observed after TLE seizures in the LVF-seizure group.

CONCLUSION

Our findings highlight distinct patterns of VHFO feature changes across different states of HYP seizures and LVF seizures. These results suggest that VHFOs could serve as indicative biomarkers for seizure alterations specifically associated with HYP-seizure states.

Optimization of ictal aborting stimulation using the dynamotype taxonomy

Electrical stimulation is an increasingly popular method to terminate epileptic seizures, yet it is not always successful. A potential reason for inconsistent efficacy is that stimuli are applied empirically without considering the underlying dynamical properties of a given seizure. We use a computational model of seizure dynamics to show that different bursting classes have disparate responses to aborting stimulation. This model was previously validated in a large set of human seizures and led to a description of the Taxonomy of Seizure Dynamics and the dynamotype, which is the clinical analog of the bursting class. In the model, the stimulation is realized as an applied input, which successfully aborts the burst when it forces the system from a bursting state to a quiescent state. This transition requires bistability, which is not present in all bursters. We examine how topological and geometric differences in the bistable state affect the probability of termination as the burster progresses from onset to offset. We find that the most significant determining factors are the burster class (dynamotype) and whether the burster has a DC (baseline) shift. Bursters with a baseline shift are far more likely to be terminated due to the necessary structure of their state space. Furthermore, we observe that the probability of termination varies throughout the burster's duration, is often dependent on the phase when it was applied, and is highly correlated to dynamotype. Our model provides a method to predict the optimal method of termination for each dynamotype. These results lead to the prediction that optimization of ictal aborting stimulation should account for seizure dynamotype, the presence of a DC shift, and the timing of the stimulation.

Interictal high-frequency oscillations, spikes, and connectivity profiles: A fingerprint of epileptogenic brain pathologies

OBJECTIVE

Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug-resistant epilepsy patients. Epileptic spikes, high-frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for preresective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies.

METHODS

Interictal awake stereoelectroencephalographic recordings of 33 patients with focal drug-resistant epilepsy with seizure-free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross-correlation, and phase consistency) were computed for neighboring electrode pairs.

RESULTS

Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures, with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy.

SIGNIFICANCE

The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery.

Understanding Focal Seizures in Adults: A Comprehensive Review

Focal or partial seizures are a common neurological disorder affecting adults. This review aims to provide an in-depth understanding of focal seizures in adults, including their classification, clinical presentation, etiology, diagnosis, and management. This article seeks to enhance awareness and knowledge among medical professionals and the general public by exploring the latest research and clinical insights. Standard electroencephalography (EEG) and recordings in presurgical electrode depth in humans provide a clear definition of patterns similar to focal seizures. Models of animals with partial seizures and epilepsy mimic seizure patterns with comparable characteristics. However, the network factors supporting interictal spikes, as well as the start, development, and end of seizures remain obscure. According to recent research, inhibitory networks are heavily implicated at the beginning of seizures, and extracellular potassium alterations help start and maintain seizure continuation. An increase in network synchronization, which may be caused by both excitatory and inhibitory pathways, is correlated with the cessation of a partial seizure. Recent research on temporal lobe focal seizures in human and animal models leads to the hypothesis that the active blocking of subcortical arousal processes brings on unconsciousness. Brainstem, basal forebrain, and thalamic arousal networks' neuronal firing is diminished during focal limbic seizures, and cortical arousal can be recovered when subcortical arousal circuits are engaged. These results suggest that thalamic neurostimulation may be therapeutic to restore arousal and consciousness during and after seizures. Targeted subcortical stimulation may increase arousal and consciousness when current treatments cannot halt seizures, enhancing safety and psychosocial function for epileptic patients. We embark on an investigation into adult focal seizures in this thorough review that goes beyond a cursory knowledge of their clinical symptoms.

Linking epileptic phenotypes and neural extracellular matrix remodeling signatures in mouse models of epilepsy

Epilepsies are multifaceted neurological disorders characterized by abnormal brain activity, e.g. caused by imbalanced synaptic excitation and inhibition. The neural extracellular matrix (ECM) is dynamically modulated by physiological and pathophysiological activity and critically involved in controlling the brain's excitability. We used different epilepsy models, i.e. mice lacking the presynaptic scaffolding protein Bassoon at excitatory, inhibitory or all synapse types as genetic models for rapidly generalizing early-onset epilepsy, and intra-hippocampal kainate injection, a model for acquired temporal lobe epilepsy, to study the relationship between epileptic seizures and ECM composition. Electroencephalogram recordings revealed Bassoon deletion at excitatory or inhibitory synapses having diverse effects on epilepsy-related phenotypes. While constitutive Bsn mutants and to a lesser extent GABAergic neuron-specific knockouts (BsnDlx5/6cKO) displayed severe epilepsy with more and stronger seizures than kainate-injected animals, mutants lacking Bassoon solely in excitatory forebrain neurons (BsnEmx1cKO) showed only mild impairments. By semiquantitative immunoblotting and immunohistochemistry we show model-specific patterns of neural ECM remodeling, and we also demonstrate significant upregulation of the ECM receptor CD44 in null and BsnDlx5/6cKO mutants. ECM-associated WFA-binding chondroitin sulfates were strongly augmented in seizure models. Strikingly, Brevican, Neurocan, Aggrecan and link proteins Hapln1 and Hapln4 levels reliably predicted seizure properties across models, suggesting a link between ECM state and epileptic phenotype.

Localizing epileptogenic zones with high-frequency oscillations and directed connectivity

PURPOSE

Precise localization of the epileptogenic zone (EZ) is essential for epilepsy surgery. Existing methods often fail to detect slow onset patterns or similar neural activities presented in the recorded signals. To address this issue, we propose a new measure to quantify epileptogenicity, i.e., the connectivity high-frequency epileptogenicity index (cHFEI).

METHODS

The cHFEI method combines directed connectivity and high-frequency oscillations (HFOs) to measure the epileptogenicity of regions involved in a brain network. By applying this method to stereoelectroencephalography (SEEG) recordings of 49 seizures in 20 patients, we calculated the accuracy, sensitivity, and precision with a visually identified epileptogenic zone as a reference. The performance was evaluated by the confusion matrix and the area under the receiver operating characteristic (ROC) curve.

RESULTS

Epileptic network estimation based on cHFEI successfully distinguished brain regions involved in seizure onset from the propagation network. Moreover, cHFEI outperformed other existing detection methods in the estimation of EZs in all patients, with an average area under the ROC curve of 0.88 and an accuracy of 0.85.

CONCLUSIONS

cHFEI can characterize EZ in a robust manner despite various seizure onset patterns and has potential application in epilepsy therapy.

Has recording of seizures become obsolete?

Some patients with medically intractable epilepsy are considered for surgical treatment. In some surgical candidates, the investigation includes the placement of intracerebral electrodes and long-term monitoring to find the region of seizure onset. This region is the primary determinant of the surgical resection but about one-third of patients are not offered surgery after electrode implantation and among those operated only about 55% are seizure free after five years. This paper discusses why the primary reliance on the seizure onset maybe suboptimal and may be in part responsible for the relatively low surgical success rate. It also proposes to consider some interictal markers that may have advantages over seizure onset and may be easier to obtain.

CA3 principal cell activation triggers hypersynchronous-onset seizures in a mouse model of mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy (MTLE) is the most common form of focal epilepsy and it is characterized by seizures that are often refractory to medications. Seizures in MTLE have two main patterns of onset that have been termed hypersynchronous (HYP) and low-voltage fast (LVF) and are believed to mainly depend on the activity of excitatory principal cells and inhibitory interneurons, respectively. In this study, we investigated whether unilateral open-loop optogenetic activation of CaMKII-positive principal cells in the hippocampus CA3 region favors the generation of spontaneous HYP seizures in kainic acid-treated (KA) CaMKII-ChR2 mice. Optogenetic activation of CA3 principal cells (1 Hz, 180 s ON, 220 s OFF) was implemented for 15 days after KA-induced status epilepticus. We found that both LVF and HYP seizures occurred in nonstimulated CaMKII-ChR2 (n = 6) and stimulated CaMKII-Cre (n = 5) mice. In contrast, optogenetic activation of principal cells in CaMKII-ChR2 mice (n = 5) triggered only HYP seizures that were characterized by high fast ripple (250-500 Hz) rates during the pre-ictal and ictal periods. These results provide firm evidence that in MTLE spontaneous seizures with different onset patterns depend on distinct neuronal network mechanisms of generation. They also demonstrate that HYP seizures occurring in vivo along with their associated fast ripples depend on the activity of principal cells in the CA3 region.NEW & NOTEWORTHY Previous evidence suggested that different seizure onset patterns rely on the activity of distinct neuronal populations. In this study, we show for the first time that in vivo optogenetic stimulation of CaMKII principal cells in kainic acid-treated mice triggers hypersynchronous-onset seizures that are associated with fast ripples. Our findings indicate that in patients with predominant HYP-onset seizures, anticonvulsant treatments should be aimed at limiting the firing of principal neurons in the seizure onset zone.

The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy

OBJECTIVE

Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics.

METHODS

We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included.

RESULTS

The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51).

INTERPRETATION

In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.

Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy

Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.

Associated factors with stimulation induced seizures and the relevance with surgical outcomes

OBJECTIVE

To analyze the associated factors with stimulation-induced seizures (SIS) and the relevant factors in predicting surgical outcomes.

METHODS

We analyzed 80 consecutive epilepsy patients explored by stereo-electroencephalography with routine electrical stimulation mapping (ESM). If seizures induced by ESM, patients were classified as SIS-positive (SIS-P); otherwise, SIS-negative (SIS-N). Patients received radical surgery were further classified as favorable (Engel I) and unfavorable (Engel II-IV) groups.

RESULTS

Of the 80 patients included, we identified 44 (55.0%) and 36(45.0%) patients in the SIS-P and SIS-N groups, respectively. Multivariate analysis revealed that the seizure onset pattern (SOP) of preceding repetitive epileptiform discharges following LVFA (PRED→LVFA) (OR 3.319, 95% CI 1.200-9.183, P = 0.021) and pathology of focal cortical dysplasia (FCD) type II (OR 3.943, 95% CI 1.093-14.226, P = 0.036) were independent factors influencing whether the electrical stimulation can induce a seizure. Among the patients received radical surgery, there were 55 and 15 patients in the favorable and unfavorable groups separately. Multivariate analysis revealed that the SOP of PRED→LVFA induced seizures by stimulation (OR 11.409, 95% CI 1.182-110.161, P = 0.035) and bilateral implantation (OR 0.048, 95% CI 0.005-0.497, P = 0.011) were independent factors affecting surgical outcomes. The previous epilepsy surgery had a trend to be a negative factor with SIS (OR 0.156, 95% CI 0.028-0.880, P = 0.035) and surgical outcomes (OR 0.253, 95% CI 0.053-1.219, P = 0.087).

CONCLUSION

ESM is a highly valuable method for localizing the seizure onset zone. The SOP of PRED→LVFA and FCD type II were associated with elicitation of SIS by ESM, whereas a previous epilepsy surgery showed a negative association. Furthermore, the SOP of PRED→LVFA together with SIS in the same patient predicted favorable surgical outcomes, whereas bilateral electrode implantation predicted unfavorable outcomes.

Synaptic alterations and neuronal firing in human epileptic neocortical excitatory networks

Epilepsy is a prevalent neurological condition, with underlying neuronal mechanisms involving hyperexcitability and hypersynchrony. Imbalance between excitatory and inhibitory circuits, as well as histological reorganization are relatively well-documented in animal models or even in the human hippocampus, but less is known about human neocortical epileptic activity. Our knowledge about changes in the excitatory signaling is especially scarce, compared to that about the inhibitory cell population. This study investigated the firing properties of single neurons in the human neocortex in vitro, during pharmacological blockade of glutamate receptors, and additionally evaluated anatomical changes in the excitatory circuit in tissue samples from epileptic and non-epileptic patients. Both epileptic and non-epileptic tissues exhibited spontaneous population activity (SPA), NMDA receptor antagonization reduced SPA recurrence only in epileptic tissue, whereas further blockade of AMPA/kainate receptors reversibly abolished SPA emergence regardless of epilepsy. Firing rates did not significantly change in excitatory principal cells and inhibitory interneurons during pharmacological experiments. Granular layer (L4) neurons showed an increased firing rate in epileptic compared to non-epileptic tissue. The burstiness of neurons remained unchanged, except for that of inhibitory cells in epileptic recordings, which decreased during blockade of glutamate receptors. Crosscorrelograms computed from single neuron discharge revealed both mono- and polysynaptic connections, particularly involving intrinsically bursting principal cells. Histological investigations found similar densities of SMI-32-immunopositive long-range projecting pyramidal cells in both groups, and shorter excitatory synaptic active zones with a higher proportion of perforated synapses in the epileptic group. These findings provide insights into epileptic modifications from the perspective of the excitatory system and highlight discrete alterations in firing patterns and synaptic structure. Our data suggest that NMDA-dependent glutamatergic signaling, as well as the excitatory synaptic machinery are perturbed in epilepsy, which might contribute to epileptic activity in the human neocortex.

Expert and deep learning model identification of iEEG seizures and seizure onset times

Hundreds of 90-s iEEG records are typically captured from each NeuroPace RNS System patient between clinic visits. While these records provide invaluable information about the patient's electrographic seizure and interictal activity patterns, manually classifying them into electrographic seizure/non-seizure activity, and manually identifying the seizure onset channels and times is an extremely time-consuming process. A convolutional neural network based Electrographic Seizure Classifier (ESC) model was developed in an earlier study. In this study, the classification model is tested against iEEG annotations provided by three expert reviewers board certified in epilepsy. The three experts individually annotated 3,874 iEEG channels from 36, 29, and 35 patients with leads in the mesiotemporal (MTL), neocortical (NEO), and MTL + NEO regions, respectively. The ESC model's seizure/non-seizure classification scores agreed with the three reviewers at 88.7%, 89.6%, and 84.3% which was similar to how reviewers agreed with each other (92.9%-86.4%). On iEEG channels with all 3 experts in agreement (83.2%), the ESC model had an agreement score of 93.2%. Additionally, the ESC model's certainty scores reflected combined reviewer certainty scores. When 0, 1, 2 and 3 (out of 3) reviewers annotated iEEG channels as electrographic seizures, the ESC model's seizure certainty scores were in the range: [0.12-0.19], [0.32-0.42], [0.61-0.70], and [0.92-0.95] respectively. The ESC model was used as a starting-point model for training a second Seizure Onset Detection (SOD) model. For this task, seizure onset times were manually annotated on a relatively small number of iEEG channels (4,859 from 50 patients). Experiments showed that fine-tuning the ESC models with augmented data (30,768 iEEG channels) resulted in a better validation performance (on 20% of the manually annotated data) compared to training with only the original data (3.1s vs 4.4s median absolute error). Similarly, using the ESC model weights as the starting point for fine-tuning instead of other model weight initialization methods provided significant advantage in SOD model validation performance (3.1s vs 4.7s and 3.5s median absolute error). Finally, on iEEG channels where three expert annotations of seizure onset times were within 1.5 s, the SOD model's seizure onset time prediction was within 1.7 s of expert annotation.

Does seizure propagate within or across intrinsic brain networks? An intracranial EEG study

BACKGROUND

Understanding the spatiotemporal propagation profiles of seizures is crucial for the preoperative assessment of epilepsy patients. The present study aimed to investigate whether seizures exhibit propagation patterns that align with intrinsic networks (INs).

METHODS

A quantitative analysis was conducted to examine ictal fast activity (IFA). The Epileptogenicity Index (EI) was employed to assess the epileptogenicity, spectral features, and temporal characteristics of IFA. Intra-network and inter-network comparisons were made regarding the IFA-related metrics. Additionally, the metrics were correlated with Euclidean distance. Network connection maps were generated to visualize seizures originating from different INs, allowing for comparisons between distinct groups.

RESULTS

Data for 81 seizures in 43 subjects were captured using stereoelectroencephalography implantation. Three metrics were compared: EI, time involvement (TI), and energy ratio index (ERI). Intra-network channels exhibited higher EI, earlier involvement of IFA, and stronger high-frequency energy. These findings were further validated through subgroup analyses stratified by neuropathology, seizure type, and seizure origination lobe. Correlation analyses revealed a negative association between distance and both EI and ERI, while distance exhibited a positive correlation with TI. Seizures originating from different INs exhibited varying propagation characteristics.

CONCLUSIONS

The study findings highlight the dominant role of intra-network dynamics over inter-network during seizure propagation. These results contribute to our understanding of seizure dynamics and their relationship with INs.

Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy

OBJECTIVE

We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state.

METHODS

We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC).

RESULTS

Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs.

CONCLUSIONS

Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs.

SIGNIFICANCE

Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.

Phase-amplitude coupling between low- and high-frequency activities as preoperative biomarker of focal cortical dysplasia subtypes

OBJECTIVE

To evaluate whether ictal phase-amplitude coupling (PAC) between high-frequency activity and low-frequency activity could be used as a preoperative biomarker of Focal Cortical Dysplasia (FCD) subtypes. We hypothesize that FCD seizures present unique PAC characteristics that may be linked to their specific histopathological features.

METHODS

We retrospectively examined 12 children with FCD and refractory epilepsy who underwent successful epilepsy surgery. We identified ictal onsets recorded with stereo-EEG. We estimated the strength of PAC between low-frequencies and high-frequencies for each seizure by means of modulation index. Generalized mixed effect models and receiver operating characteristic (ROC) curve analysis were used to test the association between ictal PAC and FCD subtypes.

RESULTS

Ictal PAC was significantly higher in patients with FCD type II compared to type I, only on SOZ-electrodes (p < 0.005). No differences in ictal PAC were found on non-SOZ electrodes. Pre-ictal PAC registered on SOZ electrodes predicted FCD histopathology with a classification accuracy > 0.9 (p < 0.05).

CONCLUSIONS

The correlations between histopathology and neurophysiology provide evidence for the contribution of ictal PAC as a preoperative biomarker of FCD subtypes.

SIGNIFICANCE

Developed into a proper clinical application, such a technique may help improve clinical management and facilitate the prediction of surgical outcome in patients with FCD undergoing stereo-EEG monitoring.

Quantitative approaches to guide epilepsy surgery from intracranial EEG

Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.

Chronological changes in phase-amplitude coupling during epileptic seizures in temporal lobe epilepsy

OBJECTIVE

To analyze chronological changes in phase-amplitude coupling (PAC) and verify whether PAC analysis can diagnose epileptogenic zones during seizures.

METHODS

We analyzed 30 seizures in 10 patients with mesial temporal lobe epilepsy who had ictal discharges with preictal spiking followed by low-voltage fast activity patterns on intracranial electroencephalography. We used the amplitude of two high-frequency bands (ripples: 80-200 Hz, fast ripples: 200-300 Hz) and the phase of three slow wave bands (0.5-1 Hz, 3-4 Hz, and 4-8 Hz) for modulation index (MI) calculation from 2 minutes before seizure onset to seizure termination. We evaluated the accuracy of epileptogenic zone detection by MI, in which a combination of MI was better for diagnosis and analyzed patterns of chronological changes in MI during seizures.

RESULTS

MI_{Ripples/3-4 Hz} and MI_{Ripples/4-8 Hz} in the hippocampus were significantly higher than those in the peripheral regions from seizure onset. Corresponding to the phase on intracranial electroencephalography, MI_{Ripples/3-4 Hz} decreased once and subsequently increased again. MI_{Ripples/4-8 Hz} showed continuously high values.

CONCLUSIONS

Continuous measurement of MI_{Ripples/3-4 Hz} and MI_{Ripples/4-8 Hz} could help identify epileptogenic zones.

SIGNIFICANCE

PAC analysis of ictal epileptic discharges can help epileptogenic zone identification.

GABAergic circuits drive focal seizures

Epilepsy is based on abnormal neuronal activities that have historically been suggested to arise from an excess of excitation and a defect of inhibition, or in other words from an excessive glutamatergic drive not balanced by GABAergic activity. More recent data however indicate that GABAergic signaling is not defective at focal seizure onset and may even be actively involved in seizure generation by providing excitatory inputs. Recordings of interneurons revealed that they are active at seizure initiation and that their selective and time-controlled activation using optogenetics triggers seizures in a more general context of increased excitability. Moreover, GABAergic signaling appears to be mandatory at seizure onset in many models. The main pro-ictogenic effect of GABAergic signaling is the depolarizing action of GABA_A conductance which may occur when an excessive GABAergic activity causes Cl^- accumulation in neurons. This process may combine with background dysregulation of Cl^-, well described in epileptic tissues. Cl^- equilibrium is maintained by (Na⁺)/K⁺/Cl^- co-transporters, which can be defective and therefore favor the depolarizing effects of GABA. In addition, these co-transporters further contribute to this effect as they mediate K⁺ outflow together with Cl^- extrusion, a process that is responsible for K⁺ accumulation in the extracellular space and subsequent increase of local excitability. The role of GABAergic signaling in focal seizure generation is obvious but its complex dynamics and balance between GABA_A flux polarity and local excitability still remain to be established, especially in epileptic tissues where receptors and ion regulators are disrupted and in which GABAergic signaling rather plays a 2 faces Janus role.

Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.

Focal cortical dysplasia: a practical guide for neurologists

Focal cortical dysplasia (FCD) is a malformation of cortical development characterised by disruption of cortical cytoarchitecture. Classification of FCDs subtypes has initially been based on correlation of the histopathology with relevant clinical, electroencephalographic and neuroimaging features. A recently proposed classification update recommends a multilayered, genotype-phenotype approach, integrating findings from histopathology, genetic analysis of resected tissue and presurgical MRI. FCDs are caused either by single somatic activating mutations in MTOR pathway genes or by double-hit inactivating mutations with a constitutional and a somatic loss-of-function mutation in repressors of the signalling pathway. Mild malformation with oligodendroglial hyperplasia in epilepsy is caused by somatic pathogenic SLC35A2 mutations. FCDs most often present with drug-resistant focal epilepsy or epileptic encephalopathy. Most patients respond to surgical treatment. The use of mechanistic target of rapamycin inhibitors may complement the surgical approach. Treatment approaches and outcomes have improved with advances in neuroimaging, neurophysiology and genetics, although predictors of treatment response have only been determined in part.

Chloride ion dysregulation in epileptogenic neuronal networks

GABA is the major inhibitory neurotransmitter in the mature CNS. When GABA_A receptors are activated the membrane potential is driven towards hyperpolarization due to chloride entry into the neuron. However, chloride ion dysregulation that alters the ionic gradient can result in depolarizing GABAergic post-synaptic potentials instead. In this review, we highlight that GABAergic inhibition prevents and restrains focal seizures but then reexamine this notion in the context of evidence that a static and/or a dynamic chloride ion dysregulation, that increases intracellular chloride ion concentrations, promotes epileptiform activity and seizures. To reconcile these findings, we hypothesize that epileptogenic pathologically interconnected neuron (PIN) microcircuits, representing a small minority of neurons, exhibit static chloride dysregulation and should exhibit depolarizing inhibitory post-synaptic potentials (IPSPs). We speculate that chloride ion dysregulation and PIN cluster activation may generate fast ripples and epileptiform spikes as well as initiate the hypersynchronous seizure onset pattern and microseizures. Also, we discuss the genetic, molecular, and cellular players important in chloride dysregulation which regulate epileptogenesis and initiate the low-voltage fast seizure onset pattern. We conclude that chloride dysregulation in neuronal networks appears to be critical for epileptogenesis and seizure genesis, but feed-back and feed-forward inhibitory GABAergic neurotransmission plays an important role in preventing and restraining seizures as well.

Reliability of visual review of intracranial electroencephalogram in identifying the seizure onset zone: A systematic review and implications for the accuracy of automated methods

Visual review of intracranial electroencephalography (iEEG) is often an essential component for defining the zone of resection for epilepsy surgery. Unsupervised approaches using machine and deep learning are being employed to identify seizure onset zones (SOZs). This prompts a more comprehensive understanding of the reliability of visual review as a reference standard. We sought to summarize existing evidence on the reliability of visual review of iEEG in defining the SOZ for patients undergoing surgical workup and understand its implications for algorithm accuracy for SOZ prediction. We performed a systematic literature review on the reliability of determining the SOZ by visual inspection of iEEG in accordance with best practices. Searches included MEDLINE, Embase, Cochrane Library, and Web of Science on May 8, 2022. We included studies with a quantitative reliability assessment within or between observers. Risk of bias assessment was performed with QUADAS-2. A model was developed to estimate the effect of Cohen kappa on the maximum possible accuracy for any algorithm detecting the SOZ. Two thousand three hundred thirty-eight articles were identified and evaluated, of which one met inclusion criteria. This study assessed reliability between two reviewers for 10 patients with temporal lobe epilepsy and found a kappa of .80. These limited data were used to model the maximum accuracy of automated methods. For a hypothetical algorithm that is 100% accurate to the ground truth, the maximum accuracy modeled with a Cohen kappa of .8 ranged from .60 to .85 (F-2). The reliability of reviewing iEEG to localize the SOZ has been evaluated only in a small sample of patients with methodologic limitations. The ability of any algorithm to estimate the SOZ is notably limited by the reliability of iEEG interpretation. We acknowledge practical limitations of rigorous reliability analysis, and we propose design characteristics and study questions to further investigate reliability.

Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset

Seizures likely result from aberrant network activity and synchronization. Changes in brain network connectivity may underlie seizure onset. We used a novel method of rapid network model estimation from intracranial electroencephalography (iEEG) data to characterize pre-ictal changes in network structure prior to seizure onset. We analyzed iEEG data from 20 patients from the iEEG.org database. Using 10 s epochs sliding by 1 s intervals, a multiple input, single output (MISO) state space model was estimated for each output channel and time point with all other channels as inputs, generating sequential directed network graphs of channel connectivity. These networks were assessed using degree and betweenness centrality. Both degree and betweenness increased at seizure onset zone (SOZ) channels 37.0 ± 2.8 s before seizure onset. Degree rose in all channels 8.2 ± 2.2 s prior to seizure onset, with increasing connections between the SOZ and surrounding channels. Interictal networks showed low and stable connectivity. A novel MISO model-based network estimation method identified changes in brain network structure just prior to seizure onset. Increased connectivity was initially isolated within the SOZ and spread to non-SOZ channels before electrographic seizure onset. Such models could help confirm localization of SOZ regions.

Source-sink connectivity: a novel interictal EEG marker for seizure localization

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Human periventricular nodular heterotopia shows several interictal epileptic patterns and hyperexcitability of neuronal firing

Periventricular nodular heterotopia (PNH) is a malformation of cortical development that frequently causes drug-resistant epilepsy. The epileptogenicity of ectopic neurons in PNH as well as their role in generating interictal and ictal activity is still a matter of debate. We report the first in vivo microelectrode recording of heterotopic neurons in humans. Highly consistent interictal patterns (IPs) were identified within the nodules: (1) Periodic Discharges PLUS Fast activity (PD+F), (2) Sporadic discharges PLUS Fast activity (SD+F), and (3) epileptic spikes (ES). Neuronal firing rates were significantly modulated during all IPs, suggesting that multiple IPs were generated by the same local neuronal populations. Furthermore, firing rates closely followed IP morphologies. Among the different IPs, the SD+F pattern was found only in the three nodules that were actively involved in seizure generation but was never observed in the nodule that did not take part in ictal discharges. On the contrary, PD+F and ES were identified in all nodules. Units that were modulated during the IPs were also found to participate in seizures, increasing their firing rate at seizure onset and maintaining an elevated rate during the seizures. Together, nodules in PNH are highly epileptogenic and show several IPs that provide promising pathognomonic signatures of PNH. Furthermore, our results show that PNH nodules may well initiate seizures.

GABAA signaling, focal epileptiform synchronization and epileptogenesis

Under physiological conditions, neuronal network synchronization leads to different oscillatory EEG patterns that are associated with specific behavioral and cognitive functions. Excessive synchronization can, however, lead to focal or generalized epileptiform activities. It is indeed well established that in both epileptic patients and animal models, focal epileptiform EEG patterns are characterized by interictal and ictal (seizure) discharges. Over the last three decades, employing in vitro and in vivo recording techniques, several experimental studies have firmly identified a paradoxical role of GABA_A signaling in generating interictal discharges, and in initiating—and perhaps sustaining—focal seizures. Here, we will review these experiments and we will extend our appraisal to evidence suggesting that GABA_A signaling may also contribute to epileptogenesis, i.e., the development of plastic changes in brain excitability that leads to the chronic epileptic condition. Overall, we anticipate that this information should provide the rationale for developing new specific pharmacological treatments for patients presenting with focal epileptic disorders such as mesial temporal lobe epilepsy (MTLE).

Recent developments in stereo electroencephalography monitoring for epilepsy surgery

Recently the utilization of the stereo electroencephalography (SEEG) method has exploded globally. It is now the preferred method of intracranial monitoring for epilepsy. Since its inception, the basic tenet of the SEEG method remains the same: strategic implantation of intracerebral electrodes based on a hypothesis grounded on anatomo-electroclinical correlation, interpretation of interictal and ictal abnormalities, and formation of a surgical plan based on these data. However, there are recent advancements in all these domains-electrodes implantations, data interpretation, and therapeutic strategy- that can make the SEEG a more accessible and effective approach. In this narrative review, these newer developments are discussed and summarized. Regarding implantation, efficient commercial robotic systems are now increasingly available, which are also more accurate in implanting electrodes. In terms of ictal and interictal abnormalities, newer studies focused on correlating these abnormalities with pathological substrates and surgical outcomes and analyzing high-frequency oscillations and cortical-subcortical connectivity. These abnormalities can now be further quantified using advanced tools (spectrum, spatiotemporal, connectivity analysis, and machine learning algorithms) for objective and efficient interpretation. Another aspect of recent development is renewed interest in SEEG-based electrical stimulation mapping (ESM). The SEEG-ESM has been used in defining epileptogenic networks, mapping eloquent cortex (primarily language), and analyzing cortico-cortical evoked potential. Regarding SEEG-guided direct therapeutic strategy, several clinical studies evaluated the use of radiofrequency thermocoagulation. As the emerging SEEG-based diagnosis and therapeutics are better evolved, treatments aimed at specific epileptogenic networks without compromising the eloquent cortex will become more easily accessible to improve the lives of individuals with drug-resistant epilepsy (DRE).