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

OBJECTIVE: Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity. METHODS: We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification. RESULTS: Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling. SIGNIFICANCE: The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.

Effects of midazolam on high-frequency oscillations in amygdala and hippocampus of epilepsy patients.

High-frequency oscillations (HFOs) are associated with normal brain function, but are also increasingly recognized as potential biomarkers of epileptogenic tissue. Considering the important role of interneuron activity in physiological HFO generation, we studied their modulation by midazolam (MDZ), an agonist of γ-aminobutyric acid type A (GABAA)-benzodiazepine receptors. Here, we analyzed 80 intracranial electrode contacts in amygdala and hippocampus of 13 patients with drug-refractory focal epilepsy who had received MDZ for seizure termination during presurgical monitoring. Ripples (80-250 Hz) and fast ripples (FRs; 250-400 Hz) were compared before and after seizures with MDZ application, and according to their origin either within or outside the individual seizure onset zone (SOZ). We found that MDZ distinctly suppressed all HFOs (ripples and FRs), whereas the reduction of ripples was significantly less pronounced inside the SOZ compared to non-SOZ contacts. The rate of FRs inside the SOZ was less affected, especially in hippocampal contacts. In a few cases, even a marked increase of FRs following MDZ administration was seen. Our results demonstrate, for the first time, a significant HFO modulation in amygdala and hippocampus by MDZ, thus giving insights into the malfunction of GABA-mediated inhibition within epileptogenic areas and its role in HFO generation.

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.

Depth versus surface: A critical review of subdural and depth electrodes in intracranial electroencephalographic studies.

Intracranial electroencephalographic (IEEG) recording, using subdural electrodes (SDEs) and stereoelectroencephalography (SEEG), plays a pivotal role in localizing the epileptogenic zone (EZ). SDEs, employed for superficial cortical seizure foci localization, provide information on two-dimensional seizure onset and propagation. In contrast, SEEG, with its three-dimensional sampling, allows exploration of deep brain structures, sulcal folds, and bihemispheric networks. SEEG offers the advantages of fewer complications, better tolerability, and coverage of sulci. Although both modalities allow electrical stimulation, SDE mapping can tessellate cortical gyri, providing the opportunity for a tailored resection. With SEEG, both superficial gyri and deep sulci can be stimulated, and there is a lower risk of afterdischarges and stimulation-induced seizures. Most systematic reviews and meta-analyses have addressed the comparative effectiveness of SDEs and SEEG in localizing the EZ and achieving seizure freedom, although discrepancies persist in the literature. The combination of SDEs and SEEG could potentially overcome the limitations inherent to each technique individually, better delineating seizure foci. This review describes the strengths and limitations of SDE and SEEG recordings, highlighting their unique indications in seizure localization, as evidenced by recent publications. Addressing controversies in the perceived usefulness of the two techniques offers insights that can aid in selecting the most suitable IEEG in clinical practice.

Ictal fast activity chirps as markers of the epileptogenic zone.

The identification of the epileptogenic zone (EZ) boundaries is crucial for effective focal epilepsy surgery. We verify the value of a neurophysiological biomarker of focal ictogenesis, characterized by a low-voltage fast-activity ictal pattern (chirp) recorded with intracerebral electrodes during invasive presurgical monitoring (stereoelectroencephalography [SEEG]). The frequency content of SEEG signals was retrospectively analyzed with semiautomatic software in 176 consecutive patients with focal epilepsies that either were cryptogenic or presented with discordant anatomoelectroclinical findings. Fast activity seizure patterns with the spectrographic features of chirps were confirmed by computer-assisted analysis in 95.4% of patients who presented with heterogeneous etiologies and diverse lobar location of the EZ. Statistical analysis demonstrated (1) correlation between seizure outcome and concordance of sublobar regions included in the EZ defined by visual analysis and chirp-generating regions, (2) high concordance in contact-by contact analysis of 68 patients with Engel class Ia outcome, and (3) that discordance between chirp location and the visually outlined EZ correlated with worse seizure outcome. Seizure outcome analysis confirms the fast activity chirp pattern is a reproducible biomarker of the EZ in a heterogeneous group of patients undergoing SEEG.

Investigating current clinical opinions in stereoelectroencephalography-informed epilepsy surgery.

OBJECTIVE: Stereoelectroencephalography (SEEG) is increasingly utilized worldwide in epilepsy surgery planning. International guidelines for SEEG terminology and interpretation are yet to be proposed. There are worldwide differences in SEEG definitions, application of features in epilepsy surgery planning, and interpretation of surgical outcomes. This hinders the clinical interpretation of SEEG findings and collaborative research. We aimed to assess the global perspectives on SEEG terminology, differences in the application of presurgical features, and variability in the interpretation of surgery outcome scores, and analyze how clinical expert demographics influenced these opinions. METHODS: We assessed the practices and opinions of epileptologists with specialized training in SEEG using a survey. Data were qualitatively analyzed, and subgroups were examined based on geographical regions and years of experience. Primary outcomes included opinions on SEEG terminology, features used for epilepsy surgery, and interpretation of outcome scores. Additionally, we conducted a multilevel regression and poststratification analysis to characterize the nonresponders. RESULTS: A total of 321 expert responses from 39 countries were analyzed. We observed substantial differences in terminology, practices, and use of presurgical features across geographical regions and SEEG expertise levels. The majority of experts (220, 68.5%) favored the Lüders epileptogenic zone definition. Experts were divided regarding the seizure onset zone definition, with 179 (55.8%) favoring onset alone and 135 (42.1%) supporting onset and early propagation. In terms of presurgical SEEG features, a clear preference was found for ictal features over interictal features. Seizure onset patterns were identified as the most important features by 265 experts (82.5%). We found similar trends after correcting for nonresponders using regression analysis. SIGNIFICANCE: This study underscores the need for standardized terminology, interpretation, and outcome assessment in SEEG-informed epilepsy surgery. By highlighting the diverse perspectives and practices in SEEG, this research lays a solid foundation for developing globally accepted terminology and guidelines, advancing the field toward improved communication and standardization in epilepsy surgery.

Combined value of interictal markers and stimulated seizures to estimate the seizure onset zone in stereoelectroencephalography.

OBJECTIVE: This study was undertaken to investigate the potential of interictal electroencephalographic (EEG) findings and electrically stimulated seizures during stereo-EEG (SEEG) as surrogate markers for the spontaneous seizure onset zone (spSOZ). We hypothesized that combining the localizing information of these markers would allow clinically meaningful estimation of the spSOZ. METHODS: We included all patients (n = 63) who underwent SEEG between January 2013 and March 2020 at Helsinki University Hospital and had spontaneous seizures during the recording. We scored spikes, gamma activity, and background abnormality on each channel visually during a 12-h epoch containing waking state and sleep. Based on semiology, we classified stimulated seizures as typical or atypical/unclassifiable and estimated the stimulated SOZ (stimSOZ) for typical seizures. To assess which markers increased the odds of channel inclusion in the spSOZ, we fitted mixed effects logistic regression models. RESULTS: A combined regression model including the stimSOZ and interictal markers scored during sleep performed better in estimating which channels were part of the spSOZ than models based on stimSOZ (p < .001) or interictal markers (p < .001) alone. Of the individual markers, the effect sizes were greatest for inclusion of a channel in the stimSOZ (odds ratio [OR] = 60, 95% confidence interval [CI] = 37-97, p < .001) and for continuous (OR = 25, 95% CI = 12-55, p < .001) and subcontinuous (OR = 36, 95% CI = 21-64, p < .001) interictal spiking. At the individual level, the model's accuracy to predict spSOZ inclusion varied markedly (median accuracy = 85.7, range = 54.4-100), which was not explained by etiology (p > .05). SIGNIFICANCE: Compared to either marker alone, combining visually rated interictal SEEG markers and stimulated seizures improved prediction of which SEEG channels belonged to the spSOZ. Inclusion in the stimSOZ and continuous or subcontinuous spikes increased the odds of spSOZ inclusion the most. Future studies should investigate whether suboptimal sampling of the true epileptogenic zone can explain the model's poor performance in certain patients.

Nocturnal paroxysmal dystonia to sleep-related hypermotor epilepsy: A critical review.

Sleep-related paroxysmal motor episodes (SPMEs) have been described by various names, including nocturnal paroxysmal dystonia, nocturnal frontal lobe epilepsy (NFLE), and sleep-related hypermotor epilepsy. The underlying pathophysiology has been debated over the years, with these episodes assumed to be a form of paroxysmal dystonia or parasomnia versus a form of epilepsy. In most studies published on SPMEs and their variants (paroxysmal arousals, nocturnal paroxysmal dystonia, and episodic nocturnal wanderings) in the early 1990s, the authors speculated on the pathophysiology but did not commit to one idea. It was not until the mid-1990s that epilepsy became the leading prospect. We performed a narrative review of the major articles that have described this syndrome in a chronological fashion. We identified three eras, 1972-1993, 1994-1998, and 1999 to the present, each era marked by a landmark study. Our critical review of these early studies shows that the neurophysiological data supporting epilepsy as the sole basis for all SPME cases is very weak. In 1994-1995, a familial pattern of this syndrome was described and the term autosomal dominant NFLE was coined, with the authors claiming that all their patients had a form of frontal lobe epilepsy. With the exception of a few reference cases, the neurophysiological evidence that all patients had frontal lobe epilepsy was very weak. Compared to articles published on surgical series of frontal lobe epilepsy, the percentage of SPME cases with positive interictal/ictal electroencephalograms remained very low, seriously questioning the epileptic basis of the syndrome. Our critical review and analysis of the published literature shows that the evidence presented in favor of SPMEs being a homogenous focal epilepsy syndrome is very weak. Neurologists must recognize that SPMEs could be a form of movement disorder, parasomnia, or epilepsy. We recommend a pragmatic semiology-based classification of these episodes using the four-dimensional classification system.

Sleep Spindle Generation Before and After Epilepsy Surgery: A Source Imaging Study in Children with Drug-Resistant Epilepsy.

INTRODUCTION: Literature lacks studies investigating the cortical generation of sleep spindles in drug-resistant epilepsy (DRE) and how they evolve after resection of the epileptogenic zone (EZ). Here, we examined sleep EEGs of children with focal DRE who became seizure-free after focal epilepsy surgery, and aimed to investigate the changes in the spindle generation before and after the surgery using low-density scalp EEG and electrical source imaging (ESI). METHODS: We analyzed N2-sleep EEGs from 19 children with DRE before and after surgery. We identified slow (8-12 Hz) and fast spindles (13-16 Hz), computed their spectral features and cortical generators through ESI and computed their distance from the EZ and irritative zone (IZ). We performed two-way ANOVA testing the effect of spindle type (slow vs. fast) and surgical phase (pre-surgery vs. post-surgery) on each feature. RESULTS: Power, frequency and cortical activation of slow spindles increased after surgery (p < 0.005), while this was not seen for fast spindles. Before surgery, the cortical generators of slow spindles were closer to the EZ (57.3 vs. 66.2 mm, p = 0.007) and IZ (41.3 vs. 55.5 mm, p = 0.02) than fast spindle generators. CONCLUSIONS: Our data indicate alterations in the EEG slow spindles after resective epilepsy surgery. Fast spindle generation on the contrary did not change after surgery. Although the study is limited by its retrospective nature, lack of healthy controls, and reduced cortical spatial sampling, our findings suggest a spatial relationship between the slow spindles and the epileptogenic generators.

Psychiatric comorbid disorders and impulsivity in patients with drug-resistant temporal and extra-temporal focal epilepsies.

PURPOSE: To analyze patients with drug-resistant focal epilepsy from temporal (TLE) and extra-temporal origin (ETE) and to compare the prevalence of psychiatric comorbid disorders and impulsivity between them and a control group. METHODS: Consecutively studied patients with TLE and ETE confirmed with Video-EEG were included. Standardized psychiatric assessment was conducted using the Structured Clinical Interview for Axis I and II diagnosis of DSM-IV (SCID I-II), the Barrat-11 scale for impulsivity, and Beck inventory for depression. Parametric and nonparametric tests were performed. RESULTS: Seventy-three patients with temporal lobe epilepsy (TLE), 21 extra-temporal epilepsy (ETE) and 58 healthy control subjects were included. Both groups of patients showed a high frequency of Axis I comorbid psychiatric disorders: Depression was the most frequent disorder followed by Anxiety Disorders. Furthermore, Axis II (Personality disorders) were also diagnosed, similarly in both groups of patients (p > 0.05). In addition, both TLE and ETE groups presented higher impulsivity scores compared with the control group (p < 0.01). ETE showed a tendency to a higher impulsivity in the motor factor (p = 0.05). Among patients with TLE, a left laterality of the epileptogenic zone, and the presence of comorbid psychiatric disorders (depression), were found as independent factors associated with higher impulsivity (p < 0.05). CONCLUSION: Comorbid depression associated with higher impulsivity are important issues to consider in behavioral and clinical evaluation of patients with drug-resistant focal epilepsies, with the aim to set up a prompt treatment.

Focal ictal direct current shifts by a time constant of 2 seconds were clinically useful for resective epilepsy surgery.

OBJECTIVE: Ictal direct current shifts (icDCs) and ictal high-frequency oscillations (icHFOs) have been reported as surrogate markers for better surgical outcomes in epilepsy surgery. icDCs have been classified into two types: rapid and slow development. icDCs have been investigated with a time constant of 10 s (TC10s); however, many institutes use electroencephalography with a time constant of 2 s (TC2s). This study aimed to evaluate whether icDCs can be observed adequately with TC2s; moreover, it examined the relationship between the resected core area of icDCs or icHFOs and surgical outcomes, occurrence rate of each type of icDCs, and relationship between each type of icDCs and pathology. METHODS: Twenty-five patients with intractable focal epilepsy were analyzed retrospectively. icDCs and icHFOs were defined according to common metrics. The amplitude of icDCs was defined at >200 µV and even <200 µV. The two electrodes producing the most prominent icDCs and icHFOs were defined as core electrodes. The correlation between the resected core electrode area and degree of seizure control after surgery was analyzed. icDCs were classified into two types based on a peak latency value cutoff of 8.9 s, and the occurrence rates of both patterns were investigated. RESULTS: icDCs (142/147 seizures [96.6%]) and icHFOs (135/147 seizures [91.8%]) occurred in all patients (100%). Compared with the amplitude of icDCs with TC10s reported in previous studies, the amplitude of icDCs with TC2s was attenuated in the current study. A significant positive correlation was observed between the resected core electrode area and degree of seizure control in both icDCs and icHFOs. A rapid development pattern was observed in 202 of 264 electrodes (76.5%). SIGNIFICANCE: Similar to icDCs with TC10s, those with TC2s were observed adequately. Furthermore, favorable outcomes are expected using TC2s, which is currently available worldwide.

Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue.

The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.

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.

[A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model].

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.

Virtual MEG sensors based on beamformer and independent component analysis can reconstruct epileptic activity as measured on simultaneous intracerebral recordings.

The prevailing gold standard for presurgical determination of epileptogenic brain networks is intracerebral EEG, a potent yet invasive approach. Magnetoencephalography (MEG) is a state-of-the art non-invasive method for investigating epileptiform discharges. However, it is not clear at what level the precision offered by MEG can reach that of SEEG. Here, we present a strategy for non-invasively retrieving the constituents of the interictal network, with high spatial and temporal precision. Our method is based on MEG and a combination of spatial filtering and independent component analysis (ICA). We validated this approach in twelve patients with drug-resistant focal epilepsy, thanks to the unprecedented ground truth provided by simultaneous recordings of MEG and SEEG. A minimum variance adaptive beamformer estimated the source time series and ICA was used to further decompose these time series into network constituents (MEG-ICs), each having a time series (virtual electrode) and a topography (spatial distribution of amplitudes in the brain). We show that MEG has a considerable sensitivity of 0.80 and 0.84 and a specificity of 0.93 and 0.91 for reconstructing deep and superficial sources, respectively, when compared to the ground truth (SEEG). For each epileptic MEG-IC (n = 131), we found at least one significantly correlating SEEG contact close to zero lag after correcting for multiple comparisons. All the patients except one had at least one epileptic component that was highly correlated (Spearman rho>0.3) with that of SEEG traces. MEG-ICs correlated well with SEEG traces. The strength of correlation coefficients did not depend on the depth of the SEEG contacts or the clinical outcome of the patient. A significant proportion of the MEG-ICs (n = 83/131) were localized in proximity with their maximally correlating SEEG, within a mean distance of 20±12.18mm. Our research is the first to validate the MEG-retrieved beamformer IC sources against SEEG-derived ground truth in a simultaneous MEG-SEEG framework. Observations from the present study suggest that non-invasive MEG source components may potentially provide additional information, comparable to SEEG in a number of instances.

From theory to practical fundamentals of electroencephalographic source imaging in localizing the epileptogenic zone.

With continued advancement in computational technologies, the analysis of electroencephalography (EEG) has shifted from pure visual analysis to a noninvasive computational technique called EEG source imaging (ESI), which involves mathematical modeling of dipolar and distributed sources of a given scalp EEG pattern. ESI is a noninvasive phase I test for presurgical localization of the seizure onset zone in focal epilepsy. It is a relatively inexpensive modality, as it leverages scalp EEG and magnetic resonance imaging (MRI) data already collected typically during presurgical evaluation. With an adequate number of electrodes and combined with patient-specific MRI-based head models, ESI has proven to be a valuable and accurate clinical diagnostic tool for localizing the epileptogenic zone. Despite its advantages, however, ESI is routinely used at only a minority of epilepsy centers. This paper reviews the current evidence and practical fundamentals for using ESI of interictal and ictal epileptic activity during the presurgical evaluation of drug-resistant patients. We identify common errors in processing and interpreting ESI studies, describe the differences in approach needed for localizing interictal and ictal EEG discharges through practical examples, and describe best practices for optimizing the diagnostic information available from these studies.

Seizure detection and epileptogenic zone localisation on heavily skewed MEG data using RUSBoost machine learning technique.

Background: Epilepsy is a neurological disorder which is characterised by recurrent and involuntary seizures. Magnetoencephalography (MEG) is clinically used as a presurgical tool in locating the epileptogenic zone by localising either interictal epileptic discharges (IEDs) or ictal activities. The localisation of ictal onset provides reliable and more accurate seizure onset zones rather than localising the IEDs. Ictals or seizures are presently detected during MEG analysis by manually inspecting the recorded data. This is laborious when the duration of recordings is longer. Methods: We propose a novel method which uses statistical features such as short-time permutation entropy (STPE), gradient of STPE (GSTPE), short-time energy (STE) and short-time mean (STM) extracted from the ictal and interictal MEG data of drug resistant epilepsy patients group. Since the data is heavily skewed, the RUSBoost algorithm with k-fold cross-validation is used to classify the data into ictal and interictal by using the four feature vectors. This method is further used for localising the epileptogenic region using region-specific classifications by means of the RUSBoost algorithm. Results: The accuracy obtained for seizure detection is 93.4%. The specificity and sensitivity for the same are 93%. The localisation accuracies for each lobe are in the range of 88.1-99.1%. Discussion: Through this ictus detection method, the current scenario of laborious inspection of the ictal MEG can be reduced. The proposed system, thus, can be implemented in real-time as a better and more efficient method for seizure detection and further it can prove to be highly beneficial for patients and health-care professionals during real-time MEG recording. Furthermore, the identification of the epileptogenic lobe can provide clinicians with useful insights, and a pre-cursor for source localisation.

[Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection].

Drug-refractory epilepsy (DRE) may be treated by surgical intervention. Intracranial EEG has been widely used to localize the epileptogenic zone (EZ). Most studies of epileptic network focus on the features of EZ nodes, such as centrality and degrees. It is difficult to apply those features to the treatment of individual patients. In this study, we proposed a spatial neighbor expansion approach for EZ localization based on a neural computational model and epileptic network reconstruction. The virtual resection method was also used to validate the effectiveness of our approach. The electrocorticography (ECoG) data from 11 patients with DRE were analyzed in this study. Both interictal data and surgical resection regions were used. The results showed that the rate of consistency between the localized regions and the surgical resections in patients with good outcomes was higher than that in patients with poor outcomes. The average deviation distance of the localized region for patients with good outcomes and poor outcomes were 15 mm and 36 mm, respectively. Outcome prediction showed that the patients with poor outcomes could be improved when the brain regions localized by the proposed approach were treated. This study provides a quantitative analysis tool for patient-specific measures for potential surgical treatment of epilepsy.

Characterizing the seizure onset zone and epileptic network using EEG-fMRI in a rat seizure model.

Accurate epileptogenic zone (EZ) or seizure onset zone (SOZ) localization is crucial for epilepsy surgery optimization. Previous animal and human studies on epilepsy have reported that changes in blood oxygen level-dependent (BOLD) signals induced by epileptic events could be used as diagnostic markers for EZ or SOZ localization. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recording is gaining interest as a non-invasive tool for preoperative epilepsy evaluation. However, EEG-fMRI studies have reported inconsistent and ambiguous findings. Therefore, it remains unclear whether BOLD responses can be used for accurate EZ or SOZ localization. In this study, we used simultaneous EEG-fMRI recording in a rat model of 4-aminopyridine-induced acute focal seizures to assess the spatial concordance between individual BOLD responses and the SOZ. This was to determine the optimal use of simultaneous EEG-fMRI recording in the SOZ localization. We observed a high spatial consistency between BOLD responses and the SOZ. Further, dynamic BOLD responses were consistent with the regions where the seizures were propagated. These results suggested that simultaneous EEG-fMRI recording could be used as a noninvasive clinical diagnostic technique for localizing the EZ or SOZ and could be an effective tool for mapping epileptic networks.

Scalp-HFO indexes are biomarkers for the lateralization and localization of the epileptogenic zone in preoperative assessment.

During the noninvasive evaluation phase for refractory epilepsy, the localization of the epileptogenic zone (EZ) is essential for the surgical protocols. Confirmation of laterality is required when the preoperative evaluation limits the EZ to bilateral anterior temporal lobes or bilateral frontal lobes. High-frequency oscillations (HFOs) are considered to be promising biological markers for the EZ. However, a large number of studies on HFOs stem from intracranial research. There were few quantitative measures for scalp HFOs, so we proposed a new method to quantify and analyze scalp HFOs. This method was called the "scalp-HFO index" (HI) and calculated in both the EZ and non-EZ. The calculation was based on the numbers and spectral power of scalp HFOs automatically detected. We labeled the brain lobes involved in the EZ as regions of interest (ROIs). The HIs based on the ripple numbers (n-HI) and spectral power (s-HI) were significantly higher in the ROI than in the contra-ROI (P = 0.012, P = 0.003), indicating that HIs contributed to the lateralization of EZ. The sensitivity and specificity of n-HI for the localization of the EZ were 90% and 79.58%, respectively, suggesting that n-HI was valuable in localizing the EZ. HI may contribute to the implantation strategy of invasive electrodes. However, few scalp HFOs were recorded when the EZ was located in the medial cortex region.NEW & NOTEWORTHY We proposed the scalp-high-frequency oscillation (HFO) index (HI) as a quantitative assessment method for scalp HFOs to locate the epileptogenic zone (EZ). Our results showed that the HI in regions of interest (ROIs) was significantly higher than in contra-ROIs. Sensitivity and specificity of HI based on ripple rates (n-HI) for EZ localization were 90% and 79.58%, respectively. If the n-HI of the brain region was >1.35, it was more likely to be an epileptogenic region. Clinical application of HIs as an indicator may facilitate localization of the EZ.