Localization Of Epileptogenic Zone Research Articles

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

Evaluating the Efficacy of CortexID Quantitative Analysis in Localization of the Epileptogenic Zone in Patients with Temporal Lobe Epilepsy.

There remains a critical need for precise localization of the epileptogenic foci in individuals with drug-resistant epilepsy (DRE). 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging can reveal hypometabolic regions during the interval between seizures in patients with epilepsy. However, visual-based qualitative analysis is time-consuming and strongly influenced by physician experience. CortexID Suite is a quantitative analysis software that helps to evaluate PET imaging of the human brain. Therefore, we aimed to evaluate the efficacy of CortexID quantitative analysis in the localization of the epileptogenic zone in patients with temporal lobe epilepsy (TLE). A total of 102 patients with epilepsy who underwent 18F-FDG-PET examinations were included in this retrospective study. The PET visual analysis was interpreted by two nuclear medicine physicians, and the quantitative analysis was performed automatically using CortexID analysis software. The assumed epileptogenic zone was evaluated comprehensively by two skilled neurologists in the preoperative assessment of epilepsy. The accuracy of epileptogenic zone localization in PET visual analysis was compared with that in CortexID quantitative analysis. The diagnostic threshold for the difference in the metabolic Z-score between the right and left sides of medial temporal lobe epilepsy (MTLE) was calculated as 0.87, and that for lateral temporal lobe epilepsy (LTLE) was 2.175. In patients with MTLE, the area under the curve (AUC) was 0.922 for PET visual analysis, 0.853 for CortexID quantitative analysis, and 0.971 for the combined diagnosis. In patients with LTLE, the AUC was 0.842 for PET visual analysis, 0.831 for CortexID quantitative analysis, and 0.897 for the combined diagnosis. These results indicate that the diagnostic efficacy of CortexID quantitative analysis is not inferior to PET visual analysis (p > 0.05), while combined analysis significantly increases diagnostic efficacy (p < 0.05). Among the 23 patients who underwent surgery, the sensitivity and specificity of PET visual analysis for localization were 95.4% and 66.7%, and the sensitivity and specificity of CortexID quantitative analysis were 100% and 50%. The diagnostic efficacy of CortexID quantitative analysis is comparable to PET visual analysis in the localization of the epileptogenic zone in patients with TLE. CortexID quantitative analysis combined with visual analysis can further improve the accuracy of epileptogenic zone localization.

Seizure outcome in drug-resistant epilepsy in the setting of polymicrogyria

ObjectiveWe aimed to analyze seizure outcomes and define ictal onset with intracranial electroencephalography (ICEEG) in patients with polymicrogyria (PMG)-related drug-resistant epilepsy (DRE), considering surrounding cortex and extent of surgical resection. MethodsRetrospective study of PMG-diagnosed patients (2001 to June 2018) at a single epilepsy center was performed. Primary outcome was complete seizure freedom (SF), based on Engel classification with follow-up of ≥ 1 year. Univariate analyses identified predictive clinical variables, later integrated into multivariate Cox proportional hazards models. ResultsThirty-five patients with PMG-related DRE (19 adults/16 pediatric: 20 unilateral/15 bilateral) were studied. In surgical group (n = 23), 52 % achieved SF (mean follow-up:47 months), whereas none in non-resective treatment group (n = 12) attained SF (mean follow-up:39.3 months) (p = 0.002). In surgical group, there were no significant differences in SF, based on the laterality of the PMG [uni or bilateral,p = 0.35], involvement of perisylvian region(p = 0.714), and extent of the PMG resection [total vs. partial,p = 0.159]. Patients with ictal ICEEG onset in both PMG and non-PMG cortices, and those limited to non- PMG cortices had a greater chance of achieving SF compared to those limited to the PMG cortices. ConclusionResective surgery guided by ICEEG for defining the epileptogenic zone (EZ), in DRE patients with PMG, leads to favorable seizure outcomes. ICEEG-guided focal surgical resection(s) may lead to SF in patients with bilateral or extensive unilateral PMG. ICEEG aids in EZ localization within and/or outside the MRI-identified PMG. Complete removal of PMG identified on MRI does not guarantee SF. Hence, developing preimplantation hypotheses based on epileptogenic networks evaluation during presurgical assessment is crucial in this patient population.

SISCOS in focal cortical dysplasia: localization and comparative analysis with MRI.

This study evaluates the efficacy of SISCOS (Subtraction ictal-interictal SPECT coregistered to SPECT) in localizing the epileptogenic zone (EZ) in focal cortical dysplasia (FCD), comparing its predictive performance with MRI and post-surgical outcomes based on ILAE classification. 84 patients with drug refractory epilepsy (DRE) who were operated and had histopathology consistent with FCD, were included in the study. All patients had undergone a complete work-up including SISCOS and MRI for EZ localization, followed by discussion in the multidisciplinary epilepsy surgery meeting prior to surgery. Ictal & interictal perfusion SPECT studies were performed with Tc-99m Ethylene Cysteinate Dimer (Tc-99m ECD) followed by SISCOS analysis using SPM2 and Bioimage Suite 2.6. Concordance for localization was determined by comparing with the surgical resection site and post-surgical outcomes were assessed using the ILAE classification. The concordance for EZ localization demonstrated by SISCOS was 73.8% and MRI was 82.1%. 52 patients (61.9%) had good surgical outcome and 31(59%) of these were FCD type 2. In patients with discordant MRI findings, SISCOS was able to provide localisation in 86% (13/15), with 69.2% showing good surgical outcomes. Sensitivity of SISCOS and MRI was 73% (95% CI = 59-84.8%) and 78% (95% CI = 67.5-90.3%) respectively with no significant difference between the two. In FCD type I, both SISCOS and MRI revealed a similar a sensitivity of 76.4% (95%CI = 50.1-93.2%). Concordant cases exhibited higher seizure-free odds ratios for both modalities. SISCOS is effective in localizing the EZ in FCD patients, comparable to MRI. Integrating SISCOS and MRI enhances lesion detection, especially in MRI discordant cases. A comprehensive diagnostic approach utilizing SISCOS and MRI can optimize the non-invasive pre-surgical assessment in DRE thereby guiding surgical decision-making in a resource-limited setting.

Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain.

The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ). HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes. Thirty-five patients were included in this study, from whom 166 104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings. The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization. In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.

EEG Innovations in Neurological Disorder Diagnostics: A Five-Year Review

The study provides a description of electroencephalography (EEG) advancements and their application in diagnosing and assessing various neurological diseases over the previous five years. The paper covers how EEG is used to examine epilepsy, sleep disorders, movement disorders, cognitive function, and brain damage. In epilepsy, EEG remains critical for seizure diagnosis, categorization, and localization of epileptogenic zones. Recent enhancements include the integration of machine learning techniques with high-density EEG equipment. In terms of sleep disorders, aberrant patterns suggestive of illnesses such as sleep apnea or narcolepsy may be diagnosed by a sleep architecture study utilizing EEGs, which can also be used to track therapy response. Cortical involvement occurs in Parkinson’s disease and Huntington’s disease, as well as other areas of the brain stem or basal ganglia. It helps researchers learn more about the cortical damage produced by these disorders, which contributes greatly to understanding their pathophysiology. Aside from that, cognitive evaluation based on EEG has evolved via the creation of quantifiable biomarkers for early identification and monitoring of deterioration in Alzheimer’s disease, among others. Traumatic injuries can damage brain functioning, hence knowledge regarding severity predicted outcomes can be acquired by Traumatic Brain Injury evaluation utilizing EEG.

Association of Localizing 18F-FDG-PET Hypometabolism and Outcome Following Epilepsy Surgery: Systematic Review and Meta-Analysis.

Although commonly used in the evaluation of patients for epilepsy surgery, the association between the detection of localizing 18fluorine fluorodeoxyglucose PET (18F-FDG-PET) hypometabolism and epilepsy surgery outcome is uncertain. We conducted a systematic review and meta-analysis to determine whether localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery. A systematic literature search was undertaken. Eligible publications included evaluation with 18F-FDG-PET before epilepsy surgery, with ≥10 participants, and those that reported surgical outcome at ≥12 months. Random-effects meta-analysis was used to calculate the odds of achieving a favorable outcome, defined as Engel class I, International League Against Epilepsy class 1-2, or seizure-free, with localizing 18F-FDG-PET hypometabolism, defined as concordant with the epilepsy surgery resection zone. Meta-regression was used to characterize sources of heterogeneity. The database search identified 8,916 studies, of which 98 were included (total patients n = 4,104). Localizing 18F-FDG-PET hypometabolism was associated with favorable outcome after epilepsy surgery for all patients with odds ratio (OR) 2.68 (95% CI 2.08-3.45). Subgroup analysis yielded similar findings for those with (OR 2.64, 95% CI 1.54-4.52) and without epileptogenic lesion detected on MRI (OR 2.49, 95% CI 1.80-3.44). Concordance with EEG (OR 2.34, 95% CI 1.43-3.83), MRI (OR 1.69, 95% CI 1.19-2.40), and triple concordance with both (OR 2.20, 95% CI 1.32-3.64) was associated with higher odds of favorable outcome. By contrast, diffuse 18F-FDG-PET hypometabolism was associated with worse outcomes compared with focal hypometabolism (OR 0.34, 95% CI 0.22-0.54). Localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery, irrespective of the presence of an epileptogenic lesion on MRI. The extent of 18F-FDG-PET hypometabolism provides additional information, with diffuse hypometabolism associated with worse surgical outcome than focal 18F-FDG-PET hypometabolism. These findings support the incorporation of 18F-FDG-PET into routine noninvasive investigations for patients being evaluated for epilepsy surgery to improve epileptogenic zone localization and to aid patient selection for surgery.

A DEEP LEARNING FRAMEWORK TO CHARACTERIZE NOISY LABELS IN EPILEPTOGENIC ZONE LOCALIZATION USING FUNCTIONAL CONNECTIVITY.

Resting-sate fMRI (rs-fMRI) has emerged as a viable tool to localize the epileptogenic zone (EZ) in medication refractory focal epilepsy patients. However, due to clinical protocol, datasets with reliable labels for the EZ are scarce. Some studies have used the entire resection area from post-operative structural T1 scans to act as the ground truth EZ labels during training and testing. These labels are subject to noise, as usually the resection area will be larger than the actual EZ tissue. We develop a mathematical framework for characterizing noisy labels in EZ localization. We use a multi-task deep learning framework to identify both the probability of a noisy label as well as the localization prediction for each ROI. We train our framework on a simulated dataset derived from the Human Connectome Project and evaluate it on both the simulated and a clinical epilepsy dataset. We show superior localization performance in our method against published localization networks on both the real and simulated dataset.

1135 Classification of Temporal Lobe Epilepsy Anatomo-clinico-electrical Hypotheses: A Stereo EEG Study

INTRODUCTION: Temporal lobe epilepsy (TLE) has historically been divided into mesial and lateral (neocortical) TLE, but electrophysiological, semiological, and anatomical evidence indicate that the epileptogenic zone (EZ) and areas of propagation involve both temporal and extratemporal structures. Hypothesis-driven, structured interrogation is crucial for a successful stereo EEG (SEEG) exploration and subsequent successful surgical intervention. METHODS: 59 patients (ages 19-77) with drug-resistant TLE underwent SEEG at our institution from 2020-2022. We retrospectively reviewed each patient’s multidisciplinary discussions, history, imaging, and outcomes. We proposed four types of implantation hypotheses: temporal, temporal/basal/occipital, anterior perisylvian, and perisylvian. We quantitatively validated these types against anatomic coverage and semiology using unsupervised clustering and clinically by comparing classification to rates of successful localization of the epileptogenic zone (EZ) and seizure outcomes. RESULTS: Temporal (only) hypotheses were the least frequent (13.6%), followed by temporal/basal/occipital (25.4%), anterior perisylvian (30.5%), and perisylvian (30.5%). The hypotheses were not different in age at SEEG (p = 0.32), sex (p = 0.67), and handedness (p = 0.33). Frequent semiological features by type were: temporal – autonomic features, dÉjÀ vu; temporal/basal/occipital – visual and language disturbance; anterior perisylvian – oromotor; perisylvian – sensory manifestation. Clustering was validated against semiology (RI = 0.63) and lobar coverage (RI = 0.73). Successful localization of the EZ occurred in 79.7% and did not associate with hypothesis type (p = 0.22). Hypothesis type (p = 0.30) or EZ localization (p = 1.00) did not associate with the presence of an MRI lesion. 6-month Engel 1a rate was 71% and did not vary by hypothesis type (p = 0.29). CONCLUSIONS: The anatomo-clinico-electrical hypotheses in TLE must consider both temporal and extratemporal structures and not be limited by a medial vs. lateral TLE mindset. This proposed classification of TLE hypotheses can serve as starting point for multidisciplinary patient discussion as well as a tool to compare SEEG exploration and outcomes across centers.

Timing matters for accurate identification of the epileptogenic zone

ObjectiveInterictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). MethodsWe used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). ResultsOn average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323–0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. ConclusionsThe model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SignificanceRandom selection of short iEEG segments may give rise to inaccurate localization of the EZ.

MP2RAGE vs. MPRAGE surface-based morphometry in focal epilepsy.

In drug-resistant focal epilepsy, detecting epileptogenic lesions using MRI poses a critical diagnostic challenge. Here, we assessed the utility of MP2RAGE-a T1-weighted sequence with self-bias correcting properties commonly utilized in ultra-high field MRI-for the detection of epileptogenic lesions using a surface-based morphometry pipeline based on FreeSurfer, and compared it to the common approach using T1w MPRAGE, both at 3T. We included data from 32 patients with focal epilepsy (5 MRI-positive, 27 MRI-negative with lobar seizure onset hypotheses) and 94 healthy controls from two epilepsy centres. Surface-based morphological measures and intensities were extracted and evaluated in univariate GLM analyses as well as multivariate unsupervised 'novelty detection' machine learning procedures. The resulting prediction maps were analyzed over a range of possible thresholds using alternative free-response receiver operating characteristic (AFROC) methodology with respect to the concordance with predefined lesion labels or hypotheses on epileptogenic zone location. We found that MP2RAGE performs at least comparable to MPRAGE and that especially analysis of MP2RAGE image intensities may provide additional diagnostic information. Secondly, we demonstrate that unsupervised novelty-detection machine learning approaches may be useful for the detection of epileptogenic lesions (maximum AFROC AUC 0.58) when there is only a limited lesional training set available. Third, we propose a statistical method of assessing lesion localization performance in MRI-negative patients with lobar hypotheses of the epileptogenic zone based on simulation of a random guessing process as null hypothesis. Based on our findings, it appears worthwhile to study similar surface-based morphometry approaches in ultra-high field MRI (≥ 7 T).

The role of magnetoencephalography in preoperative localization and postoperative outcome prediction in patients with posterior cortical epilepsy.

We aimed to explore the value of magnetoencephalography in the presurgical evaluation of patients with posterior cortex epilepsy. A total of 39 patients with posterior cortex epilepsy (PCE) and intact magnetoencephalography (MEG) images were reviewed from August 2019 to July 2022. MEG dipole clusters were classified into single clusters, multiple clusters, and scatter dipoles based on tightness criteria. The association of the surgical outcome with MEG dipole classifications was evaluated using Fisher's exact tests. Among the 39 cases, there were 24 cases of single clusters (61.5%), nine cases of multiple clusters (23.1%), and six cases of scattered dipoles (15.4%). Patients with single dipole clusters were more likely to become seizure-free. Among single dipole cluster cases (n = 24), complete MEG dipole resection yielded a more favorable surgical outcome than incomplete resection (83.3% vs. 16.7%, p = 0.007). Patients with concordant MRI and MEG findings achieved a significantly more favorable surgical outcome than discordant patients (66.7% vs. 33.3%, p = 0.044), especially in single dipole cluster patients (87.5% vs. 25.0%, p = 0.005). MEG can provide additional valuable information regarding surgical candidate selection, epileptogenic zone localization, electrode implantation schedule, and final surgical planning in patients with posterior cortex epilepsy.

NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy

Epilepsy surgery is an option for people with focal onset drug-resistant (DR) seizures but a delayed or incorrect diagnosis of epileptogenic zone (EZ) location limits its efficacy. Seizure semiological manifestations and their chronological appearance contain valuable information on the putative EZ location but their interpretation relies on extensive experience. The aim of our work is to support the localization of EZ in DR patients automatically analyzing the semiological description of seizures contained in video-EEG reports. Our sample is composed of 536 descriptions of seizures extracted from Electronic Medical Records of 122 patients. We devised numerical representations of anamnestic records and seizures descriptions, exploiting Natural Language Processing (NLP) techniques, and used them to feed Machine Learning (ML) models. We performed three binary classification tasks: localizing the EZ in the right or left hemisphere, temporal or extra-temporal, and frontal or posterior regions. Our computational pipeline reached performances above 70% in all tasks. These results show that NLP-based numerical representation combined with ML-based classification models may help in localizing the origin of the seizures relying only on seizures-related semiological text data alone. Accurate early recognition of EZ could enable a more appropriate patient management and a faster access to epilepsy surgery to potential candidates.

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

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. 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. 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. 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. 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.

Spatiotemporal Microstate Dynamics of Spike-free Scalp EEG Offer a Potential Biomarker for Refractory Temporal Lobe Epilepsy.

Refractory temporal lobe epilepsy (TLE) is one of the most frequently observed subtypes of epilepsy and endangers more than 50 million people world-wide. Although electroencephalogram (EEG) had been widely recognized as a classic tool to screen and diagnose epilepsy, for many years it heavily relied on identifying epileptic discharges and epileptogenic zone localization, which however, limits the understanding of refractory epilepsy due to the network nature of this disease. This work hypothesizes that the microstate dynamics based on resting-state scalp EEG can offer an additional network depiction of the disease and provide potential complementary evaluation tool for the TLE even without detectable epileptic discharges on EEG. We propose a novel framework for EEG microstate spatial-temporal dynamics (EEG-MiSTD) analysis based on machine learning to comprehensively model millisecond-changing whole-brain network dynamics. With only 100 seconds of resting-state EEG even without epileptic discharges, this approach successfully distinguishes TLE patients from healthy controls and is related to the lateralization of epileptic focus. Besides, microstate temporal and spatial features are found to be widely related to clinical parameters, which further demonstrate that TLE is a network disease. A preliminary exploration suggests that the spatial topography is sensitive to the following surgical outcomes. From such a new perspective, our results suggest that spatiotemporal microstate dynamics is potentially a biomarker of the disease. The developed EEG-MiSTD framework can probably be considered as a general tool to examine dynamical brain network disruption in a user-friendly way for other types of epilepsy.

Automated Detection of Interictal High-frequency Oscillations for Epileptogenic Zone Localization

Automated Detection of Interictal High-frequency Oscillations for Epileptogenic Zone Localization

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1–70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p &lt; 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient’s MRI without requiring long-term iEEG inspection.

Breath-Hold-Triggered BOLD fMRI in Drug-Resistant Nonlesional Focal Epilepsy-APilot Study.

Individuals with drug-resistant epilepsy may benefit from epilepsy surgery. In nonlesional cases, where no epileptogenic lesion can be detected on structural magnetic resonance imaging, multimodal neuroimaging studies are required. Breath-hold-triggered BOLD fMRI (bh-fMRI) was developed to measure cerebrovascular reactivity in stroke or angiopathy and highlights regional network dysfunction by visualizing focal impaired flow increase after vasodilatory stimulus. This regional dysfunction may correlate with the epileptogenic zone. In this prospective single-center single-blind pilot study, we aimed to establish the feasibility and safety of bh-fMRI in individuals with drug-resistant non-lesional focal epilepsy undergoing presurgical evaluation. In this prospective study, 10consecutive individuals undergoing presurgical evaluation for drug-resistant focal epilepsy were recruited after case review at amultidisciplinary patient management conference. Electroclinical findings and results of other neuroimaging were used to establish the epileptogenic zone hypothesis. To calculate significant differences in cerebrovascular reactivity in comparison to the normal population, bh-fMRIs of 16healthy volunteers were analyzed. The relative flow change of each volume of interest (VOI) of the atlas was then calculated compared to the flow change of the whole brain resulting in an atlas of normal cerebral reactivity. Consequently, the mean flow change of every VOI of each patient was tested against the healthy volunteers group. Areas with significant impairment of cerebrovascular reactivity had decreased flow change and were compared to the epileptogenic zone localization hypothesis in asingle-blind design. Acquisition of bh-fMRI was feasible in 9/10cases, with one patient excluded due to noncompliance with breathing maneuvers. No adverse events were observed, and breath-hold for intermittent hypercapnia was well tolerated. On blinded review, we observed full or partial concordance of the local network dysfunction seen on bh-fMRI with the electroclinical hypothesis in 6/9cases, including cases with extratemporal lobe epilepsy and those with nonlocalizing 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). This represents the first report of bh-fMRI in individuals with epilepsy undergoing presurgical evaluation. We found bh-fMRI to be feasible and safe, with apromising agreement to electroclinical findings. Thus, bh-fMRI may represent apotential modality in the presurgical evaluation of epilepsy. Further studies are needed to establish clinical utility.

Outcome of stereo-electroencephalography with single-unit recording in drug-refractory epilepsy.

The aim of this study was to evaluate the utility and safety of "hybrid" stereo-electroencephalography (SEEG) in guiding epilepsy surgery and in providing information at single-neuron levels (i.e., single-unit recording) to further the understanding of the mechanisms of epilepsy and the neurocognitive processes unique to humans. The authors evaluated 218 consecutive patients undergoing SEEG procedures from 1993 through 2018 at a single academic medical center to assess the utility and safety of this technique in both guiding epilepsy surgery and providing single-unit recordings. The hybrid electrodes used in this study contained macrocontacts and microwires to simultaneously record intracranial EEG and single-unit activity (hybrid SEEG). The outcomes of SEEG-guided surgical interventions were examined, as well as the yield and scientific utility of single-unit recordings in 213 patients who participated in the research involving single-unit recordings. All patients underwent SEEG implantation by a single surgeon and subsequent video-EEG monitoring (mean of 10.2 electrodes per patient and 12.0 monitored days). Epilepsy networks were localized in 191 (87.6%) patients. Two clinically significant procedural complications (one hemorrhage and one infection) were noted. Of 130 patients who underwent subsequent focal epilepsy surgery with a minimum 12-month follow-up, 102 (78.5%) underwent resective surgery and 28 (21.5%) underwent closed-loop responsive neurostimulation (RNS) with or without resection. Seizure freedom was achieved in 65 (63.7%) patients in the resective group. In the RNS group, 21 (75.0%) patients achieved 50% or greater seizure reduction. When the initial period of 1993 through 2013 before responsive neurostimulator implantation in 2014 was compared with the subsequent period of 2014 through 2018, the proportion of SEEG patients undergoing focal epilepsy surgery grew from 57.9% to 79.7% due to the advent of RNS, despite a decline in focal resective surgery from 55.3% to 35.6%. A total of 18,680 microwires were implanted in 213 patients, resulting in numerous significant scientific findings. Recent recordings from 35 patients showed a yield of 1813 neurons, with a mean yield of 51.8 neurons per patient. Hybrid SEEG enables safe and effective localization of epileptogenic zones to guide epilepsy surgery and provides unique scientific opportunities to investigate neurons from various brain regions in conscious patients. This technique will be increasingly utilized due to the advent of RNS and may prove a useful approach to probe neuronal networks in other brain disorders.

Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography

ObjectiveHigh-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains. MethodsHFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital. ResultsA dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features. ConclusionOur classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.