Predictors Of Seizure Freedom Research Articles

Multicentre analysis of seizure outcome predicted by removal of high frequency oscillations.

In drug-resistant focal epilepsy, planning surgical resection may involve presurgical intracranial EEG recordings (iEEG) to detect seizures and other iEEG patterns to improve postsurgical seizure outcome. We hypothesized that resection of tissue generating interictal high frequency oscillations (HFOs, 80-500 Hz) in the iEEG predicts surgical outcome. Eight international epilepsy centres recorded iEEG during the patients' pre-surgical evaluation. The patients were of all ages, had epilepsy of all types, and underwent surgical resection of a single focus aiming at seizure freedom. In a prospective analysis we applied a fully automated definition of HFO which was independent of the dataset. Using an observational cohort design that was blinded to postsurgical seizure outcome, we analysed HFO rates during non-rapid-eye-movement sleep. If channels had consistently high rates over multiple epochs, they were labelled the "HFO area". After HFO analysis, centres provided the electrode contacts located in the resected volume and the seizure outcome at follow-up ≥24 months after surgery. The study was registered at www.clinicaltrials.gov (NCT05332990). We received 160 iEEG datasets. In 146 datasets (91%), the HFO area could be defined. The patients with completely resected HFO area were more likely to achieve seizure freedom compared to those without (OR 2.61 CI [1.15-5.91], P = 0.02). Among seizure free patients, the HFO area was completely resected in 31 and was not completely resected in 43. Among patients with recurrent seizures, the HFO area was completely resected in 14 and was not completely resected in 58. When predicting seizure freedom, the negative predictive value of the HFO area (68% CI [52-81]) was higher than that for the resected volume as predictor by itself (51% CI [42-59], P = 4e-5). The sensitivity and specificity for complete HFO area resection were 0.88 CI [0.72-0.98] and 0.39 CI [0.25-0.54] and the area under the curve was 0.83 CI [0.58-0.97], indicating good predictive performance. In a blinded cohort study from independent epilepsy centres, applying a previously validated algorithm for HFO marking without the need of adjusting to new datasets allowed us to validate the clinical relevance of HFOs to plan the surgical resection.

NCMP-10. EFFECT OF EXTENT OF RESECTION ON GLIOMA SEIZURE OUTCOMES BASED ON MOLECULAR GROUPS

Abstract INTRODUCTION Increased extent of resection (EOR) is a known predictor of seizure freedom in glioma patients. However, it is unknown how tumor genomic alterations affect EOR-induced seizure control. OBJECTIVE To evaluate the genotype-specific impact of EOR on post-operative seizure control METHODS Records of 553 glioma patients who underwent tumor resection from 2012-2023 were reviewed. All patient tumors were analyzed with next generation sequencing and FISH analysis. The unsupervised machine learning algorithm non-negative matrix factorization (NMF) was used to cluster the genomic data. Clinical variables including extent of resection of contrast enhancing tumor and non-enhancing tumor, temozolomide therapy, and radiation therapy were analyzed using Cox Proportional Hazards models. RESULTS NMF clustering of tumor sequencing data revealed four molecular groups: Group 1 (n=56): CDKN2a loss, EGFR copy number gain or mutation, TERT promoter (TERTp) mutation, and MGMT methylated; Group 2 (n=27): IDH mutation, TP53 mutation, and MGMT methylated; Group 3 (n=60): TP53 mutation, TERTp mutation and MGMT methylated; Group 4 (n=244): MGMT unmethylated. Thresholds in the post-operative residual non-enhancing tumor volume (NETV) were identified which marked the most difference in seizure control. For patients in Group 1, the NETV threshold that led to improved epilepsy outcomes was <94.7 cc (p=0.047), whereas the threshold for Group 4 was <89.1 cc (p=0.016). When analysis was conducted for Group 2, the threshold of 10.2 cc failed to reach significance (p=0.1). Interestingly, the threshold analysis for Group 3 found that having a NETV of > 10.1 cc showed improved seizure outcomes (p=0.016). A multivariable cox proportional hazard analysis found that twelve-month seizure freedom was predictive of better OS in group 1 (HR 0.02, p=0.05) and group 4 (HR 0.21, p=1.63e-9). CONCLUSION Our study reveals clinically distinct molecular groups of glioma that display different seizure responses. This data suggests a tumor-specific approach to aggressiveness of extent of resection in order to achieve optimal seizure control.

Beyond seizure freedom: Reduction in anti-seizure medication after epilepsy surgery

IntroductionPatients considering therapeutic epilepsy surgery often seek to decrease the number of anti-seizure medications (ASMs) they need. Predicting such reductions remains challenging. Although predictors of seizure freedom after epilepsy surgery are well-established, long-term outcomes remain modest and factors associated with ASM reduction, even in the absence of seizure freedom, may improve surgical planning to align with patient goals. MethodsWe studied a large multi-institutional cohort of patients who underwent epilepsy surgery between 2001 and 2022, with a minimum of two years follow-up. Preoperative features, including duration of epilepsy, epilepsy etiology, non-invasive investigation data, and total number of ASMs prescribed immediately prior to surgery were extracted for each patient. Primary endpoints included likelihood of ASM reduction and ASM freedom at multiple post-operative time points up to 15 years and stratified by seizure control. ResultsA total of 250 patients were followed for a median of 6.0 (range 2, 22) years after intracranial EEG (iEEG) surgery. Significant ASM reduction was only observed in those who underwent subsequent resection, whereas those undergoing neuromodulation saw their ASM usage maintained. Engel I outcomes were the strongest driver of ASM reduction. In patients with persistent seizures, patients with lateralized seizure onset zones (SOZs) also achieved sustained ASM reduction over time. Similarly, an increased number of preoperative ASMs also corresponded to a higher likelihood of ASM reduction across all follow-up periods. Other preoperative factors, including seizure etiology, did not independently influence ASM reduction. ConclusionsEven patients with persistent seizures after epilepsy surgery can observe meaningful ASM reduction during long-term follow-up. ASM reduction may be a relevant secondary outcome measure for epilepsy surgery.

Impact of seizure onset zone and intracranial electroencephalography ictal characteristics on epilepsy surgery outcomes in tuberous sclerosis complex

Ninety percent of tuberous sclerosis complex (TSC) patients have seizures, with ∼50 % developing drug refractory epilepsy. Surgical intervention aims to remove the seizure onset zone (SOZ). This retrospective study investigated the relationship of SOZ size, ictal pattern, and extent of resection with surgical outcomes. TSC patients undergoing resective/ablative surgery with >1-year follow-up and adequate imaging were included. Preoperative iEEG data were reviewed to determine ictal pattern and SOZ location. For outcomes, an ILAE score of 1–3 was defined as good and 4–6 as poor. Forty-four patients were included (age 117.4 ± 110.8 months). Of these, 59.1 % achieved a good outcome, while 40.9 % had a poor outcome. Size of SOZ was a significant factor (p = 0.009), with the poor outcome group having a larger SOZ (11.9 ± 6.7 electrode contacts) than the good outcome group (7.3 ± 7.2). SOZ number was significant (p = 0.020); >1 SOZ was associated with poor outcome. These results demonstrate extent of SOZ as a predictor of seizure freedom following epilepsy surgery in a mostly pediatric TSC cohort. We hypothesize that these features represent biomarkers of focality of the epileptogenic zone and can be used to sharpen prognosis for epilepsy surgery outcomes in this cohort.

1139 Retrospective Review of Stereotactic Electroencephalogram Recordings in Temporal Lobectomy Patients Demonstrates the Predictive Value of Interictal Cross-frequency Correlations

INTRODUCTION: Positive correlations between low and high frequency spectra in invasive electroencephalogram (EEG) have been implicated in pathological brain activity interictally and have been used for ictal detection in both focal and network models. METHODS: We retrospectively analyzed interictal signals from 22 patients who underwent SEEG at our institution between 12/2/14 to 12/8/20. We then analyzed significant HG/Beta cross-frequency correlations using similar processing to our previous publication. These signals were then separated based on temporal and extra-temporal locations. We further evaluated any statistical differences between the seizure free (SF) and non-seizure free (NSF) groups, and mesial (M) versus mesial temporal-plus (M+) onset location. RESULTS: 1) Clinical recordings from SEEG electrodes can detect cross-frequency correlations between HG/Beta using our previous methodology. 2) Positive correlations are significantly increased in temporal versus other sampled areas. 3) Positive correlations are more predictive of seizure onset during asleep recordings. 4) NSF patients show a significantly higher proportion of positive electrodes in the resected temporal lobe than SF patients. 5) SF patients have a greater proportion of significant contacts in the mesial versus the lateral temporal lobe than NSF patients. 6) Significant HG/Beta correlations in the mesial versus lateral temporal lobe predict seizure freedom better than ictal SEEG localization of seizure onset to M or M+ locations. CONCLUSIONS: We present preliminary data that local HG/Beta correlations may be predictive for epilepsy focus and surgical outcome and may have utility as adjunct methods to conventional SEEG analysis. Further studies are needed to determine strategies for prospective studies and clinical use.

Systematic 1 Hz direct electrical stimulation for seizure induction: A reliable method for localizing seizure onset zone and predicting seizure freedom

ObjectiveTo prospectively investigate the utility of seizure induction using systematic 1 Hz stimulation by exploring its concordance with the spontaneous seizure onset zone (SOZ) and relation to surgical outcome; comparison with seizures induced by non-systematic 50 Hz stimulation was attempted as well. MethodsProspective cohort study from 2018 to 2021 with ≥ 1 y post-surgery follow up at Yale New Haven Hospital. With 1 Hz, all or most of the gray matter contacts were stimulated at 1, 5, and 10 mA for 30–60s. With 50 Hz, selected gray matter contacts outside of the medial temporal regions were stimulated at 1–5 mA for 0.5–3s. Stimulation was bipolar, biphasic with 0.3 ms pulse width. The Yale Brain Atlas was used for data visualization. Variables were analyzed using Fisher's exact, χ2, or Mann–Whitney test. ResultsForty-one consecutive patients with refractory epilepsy undergoing intracranial EEG for localization of SOZ were included. Fifty-six percent (23/41) of patients undergoing 1 Hz stimulation had seizures induced, 83% (19/23) habitual (clinically and electrographically). Eighty two percent (23/28) of patients undergoing 50 Hz stimulation had seizures, 65% (15/23) habitual. Stimulation of medial temporal or insular regions with 1 Hz was more likely to induce seizures compared to other regions [15/32 (47%) vs. 2/41 (5%), p < 0.001]. Sixteen patients underwent resection; 11/16 were seizure free at one year and all 11 had habitual seizures induced by 1 Hz; 5/16 were not seizure free at one year and none of those 5 had seizures with 1 Hz (11/11 vs 0/5, p < 0.0001). No patients had convulsions with 1 Hz stimulation, but four did with 50 Hz (0/41 vs. 4/28, p = 0.02). SignificanceInduction of habitual seizures with 1 Hz stimulation can reliably identify the SOZ, correlates with excellent surgical outcome if that area is resected, and may be superior (and safer) than 50 Hz for this purpose. However, seizure induction with 1 Hz was infrequent outside of the medial temporal and insular regions in this study.

The role of quantitative markers in surgical prognostication after stereoelectroencephalography.

Stereoelectroencephalography (SEEG) is the reference method in the presurgical exploration of drug-resistant focal epilepsy. However, prognosticating surgery on an individual level is difficult. A quantified estimation of the most epileptogenic regions by searching for relevant biomarkers can be proposed for this purpose. We investigated the performances of ictal (Epileptogenicity Index, EI; Connectivity EI, cEI), interictal (spikes, high-frequency oscillations, HFO [80-300 Hz]; Spikes × HFO), and combined (Spikes × EI; Spikes × cEI) biomarkers in predicting surgical outcome and searched for prognostic factors based on SEEG-signal quantification. Fifty-three patients operated on following SEEG were included. We compared, using precision-recall, the epileptogenic zone quantified using different biomarkers (EZq ) against the visual analysis (EZC ). Correlations between the EZ resection rates or the EZ extent and surgical prognosis were analyzed. EI and Spikes × EI showed the best precision against EZc (0.74; 0.70), followed by Spikes × cEI and cEI, whereas interictal markers showed lower precision. The EZ resection rates were greater in seizure-free than in non-seizure-free patients for the EZ defined by ictal biomarkers and were correlated with the outcome for EI and Spikes × EI. No such correlation was found for interictal markers. The extent of the quantified EZ did not correlate with the prognosis. Ictal or combined ictal-interictal markers overperformed the interictal markers both for detecting the EZ and predicting seizure freedom. Combining ictal and interictal epileptogenicity markers improves detection accuracy. Resection rates of the quantified EZ using ictal markers were the only statistically significant determinants for surgical prognosis.

Predictors of seizure outcomes of autoimmune encephalitis: A clinical and morphometric quantitative analysis study

ObjectiveAutoimmune encephalitis can be followed by treatment-resistant epilepsy. Understanding its predictors and mechanisms are crucial to future studies to improve autoimmune encephalitis outcomes. Our objective was to determine the clinical and imaging predictors of postencephalitic treatment-resistant epilepsy. MethodsWe performed a retrospective cohort study (2012–2017) of adults with autoimmune encephalitis, both antibody positive and seronegative but clinically definite or probable. We examined clinical and imaging (as defined by morphometric analysis) predictors of seizure freedom at long term follow-up. ResultsOf 37 subjects with adequate follow-up data (mean 4.3 yrs, SD 2.5), 21 (57 %) achieved seizure freedom after a mean time of 1 year (SD 2.3), and one third (13/37, 35 %) discontinued ASMs. Presence of mesial temporal hyperintensities on the initial MRI was the only independent predictor of ongoing seizures at last follow-up (OR 27.3, 95 %CI 2.48–299.5). Morphometric analysis of follow-up MRI scans (n = 20) did not reveal any statistically significant differences in hippocampal, opercular, and total brain volumes between patients with postencephalitic treatment-resistant epilepsy and those without. SignificancePostencephalitic treatment-resistant epilepsy is a common complication of autoimmune encephalitis and is more likely to occur in those with mesial temporal hyperintensities on acute MRI. Volume loss in the hippocampal, opercular, and overall brain on follow-up MRI does not predict postencephalitic treatment-resistant epilepsy, so additional factors beyond structural changes may account for its development.

Predicting Seizure Freedom through Computational Processing of Routine EEG (S30.002)

Predicting Seizure Freedom through Computational Processing of Routine EEG (S30.002)

513 Presurgical Identification of Seizure Onset and Propagative Zones Without Ictal Recordings: A Combined Diffusion MRI and SEEG Study

INTRODUCTION: Increasing evidence has suggested that identification of regions involved in early seizure propagation (“Propagation Zones”, PZ) is important to predict seizure freedom after epilepsy surgery (Andrews 2019). Resting-state connectivity analyses using stereo-electroencephalography (SEEG) have shown promise in efficiently characterizing seizure onset zones (SOZ) but have found difficulty in distinguishing both SOZs and PZs from non-involved brain regions. Recently, evidence has suggested that ictal structure-function coupling can be used to delineate brain regions important in seizure dynamics (Shah 2019). METHODS: We calculated the resting-state SEEG directed connectivity of 26 consented patients with focal epilepsy undergoing presurgical evaluation. Using preoperative diffusion MRI, we then implemented a custom technique to obtain structural connectivity metrics between SEEG contacts. We calculated the structural connectivity and structure-function coupling of SOZs, PZs (spread within 10 seconds), and non-involved regions over a range of Euclidean distances. Finally, we generated models using a support vector machine to classify SOZs, PZs, and non-involved regions. RESULTS: SOZs and PZs exhibit comparably high local structural connectivity compared to non-involved regions despite SOZs demonstrating significantly greater functional connectivity to both PZs and non-involved regions. However, PZs exhibit significantly higher local structure-function coupling to that of non-involved regions, with SOZs exhibiting the highest local structure-function coupling. A support vector machine to classify SOZs, PZs, and non-involved regions and was able to significantly increase model accuracy by incorporating local structure-function coupling. CONCLUSIONS: SOZs and PZs demonstrate a distinct local structure-function coupling to that of non-involved regions and each other. This distinct coupling profile can be used to accurately classify SOZs, PZs and non-involved regions.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review.

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction

Fast ripples (FR) are a biomarker of epileptogenic brain, but when larger portions of FR generating regions are resected seizure freedom is not always achieved. To evaluate and improve the diagnostic accuracy of FR resection for predicting seizure freedom we compared the FR resection ratio (RR) with FR network graph theoretical measures. In 23 patients FR were semi-automatically detected and quantified in stereo EEG recordings during sleep. MRI normalization and co-registration localized contacts and relation to resection margins. The number of FR, and graph theoretical measures, which were spatial (i.e., FR rate-distance radius) or temporal correlational (i.e., FR mutual information), were compared with the resection margins and with seizure outcome We found that the FR RR did not correlate with seizure-outcome (p > 0.05). In contrast, the FR rate-distance radius resected difference and the FR MI mean characteristic path length RR did correlate with seizure-outcome (p < 0.05). Retesting of positive FR RR patients using either FR rate-distance radius resected difference or the FR MI mean characteristic path length RR reduced seizure-free misclassifications from 44 to 22% and 17%, respectively. These results indicate that graph theoretical measures of FR networks can improve the diagnostic accuracy of the resection of FR events for predicting seizure freedom.

Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery.

There is substantial variability in reported seizure outcome following pediatric epilepsy surgery, and lack of individualized predictive tools that could evaluate the probability of seizure freedom postsurgery. The aim of this study was to develop and validate a supervised machine learning (ML) model for predicting seizure freedom after pediatric epilepsy surgery. This is a multicenter retrospective study of children who underwent epilepsy surgery at five pediatric epilepsy centers in North America. Clinical information, diagnostic investigations, and surgical characteristics were collected, and used as features to predict seizure-free outcome 1 year after surgery. The dataset was split randomly into 80% training and 20% testing data. Thirty-five combinations of five feature sets with seven ML classifiers were assessed on the training cohort using 10-fold cross-validation for model development. The performance of the optimal combination of ML classifier and feature set was evaluated in the testing cohort, and compared with logistic regression, a classical statistical approach. Of the 801 patients included, 61.3% were seizure-free 1 year postsurgery. During model development, the best combination was XGBoost ML algorithm with five features from the univariate feature set, including number of antiseizure medications, magnetic resonance imaging lesion, age at seizure onset, video-electroencephalography concordance, and surgery type, with a mean area under the curve (AUC) of .73 (95% confidence interval [CI] = .69-.77). The combination of XGBoost and univariate feature set was then evaluated on the testing cohort and achieved an AUC of .74 (95% CI = .66-.82; sensitivity=.87, 95% CI = .81-.94; specificity=.58, 95% CI = .47-.71). The XGBoost model outperformed the logistic regression model (AUC=.72, 95% CI = .63-.80; sensitivity=.72, 95% CI = .63-.82; specificity=.66, 95% CI = .53-.77) in the testing cohort (p= .005). This study identified important features and validated an ML algorithm, XGBoost, for predicting the probability of seizure freedom after pediatric epilepsy surgery. Improved prognostication of epilepsy surgery is critical for presurgical counseling and will inform treatment decisions.

Combined [18F]FDG-PET with MRI structural patterns in predicting post-surgical seizure outcomes in temporal lobe epilepsy patients.

To integrate the glucose metabolism measured using [18F]FDG PET/CT and anatomical features measured using MRI to forecast the post-surgical seizure outcomes of intractable temporal lobe epilepsy. This retrospective study enrolled 63 patients with drug-resistant temporal lobe epilepsy. Z-transform of the patients' PET images based on comparison with a database of healthy controls, cortical thickness, and quantitative anisotropy (QA) of the diffusion spectrum imaging concordant/non-concordant with cortical resection was adopted to quantify their predictive values for the post-surgical seizure outcomes. The PET hypometabolism region was concordant with the surgical field in 47 of the 63 patients. Forty-two patients were seizure-free post-surgery. The sensitivity and specificity of PET in predicting seizure freedom were 89.4% and 68.8%, respectively. Complete resection of foci with overlapped PET, cortical thickness, and QA abnormalities resulted in Engel I in 27 patients, which was a good predictor of seizure freedom with an odds ratio (OR) of 19.57 (95% CI 2.38-161.25, p = 0.006). Hypometabolism involved in multiple lobes (OR = 7.18, 95% CI 1.02-50.75, p = 0.048) and foci of hypometabolism with QA/cortical thickness abnormalities outside surgical field (OR = 14.72, 95% CI 2.13-101.56, p = 0.006) were two major predictors of Engel III/IV outcomes. ORs of QA to predict Engel I and seizure recurrence were 14.64 (95% CI 2.90-73.80, p = 0.001) and 12.01 (95% CI 2.91-49.65, p = 0.001), respectively. Combined PET and structural pattern is helpful to predict the post-surgical seizure outcomes and worse outcomes of Engel III/IV. This might decrease unnecessary surgical injuries to patients who are potentially not amenable to surgery. • A combined metabolic and structural pattern is helpful to predict the post-surgical seizure outcomes. • Favorable post-surgical seizure outcome was most likely reached in patients whose hypometabolism overlapped with the structural changes. • Hypometabolism in multiple lobes and QA or cortical thickness abnormalities outside the surgical field were predictors of worse seizure outcomes of Engel III/IV.

Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom.

In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach.We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80–250 Hz) and in the fast ripple (250–500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient’s HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.

Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy

ObjectiveAnterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.MethodsWe analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities.ResultsWe found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).SignificanceThese results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

Interictal pattern on scalp electroencephalogram predicts excellent surgical outcome of epilepsy caused by focal cortical dysplasia.

ObjectiveFocal cortical dysplasia (FCD) represents an essential cause of drug‐resistant epilepsy with surgery as an effective treatment option. This study aimed to identify the important predictors of favorable surgical outcomes and the impact of the interictal scalp electroencephalogram (EEG) patterns in predicting postsurgical seizure outcomes.MethodsWe retrospectively evaluated 210 consecutive patients between 2015 and 2019. They were diagnosed with FCD by pathology, underwent resection, and had at least one year of postsurgical follow‐up. Predictors of seizure freedom were analyzed.ResultsBased on the information at the latest follow‐up, seizure outcome was classified as Engel Class I (seizure‐free) in 81.4% and Engel Class II‐IV (non‐seizure‐free) in 18.6% of patients. There were 43, 105, and 62 cases of FCD type I, type II, and type III, respectively. The interictal EEG showed a repetitive discharge pattern (REDP) in 87 (41.4%) patients, polyspike discharge pattern (PDP) in 41 (19.5%), and the coexistence of REDP and PDP in the same location in 32 (15.2%) patients. The analyzed patterns in order of frequency were repetitive discharges lasting 5 seconds or more (32.4%); polyspikes (16.7%); RED type 1 (11.4%); continuous epileptiform discharges occupying >80% of the recording (11.4%); RED type 2 (6.2%); brushes (3.3%); focal, fast, continuous spikes (2.4%); focal fast rhythmic epileptiform discharges (1.43%); and frequent rhythmic bursting epileptiform activity (1.4%). The coexistence of REDP and PDP in the same location on scalp EEG and complete resection of the assumed epileptogenic zone (EZ) was independently associated with favorable postsurgical prognosis.SignificanceResective epilepsy surgery for intractable epilepsy caused by FCD has favorable outcomes. Interictal scalp EEG patterns were revealed to be predictive of excellent surgical outcomes and may help clinical decision‐making and enable better presurgical evaluation.

Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy

Epilepsy surgery can achieve seizure freedom in selected pediatric candidates, but reliable postsurgical predictors of seizure freedom are missing. High frequency oscillations (HFO) in scalp EEG are a new and promising biomarker of treatment response. However, it is unclear if the skull defect resulting from craniotomy interferes with HFO detection in postsurgical recordings. We considered 14 children with focal lesional epilepsy who underwent presurgical evaluation, epilepsy surgery, and postsurgical follow-up of ≥ 1 year. We identified the nearest EEG electrodes to the skull defect in the postsurgical MRI. We applied a previously validated automated HFO detector to determine HFO rates in presurgical and postsurgical EEG. Overall, HFO rates showed a positive correlation with seizure frequency (p < 0.001). HFO rates in channels over the HFO area decreased following successful epilepsy surgery, irrespective of their proximity to the skull defect (p = 0.005). HFO rates in channels outside the HFO area but near the skull defect showed no increase following surgery (p = 0.091) and did not differ from their contralateral channels (p = 0.726). Our observations show that the skull defect does not interfere with postsurgical HFO detection. This supports the notion that scalp HFO can predict postsurgical seizure freedom and thus guide therapy management in focal lesional epilepsy.

Predicting the Outcome of Epilepsy Surgery by Covariance Pattern Analysis of Ictal Perfusion SPECT.

Previous studies on the utility of specific perfusion patterns in ictal brain perfusion SPECT for predicting the outcome of temporal lobe epilepsy surgery used qualitative visual pattern classification, semiquantitative region-of-interest analysis, or conventional univariate voxel-based testing, which are limited by intra- and interrater variability or low sensitivity to capture functional interactions among brain regions. The present study performed covariance pattern analysis of ictal perfusion SPECT using the scaled subprofile model for unbiased identification of predictive covariance patterns. Methods: The study retrospectively included 18 responders to temporal lobe epilepsy surgery (Engel I-A at 12 mo follow-up) and 18 nonresponders (≥Engel I-B). Ictal SPECT images were analyzed with the scaled subprofile model masked to group membership for unbiased identification of the 16 covariance patterns explaining the highest proportion of variance in the whole dataset. Individual expression scores of the covariance patterns were evaluated for predicting seizure freedom after temporal lobe surgery by receiver-operating-characteristic analysis. Kaplan-Meier analysis including all available follow-up data (up to 60 mo after surgery) was also performed. Results: Among the 16 covariance patterns only 1 showed a different expression between responders and nonresponders (P = 0.03). This favorable ictal perfusion pattern resembled the typical ictal perfusion pattern in temporomesial epilepsy. The expression score of the pattern provided an area of 0.744 (95% CI, 0.577-0.911, P = 0.004) under the receiver-operating-characteristic curve. Kaplan-Meier analysis revealed a statistical trend toward longer seizure freedom in patients with positive expression score (P = 0.06). The median estimated seizure-free time was 48 mo in patients with positive expression score versus 6 mo in patients with negative expression score. Conclusion: The expression of the favorable ictal perfusion pattern identified by covariance analysis of ictal brain perfusion SPECT provides independent (from demographic and clinical variables) information for the prediction of seizure freedom after temporal lobe epilepsy surgery. The expression of this pattern is easily computed for new ictal SPECT images and, therefore, might be used to support the decision for or against temporal lobe surgery in clinical patient care.

Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings.

This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction. Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical+EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.