Research

OBJECTIVE: We aimed to test whether T-wave alternans (TWA), which is a marker of susceptibility to ventricular fibrillation, is abnormal in children with Angelman syndrome (AS) compared with typically developing children (TDC), and whether it can be used as a biomarker of AS.

MATERIALS AND METHODS: Using surface electrocardiogram (ECG), we calculated TWA in AS and compared it between AS and TDC (Wilcoxon rank sum test). We then performed logistic regression to test TWA ability to distinguish AS from TDC.

RESULTS: We observed higher TWA in AS than TDC (44 vs. 33 uV, p = 0.009), while heart rate did not differ (p = 0.26), nor its variability (p = 0.72). TWA values enabled discrimination between AS and TDC (p = 0.0008) with accuracy of 81%, positive predictive value of 72%, and negative predictive value of 100%.

INTERPRETATION: Our findings suggest that ECG in children with AS contains evidence of acquired cardiac abnormality via pathologically increased TWA.

OBJECTIVE: The current work aims to study interictal T-wave alternans (TWA), a biomarker of cardiac repolarization instability that is elevated in epilepsy, in children with drug-resistant epilepsy (DRE) and to test whether it normalizes following postoperative seizure resolution.

METHODS: In this cohort study, we studied children with DRE who underwent successful epilepsy surgery (Engel class IA). Interictal TWA was computed before and after surgery using electrocardiographic data collected during electroencephalographic video monitoring. These values were compared to each other (Wilcoxon signed-rank test) and to healthy controls (Wilcoxon rank-sum test). Heart rate (HR) and HR variability (HRV) were also assessed and compared. Correlations with clinical variables, including time since surgery and number of antiseizure medications, were analyzed.

RESULTS: Preoperative TWA was elevated in children with DRE compared to healthy controls (25.5 μV vs. 13.7 μV, p = .009). Following surgery in DRE, TWA decreased (17.9 μV, p = .002) and was no longer different from control levels (p = .81). Preoperative HRV was reduced compared to controls (p = .02) and did not change postoperatively (p = .70). The TWA percent change did not correlate with the time elapsed since surgery (p = .26).

SIGNIFICANCE: Interictal TWA is elevated in children with DRE and normalizes following successful surgical treatment, suggesting that epilepsy-associated cardiac repolarization instability is reversible. TWA may serve as a dynamic biomarker of epilepsy-related cardiac stress and recovery, with potential utility in monitoring treatment response and stratifying cardiac risk. We provide the first evidence that cardiac electrical instability in pediatric DRE, as measured by TWA, improves after seizure resolution through surgery. These findings highlight the relevance of noninvasive cardiac biomarkers in epilepsy management and support further research into TWA as a marker of systemic recovery after surgical seizure treatment.

Delineating the epileptogenic zone (EZ) is essential for achieving seizure freedom in drug-resistant epilepsy (DRE). Conventionally, seizure onset derived from ictal intracranial EEG (iEEG) approximates the EZ, but acquiring ictal data can be challenging. Interictal iEEG abnormalities offer abundant, easily acquired, non-seizure-dependent markers of the epileptogenic tissue; however, these biomarkers offer limited specificity. Here, we propose a machine-learning framework that integrates interictal spike and ripple features to automatically delineate the EZ and predict outcome with improved performance compared to individual biomarkers. We retrospectively analyzed iEEG data from 62 children with DRE ([34 good (Engel 1) outcomes] undergoing neurosurgery, automatically detected spikes and ripples, and computed temporal, spectral, and spatial features for each channel. We trained Random Forest classifiers to predict the EZ using combinations of these features. The predicted EZ derived from spike-based and combined spike-ripple feature sets outperformed those from individual biomarkers in defining the EZ, with an area under the receiver operating characteristic curve of 0.9 and 74% spatial overlap with resection. Although most individual features and classifiers predicted the outcome, the combined feature model performed best (i.e., sensitivity 88%, specificity 68%, and accuracy 79%). Our findings demonstrate that integrating multimodal interictal features improves the EZ delineation, providing valuable prognostic insights for epilepsy surgery.

OBJECTIVE: Approximately 40% of children undergoing epilepsy surgery have postoperative seizures, underscoring the need for enhanced estimators of the epileptogenic zone (EZ). We hypothesize that visually imperceptible low-entropy activity in the interictal periods, even in the absence of conventional spikes, is a robust signature of the EZ. To test this, we mapped interictal "low-entropy zones" using intracranial electroencephalography (iEEG) in children with drug-resistant epilepsy (DRE) and assessed their value for postsurgical outcome prediction when targeted during surgery, along with their stability over prolonged periods.

METHODS: We analyzed iEEG data of 75 DRE children, including brief (5 min) data from patients with known Engel outcome (N = 59; used for outcome prediction) plus prolonged data from a separate recent cohort (N = 16; used for stability assessment). We estimated each contact's entropy across various frequencies (delta to fast-ripple), pinpointed low-entropy zones, and assessed whether their removal predicts outcome (3-fold cross-validation). In addition, the predictive value of entropy during non-epileptiform (spike-free) epochs was also assessed. Furthermore, established interictal estimators (spikes-on-ripple, fast ripples) were tested for outcome prediction. Using the prolonged dataset, we tested whether entropy distribution over brief epochs was similar to prolonged (3 h) data.

RESULTS: High overlap between low-entropy zones and resection correlated with low Engel class (p < 0.0001, R = -0.54, N = 59), also during non-epileptiform epochs (R = -0.52). Low-entropy-zone removal predicted outcomes with F1 score of 87% (p < 0.0001, N = 51; Engel I vs III-IV) outperforming spikes-on-ripple (F1 score = 82%, p = 0.002) or fast ripples (F1 score = 80%, p = 0.01). Low-entropy zones retained high predictive value when non-epileptiform epochs were used (F1 score = 89%, N = 44). Entropy distribution over brief epochs was strongly correlated with prolonged data (R > 0.8, p < 0.0001), and its relationship with seizure-onset zone did not differ (brief vs prolonged data: p > 0.6).

SIGNIFICANCE: Surgically targeting low-entropy zones accurately predicts the postoperative seizure outcomes of children with DRE. Mapping low-entropy activity using brief iEEG segments shows consistency with using prolonged data and could enhance surgical planning in pediatric DRE.

High frequency oscillations have been extensively investigated as interictal biomarkers of epilepsy. Yet, their value is largely debated due to the presence of physiological oscillations, which complicate distinguishing between normal versus abnormal events. So far, this debate has been addressed using intracranial EEG data from patients with drug-resistant epilepsy. Yet, this approach suffers from inability to record control data from healthy subjects and lack of whole brain coverage. Here, we aim to differentiate physiological from pathological high frequency oscillations using non-invasive whole brain electrophysiological recordings from children with drug-resistant epilepsy and typically developing controls. We recorded high-density EEG and magnetoencephalography data from 47 controls (median age: 11 years; 25 females) and 54 children with drug-resistant epilepsy (median age: 14 years, 33 females). We detected high frequency oscillations (in ripple frequency band) semi-automatically and localized their cortical generators through electric or magnetic source imaging. From each ripple, we extracted a set of temporal, morphological, spectral and spatial features. We then compared the features between ripples recorded from the epileptic brain (further distinguished into those from epileptogenic and non-epileptogenic regions) and those recorded from the control group (normal brain). We used these features to cross-validate a Naïve-Bayes algorithm for classifying each ripple recorded from children with epilepsy as coming from an epileptogenic region or not. We observed more high frequency oscillations on EEG than magnetoencephalography recordings (P < 0.001) both in the epilepsy and control groups. Physiological high frequency oscillations (recorded from controls) showed lower power, shorter duration and less variability (in both amplitude and duration) than those recorded from the epilepsy group (P < 0.001). Inter-channel latency of physiological ripples was longer compared to ripples from the epileptogenic regions (P < 0.01), while it was similar to the ripples from non-epileptogenic regions (P > 0.05). Ripples from epileptogenic regions showed larger extent than those from non-epileptogenic regions or from the control group (P < 0.001). The classification model showed an accuracy of 73%, with negative and positive predictive values of 73% and 70% (P < 0.0001), respectively, in classifying high frequency oscillations from the drug-resistant epilepsy group (as either epileptogenic or not). Our study indicates that physiological high frequency oscillations, recorded from the healthy brain, have distinct temporal, morphological, spectral and spatial features compared to those generated by the epileptic brain. The differentiation of pathological from physiological high frequency oscillations through non-invasive full-head techniques may augment the presurgical evaluation process of children with drug-resistant epilepsy and lead to better postsurgical seizure outcomes.