Publications

Spikes are the most established interictal epilepsy biomarkers. Yet, they suffer from low specificity since they are partially concordant with the epileptogenic zone and are often found in non-epileptogenic areas. High-frequency oscillations, classified as ripples and fast ripples, are considered more specific biomarkers compared with spikes. Ripples occur more often than fast ripples but are believed to be less specific, since they are more frequently generated by physiological mechanisms. Here, we examine the temporal relationship between spikes, ripples and fast ripples, and assess the ability of these biomarkers (and their combinations) to delineate the epileptogenic zone and predict outcome. We hypothesize that spikes on ripples (temporal co-occurrence of spikes and ripples) can identify the epileptogenic zone and predict outcome better than spikes or ripples. We analysed intracranial EEG data from 40 children with drug-resistant epilepsy. Spikes, ripples and fast ripples were classified based on their temporal occurrence. Their rates were compared with resection by performing a receiver operating characteristic analysis. The resection ratio, quantifying the extent of each biomarker's removal, was computed, and correlated with patients' outcome. Spikes on ripples were seen in all patients; fast ripples were seen in 43% of patients. In good outcome patients, fast ripple and spike on ripple rates were higher inside resection (P = 0.027; P = 0.003, respectively). Fast ripples and spikes on ripples resection ratio predicted outcome (P < 0.05). For fast ripples, outcome was predicted in 82% of patients; this proportion was higher than the one for spikes (48%, P = 0.015) and ripples (40%, P = 0.003), and spikes on ripples (53%, P = 0.034). Fast ripples were the most accurate (82%) to predict outcome; spikes on ripples were the most precise (positive predictive value = 90%). Spike rate and spikes on ripples performance to predict the epileptogenic zone were correlated (r = 0.36, P = 0.035). For patients with frequent spikes, spikes on ripples accuracy to predict outcome reached 70%. Fast ripples are the best biomarker, but they can be seen in only half of patients with drug-resistant epilepsy. Spikes on ripples are a good alternative with more universal applicability since they can be seen in all patients while their resection predicts good outcome; their performance is improved in patients with frequent spikes. Overall, in the absence of fast ripples, spike on ripple areas should be targeted during surgery.

Surgical success for patients with focal drug resistant epilepsy (DRE) relies on accurate localization of the epileptogenic zone (EZ). Currently, no exam delineates this zone unambiguously. Instead, the EZ is approximated by the area where seizures begin, which is identified manually through a tedious process that is prone to errors and biases. More importantly, resection of this area does not always predict good surgical outcome. Here, we propose an artificially intelligent, patient-specific framework that automatically identifies the EZ requiring little to no input from clinicians, without having to wait for a seizure to occur. The framework transforms interictal intracranial electroencephalography data into spatiotemporal representations of brain activity discriminating the interictal epileptogenic network from background activity. The epileptogenic network delineates the EZ with high precision and predicts surgical outcome. Our framework eliminates the need for manual data inspection, reduces prolonged monitoring, and enhances surgical planning for DRE patients.

OBJECTIVE: Interictal biomarkers are critical for identifying the epileptogenic focus. However, spikes and ripples lack specificity while fast ripples lack sensitivity. These biomarkers propagate from more epileptogenic onset to areas of spread. The pathophysiological mechanism of these propagations is elusive. Here, we examine zones where spikes and high frequency oscillations co-occur (SHFO), the spatiotemporal propagations of spikes, ripples, and fast ripples, and evaluate the spike-ripple onset overlap (SRO) as an epilepsy biomarker.

METHODS: We retrospectively analyzed intracranial EEG data from 41 patients with drug-resistant epilepsy. We mapped propagations of spikes, ripples, and fast ripples, and identified their onset and spread zones, as well as SHFO and SRO. We then estimated the SRO prognostic value in predicting surgical outcome and compared it to onset and spread zones of spike, ripple, and fast ripple propagations, and SHFO.

RESULTS: We detected spikes and ripples in all patients and fast ripples in 12 patients (29%). We observed spike and ripple propagations in 40 (98%) patients. Spike and ripple onsets overlapped in 35 (85%) patients. In good outcome patients, SRO showed higher specificity and precision (p < 0.05) in predicting resection compared to onset and zones of spikes, ripples, and SHFO. Only SRO resection predicted outcome (p = 0.01) with positive and negative predictive values of 82% and 57%, respectively.

INTERPRETATION: SRO is a specific and precise biomarker of the epileptogenic zone whose removal predicts outcome. SRO is present in most patients with drug-resistant epilepsy. Such a biomarker may reduce prolonged intracranial monitoring and improve outcome.

Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.

OBJECTIVE: To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.

METHODS: We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization.

RESULTS: FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes.

SIGNIFICANCE: We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

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.

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 < 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.

OBJECTIVE: To evaluate the diagnostic accuracy of electromagnetic source imaging (EMSI) in localizing spikes and predict surgical outcome in children with drug resistant epilepsy (DRE) due to focal cortical dysplasia (FCD).

METHODS: We retrospectively analyzed magnetoencephalography (MEG) and high-density (HD-EEG) data from 23 children with FCD-associated DRE who underwent intracranial EEG and surgery. We localized spikes using equivalent current dipole (ECD) fitting, dipole clustering, and dynamical statistical parametric mapping (dSPM) on EMSI, electric source imaging (ESI), and magnetic source imaging (MSI). We calculated the distance from the seizure onset zone (DSOZ) and resection (DRES). We estimated receiver operating characteristic (ROC) curves with Youden's index (J) to predict outcome.

RESULTS: EMSI presented shorter DSOZ (15.18 ± 9.06 mm) and DRES (8.56 ± 6.24 mm) compared to ESI (DSOZ: 25.04 ± 16.20 mm, p < 0.009; DRES: 18.88 ± 17.30 mm, p < 0.03) and MSI (DSOZ: 23.37 ± 8.98 mm, p < 0.03; DRES: 15.51 ± 10.11 mm, p < 0.02) for clustering in patients with good outcome. Clustering showed shorter DSOZ and DRES compared to ECD fitting and dSPM (p < 0.05). EMSI had higher performance as outcome predictor (J = 70.63%) compared to ESI (J = 41.27%) and MSI (J = 33.33%) for clustering.

CONCLUSIONS: EMSI provides superior localization and improved predictive performance than individual modalities.

SIGNIFICANCE: EMSI can help the surgical planning and facilitate the localization of epileptogenic foci.

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

Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.