Explaining slow seizure propagation with white matter tractography.

Epileptic seizures recorded with stereoelectroencephalography (SEEG) can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and SEEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread, and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared to regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-IV). The seizure onset zone was more tract connected to regions of seizure spread than to non-involved regions (p<0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared to non-involved regions. In seizure free patients only, regions of seizure spread were more tract connected to the seizure onset zone than to other regions of spread (p<0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract connected to regions of early spread compared to regions of late spread in seizure free patients only (p<0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone may be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds may be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship may eventually provide insight into selecting the correct targets for epilepsy surgery.

Electroencephalography-functional magnetic resonance imaging for clinical evaluation in focal epilepsy.

OBJECTIVE: We aimed to evaluate the contribution of simultaneous recording of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) in the diagnosis of epilepsy syndrome, localization of the epileptogenic zone (EZ), and decision-making regarding surgical treatment. METHODS: We performed a retrospective study to evaluate patients with focal epilepsy who underwent EEG-fMRI. Two evaluators assessed epilepsy syndrome, presumed focus, and surgical candidacy and defined confidence levels. They assessed these clinical characteristics first without EEG-fMRI and then including EEG-fMRI to assess how the results of EEG-fMRI changed the evaluations. We also determined how the clinical evaluation was affected by the concordance level between the blood oxygen level-dependent (BOLD) response and the presumed focus location, and by the confidence level of the BOLD response itself based on the t-value of the primary and secondary clusters. RESULTS: Fifty-one scans from 48 patients were included. The BOLD map affected 66.7% of the evaluations by altering evaluation items (epilepsy syndrome, presumed focus, or surgical candidacy) or their confidence levels. EEG-fMRI results increased the confidence levels of epilepsy syndrome, presumed focus, or surgical candidacy in 47.1% of patients but reduced clinical confidence in these features in 11.8%. More specifically, the confidence levels increased for epilepsy syndrome in 28.5%, identification of presumed focus in 33.9%, and determination of surgical candidacy in 29.4%. The BOLD signal confidence level, whether high or low, did not influence these clinical factors. SIGNIFICANCE: Previous studies have emphasized the utility of EEG-fMRI for the localization of the epileptogenic zone. This study demonstrated the potential of EEG-fMRI to influence clinical confidence when determining epilepsy syndrome, the presumed epileptic focus, and surgical candidacy.

Rapid eye movement sleep affects interictal epileptic activity differently in mesiotemporal and neocortical areas.

OBJECTIVE: Rapid eye movement (REM) sleep reduces the rate and extent of interictal epileptiform discharges (IEDs). Breakthrough epileptic activity during REM sleep is therefore thought to best localize the seizure onset zone (SOZ). We utilized polysomnography combined with direct cortical recordings to investigate the influences of anatomical locations and the time of night on the suppressive effect of REM sleep on IEDs. METHODS: Forty consecutive patients with drug-resistant focal epilepsy underwent combined polysomnography and stereo-electroencephalography during presurgical evaluation. Ten-minute interictal epochs were selected 2 h prior to sleep onset (wakefulness), and from the first and second half of the night during non-REM (NREM) sleep and REM sleep. IEDs were detected automatically across all channels. Anatomic localization, time of night, and channel type (within or outside the SOZ) were tested as modulating factors. RESULTS: Relative to wakefulness, there was a suppression of IEDs by REM sleep in neocortical regions (median = -27.6%), whereas mesiotemporal regions showed an increase in IEDs (19.1%, p = .01, d = .39). This effect was reversed when comparing the regional suppression of IEDs by REM sleep relative to NREM sleep (-35.1% in neocortical, -58.7% in mesiotemporal, p < .001, d = .39). Across all patients, no clinically relevant novel IED regions were observed in REM sleep versus NREM or wakefulness based on our predetermined thresholds (4 IEDs/min in REM, 0 IEDs/min in NREM and wakefulness). Finally, there was a reduction in IEDs in late (NREM: 1.08/min, REM: .61/min) compared to early sleep (NREM: 1.22/min, REM: .69/min) for both NREM (p < .001, d = .21) and REM (p = .04, d = .14). SIGNIFICANCE: Our results demonstrate a spatiotemporal effect of IED suppression by REM sleep relative to wakefulness in neocortical but not mesiotemporal regions, and in late versus early sleep. This suggests the importance of considering sleep stage interactions and the potential influences of anatomical locations when using IEDs to define the epileptic focus.

The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy.

OBJECTIVE: Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics. METHODS: We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included. RESULTS: The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51). INTERPRETATION: In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.

Estimation of fMRI responses related to epileptic discharges using Bayesian hierarchical modeling.

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a unique and noninvasive method for epilepsy presurgical evaluation. When selecting voxels by null-hypothesis tests, the conventional analysis may overestimate fMRI response amplitudes related to interictal epileptic discharges (IEDs), especially when IEDs are rare. We aimed to estimate fMRI response amplitudes represented by blood oxygen level dependent (BOLD) percentage changes related to IEDs using a hierarchical model. It involves the local and distributed hemodynamic response homogeneity to regularize estimations. Bayesian inference was applied to fit the model. Eighty-two epilepsy patients who underwent EEG-fMRI and subsequent surgery were included in this study. A conventional voxel-wise general linear model was compared to the hierarchical model on estimated fMRI response amplitudes and on the concordance between the highest response cluster and the surgical cavity. The voxel-wise model overestimated fMRI responses compared to the hierarchical model, evidenced by a practically and statistically significant difference between the estimated BOLD percentage changes. Only the hierarchical model differentiated brief and long-lasting IEDs with significantly different BOLD percentage changes. Overall, the hierarchical model outperformed the voxel-wise model on presurgical evaluation, measured by higher prediction performance. When compared with a previous study, the hierarchical model showed higher performance metric values, but the same or lower sensitivity. Our results demonstrated the capability of the hierarchical model of providing more physiologically reasonable and more accurate estimations of fMRI response amplitudes induced by IEDs. To enhance the sensitivity of EEG-fMRI for presurgical evaluation, it may be necessary to incorporate more appropriate spatial priors and bespoke decision strategies.

Interictal high-frequency oscillations, spikes, and connectivity profiles: A fingerprint of epileptogenic brain pathologies.

OBJECTIVE: Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug-resistant epilepsy patients. Epileptic spikes, high-frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for preresective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies. METHODS: Interictal awake stereoelectroencephalographic recordings of 33 patients with focal drug-resistant epilepsy with seizure-free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross-correlation, and phase consistency) were computed for neighboring electrode pairs. RESULTS: Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures, with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy. SIGNIFICANCE: The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery.

Regional variability in intracerebral properties of NREM to REM sleep transitions in humans.

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas.

BACKGROUND: Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. METHOD: We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas. RESEARCH: mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. RESULTS: The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed. CONCLUSION: This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.

A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome.

OBJECTIVE: Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. METHODS: We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. RESULTS: The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. INTERPRETATION: Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.

Integration of white matter architecture to stereo-EEG better describes epileptic spike propagation.

OBJECTIVE: Stereo-electroencephalography (SEEG)-derived epilepsy networks are used to better understand a patient's epilepsy; however, a unimodal approach provides an incomplete picture. We combine tractography and SEEG to determine the relationship between spike propagation and the white matter architecture and to improve our understanding of spike propagation mechanisms. METHODS: Probablistic tractography from diffusion imaging (dMRI) of matched subjects from the Human Connectome Project (HCP) was combined with patient-specific SEEG-derived spike propagation networks. Two regions-of-interest (ROIs) with a significant spike propagation relationship constituted a Propagation Pair. RESULTS: In 56 of 59 patients, Propagation Pairs were more often tract-connected as compared to all ROI pairs (p < 0.01; d = -1.91). The degree of spike propagation between tract-connected ROIs was greater (39 ± 21%) compared to tract-unconnected ROIs (31 ± 18%; p < 0.0001). Within the same network, ROIs receiving propagation earlier were more often tract-connected to the source (59.7%) as compared to late receivers (25.4%; p < 0.0001). CONCLUSIONS: Brain regions involved in spike propagation are more likely to be connected by white matter tracts. Between nodes, presence of tracts suggests a direct course of propagation, whereas the absence of tracts suggests an indirect course of propagation. SIGNIFICANCE: We demonstrate a logical and consistent relationship between spike propagation and the white matter architecture.

Focal epilepsy impacts rapid eye movement sleep microstructure.

STUDY OBJECTIVES: Whereas there is plenty of evidence on the influence of epileptic activity on non-rapid eye movement (NREM) sleep macro- and micro-structure, data on the impact of epilepsy on rapid eye movement (REM) sleep remains sparse. Using high-density electroencephalography (HD-EEG), we assessed global and focal disturbances of sawtooth waves (STW) as cortically generated sleep oscillations of REM sleep in patients with focal epilepsy. METHODS: Twenty-two patients with drug-resistant focal epilepsy (13 females; mean age, 32.6 ± 10.7 years; 12 temporal lobe epilepsy) and 12 healthy controls (3 females; 24.0 ± 3.2 years) underwent combined overnight HD-EEG and polysomnography. STW rate, duration, frequency, power, spatial extent, IED rates and sleep homeostatic properties were analyzed. RESULTS: STW rate and duration were reduced in patients with focal epilepsy compared to healthy controls (rate: 0.64/min ± 0.46 vs. 1.12/min ± 0.41, p = .005, d = -0.98; duration: 3.60 s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01). Not surprisingly given the fronto-central maximum of STW, the reductions were driven by extratemporal lobe epilepsy patients (rate: 0.45/min ± 0.31 vs. 1.12/min ± 0.41, p = .0004, d = -1.35; duration: 3.49 s ± 0.92 vs. 4.57 ± 1.00, p = .017, d = -0.99) and were more pronounced in the first vs. the last sleep cycle (rate first cycle patients vs. controls: 0.60/min ± 0.49 vs. 1.10/min ± 0.55, p = .016, d = -0.90, rate last cycle patients vs. controls: 0.67/min ± 0.51 vs. 0.99/min ± 0.49, p = .11, d = -0.62; duration first cycle patients vs. controls: 3.60s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01, duration last cycle patients vs. controls: 3.66s ± 0.84 vs. 4.51 ± 1.26, p = .039, d = -0.80). There was no regional decrease of STWs in the region with the epileptic focus vs. the contralateral side (all p > .05). CONCLUSION: Patients with focal epilepsy and in particular extratemporal lobe epilepsy show a global reduction of STW activity in REM sleep. This may suggest that epilepsy impacts cortically generated sleep oscillations even in REM sleep when epileptic activity is low.

Understanding Heightened Seizure Risk: The Pro-Ictal State.

The large-scale neuronal networks that underpin normal brain function are disrupted during seizures, which are characterized by a transition to abnormal, hypersynchronous neuronal activity. Many factors can contribute to transitions from interictal to ictal states, and an enduring predisposition to spontaneous, dynamic changes results in recurrent seizures - that is, epilepsy. Unpredictability and the apparent randomness of seizure occurrence seem to be a hallmark of many epilepsies, yet clinicians and patients are aware of periods during which a variety of converging factors may increase the risk of seizures.

"Generalized-to-focal" epilepsy: stereotactic EEG and high-frequency oscillation patterns

Objective: We aimed to clarify the pathophysiology of epilepsy involving seizures with apparently generalized onset, progressing to focal ictal rhythm through stereotactic EEG (SEEG) implantation, recording, stimulation and high-frequency oscillation (HFO) analysis. Methods: We identified two patients with seizures with bilateral electrographic onset evolving to focal ictal rhythm, who underwent SEEG implantation. Patients had pre-surgical epilepsy work-up, including prolonged video scalp EEG, brain MRI, PET, ictal/interictal SPECT, MEG, and EEG-fMRI prior to SEEG implantation. Results: Both patients had childhood-onset seizures involving behavioural arrest and left versive head and eye deviation, evolving to bilateral tonic-clonic convulsions. Seizures were electrographically preceded by diffuse, bilateral 3-Hz activity resembling absence seizures. Both had suspected focal lesions based on neuroimaging, including 3T MRI and voxel-based post-processing in one patient. Electrode stimulation did not elicit any habitual electroclinical seizures. HFO analysis showed bilateral focal regions with high fast-ripple rates. Significance: "Generalized-to-focal" seizures may occur due to a diffuse, bilateral epileptic network, however, both patients showed ictal evolution from a generalized pattern to a single dominant focus which may explain why the focal aspect of their seizures had a consistent clinical semiology. Patients such as these may have a unique form of generalized epilepsy, but focal/multifocal cerebral abnormalities are also a possibility.

A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.

Electroencephalography-functional magnetic resonance imaging of epileptiform discharges: Noninvasive investigation of the whole brain.

Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) is a unique and noninvasive method for investigating epileptic activity. Interictal epileptiform discharge-related EEG-fMRI provides cortical and subcortical blood oxygen level-dependent (BOLD) signal changes specific to epileptic discharges. As a result, EEG-fMRI has revealed insights into generators and networks involved in epileptic activity in different types of epilepsy, demonstrating-for instance-the implication of the thalamus in human generalized spike and wave discharges and the role of the default mode network in absences and focal epilepsy, and has suggested a mechanism for the cortico-subcortical interactions in Lennox-Gastaut syndrome discharges. EEG-fMRI can find deep sources of epileptic activity not available to scalp EEG or magnetoencephalography, and provides critical new information to delineate the epileptic focus when considering surgical treatment or electrode implantation. In recent years, methodological advances, such as artifact removal and automatic detection of events, have rendered this method easier to implement, and its clinical potential has since been established by evidence of the impact of BOLD response on clinical decision-making and of the relationship between concordance of BOLD responses with extent of resection and surgical outcome. This review presents the recent developments in EEG-fMRI methodology and EEG-fMRI studies in different types of epileptic disorders as follows: EEG-fMRI acquisition, gradient and pulse artifact removal, statistical analysis, clinical applications, presurgical evaluation, altered physiological state in generalized genetic epilepsy, and pediatric EEG-fMRI studies.

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.

SleepSEEG: automatic sleep scoring using intracranial EEG recordings only.

Objective.To perform automatic sleep scoring based only on intracranial electroencephalography (iEEG), without the need for scalp EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction.Approach. Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch.Main results. An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm using iEEG alone and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%.Significance. The automatic algorithm can perform sleep scoring of long-term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.