Timing matters for accurate identification of the epileptogenic zone.

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

The utility of intraoperative ECoG in tumor-related epilepsy: Systematic review.

OBJECT: Epilepsy is one of the most common clinical manifestations of primary brain tumors. Intraoperative electrocorticography (ECoG) has been widely used in tumor resection. We aim to describe the indication and utility of ECoG during brain tumor surgery. METHODS: We performed a systematic review of the literature on the prognosis of tumor-related epilepsy surgery guided by intraoperative ECoG. The published studies were searched in PubMed, Embase, and Web of Science using the keyword 'seizure' or 'epilepsy' and 'electrocorticography' or 'ECoG'. Two reviewer authors screened studies and extracted data independently. RESULTS: Thirteen studies included 569 patients were finally selected, of which eight investigated medically intractable epilepsy. Three publications described temporal tumor-related epilepsy. All included studies were retrospective, and the age of all patients ranged from 1 to 71 years. The duration of epilepsy ranged from 1 month to 30 years. Patients with tumor-related epilepsy underwent surgical treatment with Engel I outcomes ranging from 56.5%-100%. CONCLUSION: Intraoperative ECoG is generally considered a useful technique in delineating epileptogenic areas and improving the prognosis of surgical treatment of tumor-related epilepsy. However, large-scale randomized control trials are still needed to verify these findings and formulate appropriate surgical strategies.

MEG detection of high frequency oscillations and intracranial-EEG validation in pediatric epilepsy surgery.

OBJECTIVE: To assess the feasibility of automatically detecting high frequency oscillations (HFOs) in magnetoencephalography (MEG) recordings in a group of ten paediatric epilepsy surgery patients who had undergone intracranial electroencephalography (iEEG). METHODS: A beamforming source-analysis method was used to construct virtual sensors and an automatic algorithm was applied to detect HFOs (80-250 Hz). We evaluated the concordance of MEG findings with the sources of iEEG HFOs, the clinically defined seizure onset zone (SOZ), the location of resected brain structures, and with post-operative outcome. RESULTS: In 8/9 patients there was good concordance between the sources of MEG HFOs and iEEG HFOs and the SOZ. Significantly more HFOs were detected in iEEG relative to MEG t(71) = 2.85, p < .05. There was good concordance between sources of MEG HFOs and the resected area in patients with good and poor outcome, however HFOs were also detected outside of the resected area in patients with poor outcome. CONCLUSION: Our findings demonstrate the feasibility of automatically detecting HFOs non-invasively in MEG recordings in paediatric patients, and confirm compatibility of results with invasive recordings. SIGNIFICANCE: This approach provides support for the non-invasive detection of HFOs to aid surgical planning and potentially reduce the need for invasive monitoring, which is pertinent to paediatric patients.

Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats.

PURPOSE: During the last decade, the reported prevalence of sleep-disordered breathing in adults has been rapidly increasing. Therefore, automatic methods of sleep assessment are of particular interest. In a framework of translational neuroscience, this study introduces a reliable automatic detection system of behavioral sleep in laboratory rats based on the signal recorded at the cortical surface without requiring electromyography. METHODS: Experimental data were obtained in 16 adult male WAG/Rij rats at the age of 9 months. Electrocorticographic signals (ECoG) were recorded in freely moving rats during the entire day (22.5 ± 2.2 h). Automatic wavelet-based assessment of behavioral sleep (BS) was proposed. The performance of this wavelet-based method was validated in a group of rats with genetic predisposition to absence epilepsy (n=16) based on visual analysis of their behavior in simultaneously recorded video. RESULTS: The accuracy of automatic sleep detection was 98% over a 24-h period. An automatic BS assessment method can be adjusted for detecting short arousals during sleep (microarousals) with various duration. CONCLUSIONS: These findings suggest that automatic wavelet-based assessment of behavioral sleep can be used for assessment of sleep quality. Current analysis indicates a temporal relationship between microarousals, sleep, and epileptic discharges in genetically prone subjects.

Accuracy of omni-planar and surface casting of epileptiform activity for intracranial seizure localization.

OBJECTIVE: Intracranial electroencephalography (ICEEG) recordings are performed for seizure localization in medically refractory epilepsy. Signal quantifications such as frequency power can be projected as heatmaps on personalized three-dimensional (3D) reconstructed cortical surfaces to distill these complex recordings into intuitive cinematic visualizations. However, simultaneously reconciling deep recording locations and reliably tracking evolving ictal patterns remain significant challenges. METHODS: We fused oblique magnetic resonance imaging (MRI) slices along depth probe trajectories with cortical surface reconstructions and projected dynamic heatmaps using a simple mathematical metric of epileptiform activity (line-length). This omni-planar and surface casting of epileptiform activity approach (OPSCEA) thus illustrated seizure onset and spread among both deep and superficial locations simultaneously with minimal need for signal processing supervision. We utilized the approach on 41 patients at our center implanted with grid, strip, and/or depth electrodes for localizing medically refractory seizures. Peri-ictal data were converted into OPSCEA videos with multiple 3D brain views illustrating all electrode locations. Five people of varying expertise in epilepsy (medical student through epilepsy attending level) attempted to localize the seizure-onset zones. RESULTS: We retrospectively compared this approach with the original ICEEG study reports for validation. Accuracy ranged from 73.2% to 97.6% for complete or overlapping onset lobe(s), respectively, and ~56.1% to 95.1% for the specific focus (or foci). Higher answer certainty for a given case predicted better accuracy, and scorers had similar accuracy across different training levels. SIGNIFICANCE: In an era of increasing stereo-EEG use, cinematic visualizations fusing omni-planar and surface functional projections appear to provide a useful adjunct for interpreting complex intracranial recordings and subsequent surgery planning.

Preictal variability of high-frequency oscillation rates in refractory epilepsy.

OBJECTIVE: High-frequency oscillations (HFOs) have shown promising utility in the spatial localization of the seizure onset zone for patients with focal refractory epilepsy. Comparatively few studies have addressed potential temporal variations in HFOs, or their role in the preictal period. Here, we introduce a novel evaluation of the instantaneous HFO rate through interictal and peri-ictal epochs to assess their usefulness in identifying imminent seizure onset. METHODS: Utilizing an automated HFO detector, we analyzed intracranial electroencephalographic data from 30 patients with refractory epilepsy undergoing long-term presurgical evaluation. We evaluated HFO rates both as a 30-minute average and as a continuous function of time and used nonparametric statistical methods to compare individual and population-level differences in rate during peri-ictal and interictal periods. RESULTS: Mean HFO rate was significantly higher for all epochs in seizure onset zone channels versus other channels. Across the 30 patients of our cohort, we found no statistically significant differences in mean HFO rate during preictal and interictal epochs. For continuous HFO rates in seizure onset zone channels, however, we found significant population-wide increases in preictal trends relative to interictal periods. Using a data-driven analysis, we identified a subset of 11 patients in whom either preictal HFO rates or their continuous trends were significantly increased relative to those of interictal baseline and the rest of the population. SIGNIFICANCE: These results corroborate existing findings that HFO rates within epileptic tissue are higher during interictal periods. We show this finding is also present in preictal, ictal, and postictal data, and identify a novel biomarker of preictal state: an upward trend in HFO rate leading into seizures in some patients. Overall, our findings provide preliminary evidence that HFOs can function as a temporal biomarker of seizure onset.

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.

One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.

Spike-associated networks and intracranial electrographic findings.

Functional connectivity is providing new insights into the network nature of epilepsy with growing clinical applications. Our objective was to validate a novel magnetoencephalography-based method to non-invasively measure the epileptic network. We retrospectively identified pediatric and adult patients with refractory focal epilepsy who underwent pre-surgical magnetoencephalography with subsequent intracranial electrographic monitoring. Magnetoencephalography tracings were visually reviewed, and interictal epileptiform discharges ("spikes") were individually marked. We then evaluated differences in whole-brain connectivity during brief epochs preceding the spikes and during the spikes using the Network-Based Statistic to test differences at the network level. In six patients with statistically-significant network differences, we observed substantial overlap between the spike-associated networks and electrographically active areas identified during intracranial monitoring (the spike-associated network was 78% and 83% sensitive for intracranial electroencephalography-defined regions in the irritative and seizure onset zones, respectively). These findings support the neurobiological validity of the spike-associated network method. Assessment of spike-associated networks has the potential to improve surgical planning in epilepsy surgery patients by identifying components of the epileptic network prior to implantation.

Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery.

OBJECTIVE: To assess the ability of high-density Electroencephalography (HD-EEG) and magnetoencephalography (MEG) to localize interictal ripples, distinguish between ripples co-occurring with spikes (ripples-on-spike) and independent from spikes (ripples-alone), and evaluate their localizing value as biomarkers of epileptogenicity in children with medically refractory epilepsy. METHODS: We retrospectively studied 20 children who underwent epilepsy surgery. We identified ripples on HD-EEG and MEG data, localized their generators, and compared them with intracranial EEG (icEEG) ripples. When ripples and spikes co-occurred, we performed source imaging distinctly on the data above 80 Hz (to localize ripples) and below 70 Hz (to localize spikes). We assessed whether missed resection of ripple sources predicted poor outcome, separately for ripples-on-spikes and ripples-alone. Similarly, predictive value of spikes was calculated. RESULTS: We observed scalp ripples in 16 patients (10 good outcome). Ripple sources were highly concordant to the icEEG ripples (HD-EEG concordance: 79%; MEG: 83%). When ripples and spikes co-occurred, their sources were spatially distinct in 83-84% of the cases. Removing the sources of ripples-on-spikes predicted good outcome with 90% accuracy for HD-EEG (P = 0.008) and 86% for MEG (P = 0.044). Conversely, removing ripples-alone did not predict outcome. Resection of spike sources (generated at the same time as ripples) predicted good outcome for HD-EEG (P = 0.036; accuracy = 87%), while did not reach significance for MEG (P = 0.1; accuracy = 80%). INTERPRETATION: HD-EEG and MEG localize interictal ripples with high precision in children with refractory epilepsy. Scalp ripples-on-spikes are prognostic, noninvasive biomarkers of epileptogenicity, since removing their cortical generators predicts good outcome. Conversely, scalp ripples-alone are most likely generated by non-epileptogenic areas.

Cortical hemodynamic responses induced by low-intensity transcranial ultrasound stimulation of mouse cortex.

Ultrasound-mediated neuromodulation is emerging as a key technology for targeted noninvasive brain stimulation, but key insights into its effects and dose-response characteristics are still missing. The purpose of this study is to systematically evaluate the effect of low-intensity transcranial ultrasound stimulation (TUS) on complementary aspects of cerebral hemodynamic. We simultaneously record the EMG signal, local field potential (LFP) and cortical blood flow (CBF) using electrophysiological recording and laser speckle contrast imaging under ultrasound stimulation to simultaneously monitor motor responses, neural activities and hemodynamic changes during the application of low-intensity TUS in mouse motor cortex, using excitation pulses which caused whisker and tail movement. Our experimental results demonstrate interdependent TUS-induced motor, neural activity and hemodynamic responses that peak approximately 0.55s, 1.05s and 2.5s after TUS onset, respectively, and show a linear coupling relationship between their respective varying response amplitudes to repeated stimuli. We also found monotonic dose-response parametric relations of the CBF peak value increase as a function of stimulation intensity and duration, while stimulus duty-cycle had only a weak effect on peak responses. These findings demonstrate that TUS induces a change in cortical hemodynamics and LSCI provide a high temporal resolution view of these changes.

Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network.

Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networks (CNN), is proposed to detect seizure onsets in long-term intracranial EEG recordings. Primarily, raw EEG data is filtered with wavelet decomposition. Then, S-transform is used to obtain a proper time-frequency representation of each EEG segment. After that, a 15-layer deep CNN using dropout and batch normalization serves as a robust feature extractor and classifier. Finally, smoothing and collar technique are applied to the outputs of CNN to improve the detection accuracy and reduce the false detection rate (FDR). The segment-based and event-based evaluation assessments and receiver operating characteristic (ROC) curves are employed for the performance evaluation on a public EEG database containing 21 patients. A segment-based sensitivity of 97.01% and a specificity of 98.12% are yielded. For the event-based assessment, this method achieves a sensitivity of 95.45% with an FDR of 0.36/h.

Misleading non-epileptic epileptiform activities on intracranial recordings.

Numerous non-epileptic physiological electroencephalographic (EEG) patterns morphologically mimic epileptiform activity. However, misleading non-epileptic findings of electrocorticography (ECoG) have not yet been examined in detail. The aim of the present study was to identify non-epileptic epileptiform ECoG findings. We retrospectively reviewed the intracranial recordings of 21 patients with intractable focal epilepsy who became seizure-free after a presurgical evaluation with subdural electrodes following resective surgeries at Sapporo Medical University between January 2014 and December 2018. Morphological epileptiform findings outside epileptogenic areas were judged as non-epileptic and analyzed. Seventeen areas in nine patients exhibited non-epileptic epileptiform activities. These areas were identified in the lateral temporal cortices, basal temporal areas, rolandic areas, and frontal lobe. Morphological patterns were classified into three types: 1) spiky oscillations, 2) isolated spiky activity, and 3) isolated fast activity. The normal cortex may exhibit non-epileptic epileptiform activities. These activities need to be carefully differentiated from real epileptic abnormalities to prevent the mislocalization of epileptogenic areas.

Surgical techniques: Stereoelectroencephalography-guided radiofrequency-thermocoagulation (SEEG-guided RF-TC).

Stereoelectroencephalography-guided radiofrequency-thermocoagulation (SEEG-guided RF-TC) consists of coupling SEEG investigation with RF-TC stereotactic lesioning directly through the recording electrodes. In this systematic review the surgical technique, indications, and outcomes are described. Maximum accuracy is reached when a frame-based procedure with a robotic assistance and a per-operative vascular X-ray imaging are performed. Monitoring of the lesioning procedure based on the impedance, a sharp modification of which indicates that the thermocoagulation has reached its maximum volume, allows the optimization of the lesion size. The first indication concerns patients in whom a SEEG is required to determine whether surgery is feasible and in whom resection is indeed possible. Even if surgery is performed owing to insufficient efficacy of SEEG-guided RF-TC, the procedure remains interesting owing to its high positive predictive value for good outcome after surgery. The second indication concerns patients in whom phase I non-invasive investigations have concluded to surgical contraindication and who may still undergo SEEG in a purely therapeutic perspective (small deep zones inaccessible to surgery and network nodes of large epileptic networks). Lastly, SEEG-guided RF-TC can be considered as a first-line treatment for periventricular nodular heterotopia (PNH). Independently of indication, the overall seizure-free rate is 23% and the responder rate is 58%. The best results are obtained for PNH (38% seizure-free and 81% responders), while the worst results have been reported for temporal lobe-epilepsy in a dedicated study. The overall complication rate is 2.5%. More evidence is needed to help determine the exact place of SEEG-guided RF-TC in the surgical management algorithm.

Electrical stimulation mapping in children.

Electrical stimulation mapping is a longstanding practice that aids in identification and delineation of eloquent cortex. Initially used to expand our understanding of the typical human cortex, it now plays a significant role in mapping cortical function in individuals with atypical structural and functional tissue organization undergoing epilepsy surgery. This review discusses the unique challenges that arise in the functional testing of the immature cortex of a child and the parameters of stimulation that optimize accurate results in conventional open implantation and in stereo-electroencephalography. The prerequisite baseline evaluation and preparation recommended to increase the yield from pediatric stimulation mapping sessions is described, as are ideal approaches to the mapping of the sensory, motor, language, and visual cortices.

DC shifts, high frequency oscillations, ripples and fast ripples in relation to the seizure onset zone.

Efforts to improve epilepsy surgery outcomes have led to increased interest in the study of electroencephalographic oscillations outside the conventional EEG bands. These include fast activity above the gamma band, known as high frequency oscillations (HFOs), and infraslow activity (ISA) below the delta band, sometimes referred to as direct current (DC) or ictal baseline shifts (IBS). HFOs in particular have been extensively studied as potential biomarkers for epileptogenic tissue in light of evidence showing that resection of brain tissue containing HFOs is associated with good surgical outcomes. Not all HFOs are conclusively pathological, however, as they can be recorded in nonepileptic tissue and induced by cognitive, visual, or motor tasks. Consequently, efforts to distinguish between pathological and physiological HFOs have identified several traits specific to pathological HFOs, such as coupling with interictal spikes, association with delta waves, and stereotypical morphologies. On the opposite end of the EEG spectrum, sub-delta oscillations have been shown to co-localize with the seizure onset zones (SOZ) and appear in a narrower spatial distribution than activity in the conventional EEG frequency bands. In this report, we review studies that implicate HFOs and ISA in ictogenesis and discuss current limitations such as inter-observer variability and poor standardization of recording techniques. Furthermore, we propose that HFOs and ISA should be analyzed in addition to activity in the conventional EEG band during intracranial presurgical EEG monitoring to identify the best possible surgical margin.

Invasive evaluation in children (SEEG vs subdural grids).

Designed from the 60s to the 80s for adults, and despite the development of many new techniques, invasive explorations still have indications in children with focal drug-resistant epilepsy. The main types are stereoelectroencephalography (SEEG) and subdural explorations (SDE). They provide precise information on the localization of the epileptogenic zone (EZ), its relationships with eloquent cortex, and the feasibility of performing a tailored surgical resection. Thermocoagulations, which are a diagnostic and therapeutic tool, can be performed using SEEG electrodes. Both techniques are feasible in children, with an age limitation for SEEG (which requires a bone thickness above 2 mm). The complication rate is higher with SDE. Opposed for a long time and never compared in a systematic study, they should presently be considered complementary. The indications cannot be directly inferred from those for adults, as there are pediatric particularities in the seizures' semiology, functional areas, imaging and urgent situations. We successively discuss the choice in individual cases of SEEG or SDE respectively, the specific problematic in infancy and early childhood, the schema in SEEG for cryptogenic epilepsies (in particular insular), the particularities of polymicrogyria and deeply located lesions, and finally, SEEG designed for thermocoagulations. Future improvements should include more accurate implantation schemas thanks to advanced non-invasive explorations and possibilities to perform SEEG in infants.

Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation.

Comparing electric field simulations from individualized head models against in-vivo intra-cranial recordings is considered the gold standard for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalography. The measurements also help to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that modeling differences in the pipelines lead to notable differences in the simulated electric field distributions that are often large enough to change the conclusions regarding the dose distribution and strength in the brain. Specifically, differences in the automatic segmentations of the head anatomy from structural magnetic resonance images are a major factor contributing to the observed field differences. However, the differences in the simulated fields are not reflected in the comparison between the simulations and intra-cranial measurements. This apparent mismatch is partly explained by the noisiness of the intra-cranial measurements, which renders comparisons between the methods inconclusive. We further demonstrate that a standard regression analysis, which ignores uncertainties in the simulations, leads to a strong bias in the estimated linear relationship between simulated and measured fields. Ignoring this bias leads to the incorrect conclusion that the models systematically misestimate the field strength in the brain. We propose a new Bayesian regression analysis of the data that yields unbiased parameter estimates, along with their uncertainties, and gives further insights to the fit between simulations and measurements. Specifically, the unbiased results give only weak support for systematic misestimations of the fields by the models.

Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction.

The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

OBJECTIVE: Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings. METHODS: An epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation. RESULTS: On the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation. CONCLUSIONS: Using deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes. SIGNIFICANCE: Our algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.