High-frequency oscillations and seizure generation in neocortical epilepsy

Neocortical seizures are often poorly localized, explosive and widespread at onset, making them poorly amenable to epilepsy surgery in the absence of associated focal brain lesions. We describe, for the first time in an unselected group of patients with neocortical epilepsy, the finding that high-frequency (60-100 Hz) epileptiform oscillations are highly localized in the seizure onset zone, both before and temporally removed from seizure onset. These findings were observed in all six patients with neocortical epilepsy out of 23 consecutive patients implanted with intracranial electrodes for pre-surgical evaluation during the study period. The majority of seizures (62%) in these patients were anticipated by an increase in high-frequency activity in the 20 min prior to neocortical seizure onset. Contrary to observations in normal brain, high-frequency activity was strongly modulated by behavioural state, and was maximal during slow-wave sleep, which may explain the propensity for neocortical onset seizures to begin during sleep. These findings point to an important role for neuromodulatory circuits, probably involving the thalamus, in mechanisms underlying seizure generation in neocortical epilepsy. These findings demonstrate that high-frequency epileptiform oscillations may prove clinically useful in localizing the seizure onset zone in neocortical epilepsy, for identifying periods of increased probability of seizure onset, and in elucidating mechanisms underlying neocortical ictogenesis. Confirmation that prolonged bursts of high-frequency activity may predict focal onset neocortical seizures will require prospective validation on continuous, prolonged recordings in a larger number of patients. Importantly, the results show that the dynamic range utilized in current clinical practice for localization of epileptogenic brain largely ignores fundamental oscillations that are signatures of an epileptogenic brain. It may prove that many currently available clinical EEG systems and EEG analysis methods utilize a dynamic range that discards clinically important information.

High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings

Neuronal oscillations span a wide range of spatial and temporal scales that extend beyond traditional clinical EEG. Recent research suggests that high-frequency oscillations (HFO), in the ripple (80-250 Hz) and fast ripple (250-1000 Hz) frequency range, may be signatures of epileptogenic brain and involved in the generation of seizures. However, most research investigating HFO in humans comes from microwire recordings, whose relationship to standard clinical intracranial EEG (iEEG) has not been explored. In this study iEEG recordings (DC - 9000 Hz) were obtained from human medial temporal lobe using custom depth electrodes containing both microwires and clinical macroelectrodes. Ripple and fast-ripple HFO recorded from both microwires and clinical macroelectrodes were increased in seizure generating brain regions compared to control regions. The distribution of HFO frequencies recorded from the macroelectrodes was concentrated in the ripple frequency range, compared to a broad distribution of HFO frequencies recorded from microwires. The average frequency of ripple HFO recorded from macroelectrodes was lower than that recorded from microwires (143.3 +/- 49.3 Hz versus 116.3 +/- 38.4, Wilcoxon rank sum P<0.0001). Fast-ripple HFO were most often recorded on a single microwire, supporting the hypothesis that fast-ripple HFO are primarily generated by highly localized, sub-millimeter scale neuronal assemblies that are most effectively sampled by microwire electrodes. Future research will address the clinical utility of these recordings for localizing epileptogenic networks and understanding seizure generation.

Microseizures and the spatiotemporal scales of human partial epilepsy

Focal seizures appear to start abruptly and unpredictably when recorded from volumes of brain probed by clinical intracranial electroencephalograms. To investigate the spatiotemporal scale of focal epilepsy, wide-bandwidth electrophysiological recordings were obtained using clinical macro- and research microelectrodes in patients with epilepsy and control subjects with intractable facial pain. Seizure-like events not detectable on clinical macroelectrodes were observed on isolated microelectrodes. These 'microseizures' were sparsely distributed, more frequent in brain regions that generated seizures, and sporadically evolved into large-scale clinical seizures. Rare microseizures observed in control patients suggest that this phenomenon is ubiquitous, but their density distinguishes normal from epileptic brain. Epileptogenesis may involve the creation of these topographically fractured microdomains and ictogenesis (seizure generation), the dynamics of their interaction and spread.

Localization of the epileptic focus by low-resolution electromagnetic tomography in patients with a lesion demonstrated by MRI

Patients with medically intractable partial epilepsy and well-defined symptomatic MRI lesions were studied using phase-encoded frequency spectral analysis (PEFSA) combined with low-resolution electromagnetic tomography (LORETA). Ten patients admitted to the epilepsy monitoring unit with MRI-identified lesions and intractable partial epilepsy were studied using 31-electrode scalp EEG. The scalp electrodes were located in three-dimensional space using a magnetic digitizer and coregistered with the patient's MRI. PEFSA was used to obtain a phase-encoded scalp map for the ictal frequencies. The ictal generators were obtained from the scalp map using LORETA. In addition, the generators of interictal epileptogenic spikes were identified using time-domain LORETA. The LORETA generators were rostral to the MRI lesion in 87% (7/8) of patients with temporal lobe lesions, but all were located in the mesial temporal lobe in concordance with the patients' MRI lesions. In patients with frontal lobe epilepsy, the ictal generators at the time that the spectral power was maximal localized to the MRI lesions. Eight of 10 patients had interictal spikes, of which 4 were bilateral independent temporal lobe spikes. Only generators of the interictal spikes that were ipsilateral to seizure onset correlated with the ictal generators. LORETA combined with PEFSA of the ictal discharge can localize ictal EEG discharges accurately and improve correlation with brain anatomy by allowing coregistration of the ictal generator with the MRI. Analysis of interictal spikes was less useful than analysis of the ictal discharge.

Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings

OBJECTIVE

Recent studies indicate that pathologic high-frequency oscillations (HFOs) are signatures of epileptogenic brain. Automated tools are required to characterize these events. We present a new algorithm tuned to detect HFOs from 30 to 85 Hz, and validate it against human expert electroencephalographers.

METHODS

We randomly selected 28 3-min single-channel epochs of intracranial EEG (IEEG) from two patients. Three human reviewers and three automated detectors marked all records to identify candidate HFOs. Subsequently, human reviewers verified all markings.

RESULTS

A total of 1330 events were collectively identified. The new method presented here achieved 89.7% accuracy against a consensus set of human expert markings. A one-way ANOVA determined no difference between the mean F-measures of the human reviewers and automated algorithm. Human kappa statistics (mean kappa=0.38) demonstrated marginal identification consistency, primarily due to false negative errors.

CONCLUSIONS

We present an HFO detector that improves upon existing algorithms, and performs as well as human experts on our test data set. Validation of detector performance must be compared to more than one expert because of interrater variability.

SIGNIFICANCE

This algorithm will be useful for analyzing large EEG databases to determine the pathophysiological significance of HFO events in human epileptic networks.

Utility of an immunotherapy trial in evaluating patients with presumed autoimmune epilepsy

OBJECTIVE

To evaluate a trial of immunotherapy as an aid to diagnosis in suspected autoimmune epilepsy.

METHOD

We reviewed the charts of 110 patients seen at our autoimmune neurology clinic with seizures as a chief complaint. Twenty-nine patients met the following inclusion criteria: (1) autoimmune epilepsy suspected based on the presence of ≥ 1 neural autoantibody (n = 23), personal or family history or physical stigmata of autoimmunity, and frequent or medically intractable seizures; and (2) initiated a 6- to 12-week trial of IV methylprednisolone (IVMP), IV immune globulin (IVIg), or both. Patients were defined as responders if there was a 50% or greater reduction in seizure frequency.

RESULTS

Eighteen patients (62%) responded, of whom 10 (34%) became seizure-free; 52% improved with the first agent. Of those receiving a second agent after not responding to the first, 43% improved. A favorable response correlated with shorter interval between symptom onset and treatment initiation (median 9.5 vs 22 months; p = 0.048). Responders included 14/16 (87.5%) patients with antibodies to plasma membrane antigens, 2/6 (33%) patients seropositive for glutamic acid decarboxylase 65 antibodies, and 2/6 (33%) patients without detectable antibodies. Of 13 responders followed for more than 6 months after initiating long-term oral immunosuppression, response was sustained in 11 (85%).

CONCLUSIONS

These retrospective findings justify consideration of a trial of immunotherapy in patients with suspected autoimmune epilepsy.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that in patients with suspected autoimmune epilepsy, IVMP, IVIg, or both improve seizure control.

Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings

The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.

High-frequency oscillations and other electrophysiological biomarkers of epilepsy: clinical studies

Accurate localization of epileptogenic brain is critical for successful epilepsy surgery. Recent research using wide bandwidth intracranial EEG has demonstrated that interictal high-frequency oscillations are preferentially localized to the brain region generating spontaneous seizures, and are a potential biomarker of epileptogenic brain. The existence of an interictal, electrophysiological biomarker of epileptogenic brain has the potential to significantly advance epilepsy surgery by improving outcomes through improved localization and potentially eliminating the reliance on chronic intracranial EEG monitoring.

Stroke-like migraine attacks after radiation therapy (SMART) syndrome is not always completely reversible: a case series

We retrospectively reviewed clinical and imaging findings in 11 patients with stroke-like migraine attacks after radiation therapy (SMART) syndrome to better understand this disorder previously thought to be reversible. Six men and 5 women had complex bouts of neurologic impairment beginning, on average, 20 years after cerebral irradiation. All had characteristic, unilateral gyriform enhancement on MR imaging that developed within 2-7 days and typically resolved in 2-5 weeks. Unlike prior reports, 45% had incomplete neurologic recovery manifesting as dysphasia, cognitive impairment, or hemiparesis. The remaining 55% recovered completely over an average of 2 months. Three of 11 patients developed cortical laminar necrosis. Brain biopsies in 4 of 11 did not demonstrate a specific pathologic substrate. These additional 11 patients contribute to the understanding of variability in stroke-like migraine attacks after radiation therapy syndrome, which often but not uniformly manifests with headaches and seizures, demonstrates a typical evolution of imaging findings, and may result in permanent neurologic and imaging sequelae.

Physiological and pathological high frequency oscillations in focal epilepsy

Objective

This study investigates high‐frequency oscillations (HFOs; 65–600 Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFOs (pathHFO) from physiological HFOs (physHFO). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories.

Methods

We recorded HFOs using intracranial EEG (iEEG) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative physHFOs were induced by cognitive or motor tasks. HFOs were identified using validated detectors. A support vector machine (SVM) using HFO features was developed to classify tissue under individual electrodes as normal or epileptogenic.

Results

There was significant overlap in the amplitude, frequency, and duration distributions for spontaneous physHFO, task induced physHFO, and pathHFO, but the amplitudes of the pathHFO were higher (P < 0.0001). High gamma pathHFO had the strongest association with seizure onset zone (SOZ), and were elevated on SOZ electrodes in 70% of epilepsy patients (P < 0.0001). Failure to resect tissue generating high gamma pathHFO was associated with poor outcomes (P < 0.0001). A SVM classified individual electrodes as epileptogenic with 63.9% sensitivity and 73.7% specificity using SOZ as the target.

Interpretation

A broader range of interictal pathHFO (65–600 Hz) than previously recognized are biomarkers of epileptogenic brain, and are associated with SOZ and surgical outcome. Classification of HFOs into physiological or pathological remains challenging. Classification of tissue under individual electrodes was demonstrated to be feasible. The open source data and algorithms provide a resource for future studies.

Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus

Epileptogenic human hippocampus generates spontaneous energy fluctuations with a wide range of amplitude and temporal variation, which are often assumed to be entirely random. However, the temporal dynamics of these fluctuations are poorly understood, and the question of whether they exhibit persistent long-range temporal correlations (LRTC) remains unanswered. In this paper we use detrended fluctuation analysis (DFA) to show that the energy fluctuations in human hippocampus show LRTC with power-law scaling, and that these correlations differ between epileptogenic and non-epileptogenic hippocampus. The analysis shows that the energy fluctuations exhibit slower decay of the correlations in the epileptogenic hippocampus compared with the non-epileptogenic hippocampus. The DFA-derived scaling exponents demonstrate that there are LRTC of energy fluctuations in human hippocampus, and that the temporal persistence of energy fluctuations is characterized by a bias for large (small) energy fluctuations to be followed by large (small) energy fluctuations. Furthermore, we find that in the period of time leading up to seizures there is no change in the scaling exponents that characterize the LRTC of energy fluctuations. The fact that the LRTC of energy fluctuations do not change as seizures approach provides evidence that the local neuronal network dynamics do not change in the period before seizures, and that seizures in mesial temporal lobe epilepsy may be triggered by an influence that is external to the hippocampus. The presence of LRTC with power-law scaling does not imply a specific mechanism, but the finding that temporal correlations decay more slowly in epileptogenic hippocampus provides electrophysiologic evidence that the underlying neuronal dynamics are different within the epileptogenic hippocampus compared with contralateral hippocampus. We briefly discuss possible neurobiological mechanisms for LRTC of the energy fluctuations in hippocampus.

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic

It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

Ictal SPECT statistical parametric mapping in temporal lobe epilepsy surgery

OBJECTIVE

Although subtraction ictal SPECT coregistered to MRI (SISCOM) is clinically useful in epilepsy surgery evaluation, it does not determine whether the ictal-interictal subtraction difference is statistically different from the expected random variation between 2 SPECT studies. We developed a statistical parametric mapping and MRI voxel-based method of analyzing ictal-interictal SPECT difference data (statistical ictal SPECT coregistered to MRI [STATISCOM]) and compared it with SISCOM.

METHODS

Two serial SPECT studies were performed in 11 healthy volunteers without epilepsy (control subjects) to measure random variation between serial studies from individuals. STATISCOM and SISCOM images from 87 consecutive patients who had ictal SPECT studies and subsequent temporal lobectomy were assessed by reviewers blinded to clinical data and outcome.

RESULTS

Interobserver agreement between blinded reviewers was higher for STATISCOM images than for SISCOM images (kappa = 0.81 vs kappa = 0.36). STATISCOM identified a hyperperfusion focus in 84% of patients, SISCOM in 66% (p < 0.05). STATISCOM correctly localized the temporal lobe epilepsy (TLE) subtypes (mesial vs lateral neocortical) in 68% of patients compared with 24% by SISCOM (p = 0.02); subgroup analysis of patients without lesions (as determined by MRI) showed superiority of STATISCOM (80% vs 47%; p = 0.04). Moreover, the probability of seizure-free outcome was higher when STATISCOM correctly localized the TLE subtype than when it was indeterminate (81% vs 53%; p = 0.03).

CONCLUSION

Statistical ictal SPECT coregistered to MRI (STATISCOM) was superior to subtraction ictal SPECT coregistered to MRI for seizure localization before temporal lobe epilepsy (TLE) surgery. STATISCOM localization to the correct TLE subtype was prognostically important for postsurgical seizure freedom.

Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice

Visual scoring of murine EEG signals is time-consuming and subject to low inter-observer reproducibility. The Racine scale for behavioral seizure severity does not provide information about interictal or sub-clinical epileptiform activity. An automated algorithm for murine EEG analysis was developed using total signal variation and wavelet decomposition to identify spike, seizure, and other abnormal signal types in single-channel EEG collected from kainic acid-treated mice. The algorithm was validated on multi-channel EEG collected from γ-butyrolacetone-treated mice experiencing absence seizures. The algorithm identified epileptiform activity with high fidelity compared to visual scoring, correctly classifying spikes and seizures with 99% accuracy and 91% precision. The algorithm correctly identifed a spike-wave discharge focus in an absence-type seizure recorded by 36 cortical electrodes. The algorithm provides a reliable and automated method for quantification of multiple classes of epileptiform activity within the murine EEG and is tunable to a variety of event types and seizure categories.

A multi-feature and multi-channel univariate selection process for seizure prediction

OBJECTIVE

To develop a prospective method for optimizing seizure prediction, given an array of implanted electrodes and a set of candidate quantitative features computed at each contact location.

METHODS

The method employs a genetic-based selection process, and then tunes a probabilistic neural network classifier to predict seizures within a 10 min prediction horizon. Initial seizure and interictal data were used for training, and the remaining IEEG data were used for testing. The method continues to train and learn over time.

RESULTS

Validation of these results over two workshop patients demonstrated a sensitivity of 100%, and 1.1 false positives per hour for Patient E, using a 2.4s block predictor, and a failure of the method on Patient B.

CONCLUSIONS

This study demonstrates a prospective, exploratory implementation of a seizure prediction method designed to adapt to individual patients with a wide variety of pre-ictal patterns, implanted electrodes and seizure types. Its current performance is limited likely by the small number of input channels and quantitative features employed in this study, and segmentation of the data set into training and testing sets rather than using all continuous data available.

SIGNIFICANCE

This technique theoretically has the potential to address the challenge presented by the heterogeneity of EEG patterns seen in medication-resistant epilepsy. A more comprehensive implementation utilizing all electrode sites, a broader feature library, and automated multi-feature fusion will be required to fully judge the method's potential for predicting seizures.

Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank

The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.

Continuous energy variation during the seizure cycle: towards an on-line accumulated energy

OBJECTIVE

Increases in accumulated energy on intracranial EEG are associated with oncoming seizures in retrospective studies, supporting the idea that seizures are generated over time. Published seizure prediction methods require comparison to 'baseline' data, sleep staging, and selecting seizures that are not clustered closely in time. In this study, we attempt to remove these constraints by using a continuously adapting energy threshold, and to identify stereotyped energy variations through the seizure cycle (inter-, pre-, post- and ictal periods).

METHODS

Accumulated energy was approximated by using moving averages of signal energy, computed for window lengths of 1 and 20 min, and an adaptive decision threshold. Predictions occurred when energy within the shorter running window exceeded the decision threshold.

RESULTS

Predictions for time horizons of less than 3h did not achieve statistical significance in the data sets analyzed that had an average inter-seizure interval ranging from 2.9 to 8.6h. 51.6% of seizures across all patients exhibited stereotyped pre-ictal energy bursting and quiet periods.

CONCLUSIONS

Accumulating energy alone is not sufficient for predicting seizures using a 20 min running baseline for comparison. Stereotyped energy patterns through the seizure cycle may provide clues to mechanisms underlying seizure generation.

SIGNIFICANCE

Energy-based seizure prediction will require fusion of multiple complimentary features and perhaps longer running averages to compensate for post-ictal and sleep-induced energy changes.

Medial temporal lobe functional connectivity predicts stimulation-induced theta power

Focal electrical stimulation of the brain incites a cascade of neural activity that propagates from the stimulated region to both nearby and remote areas, offering the potential to control the activity of brain networks. Understanding how exogenous electrical signals perturb such networks in humans is key to its clinical translation. To investigate this, we applied electrical stimulation to subregions of the medial temporal lobe in 26 neurosurgical patients fitted with indwelling electrodes. Networks of low-frequency (5–13 Hz) spectral coherence predicted stimulation-evoked increases in theta (5–8 Hz) power, particularly when stimulation was applied in or adjacent to white matter. Stimulation tended to decrease power in the high-frequency broadband (HFB; 50–200 Hz) range, and these modulations were correlated with HFB-based networks in a subset of subjects. Our results demonstrate that functional connectivity is predictive of causal changes in the brain, capturing evoked activity across brain regions and frequency bands.

Direct electrical brain stimulation can induce widespread changes in neural activity, offering a means to modulate network-wide activity and treat disease. Here, the authors show that the low-frequency functional connectivity profile of a stimulation target predicts where induced theta activity occurs.

Chronic subthreshold cortical stimulation and stimulation-related EEG biomarkers for focal epilepsy

Brain stimulation offers an alternative to focal resection for the treatment of focal drug-resistant epilepsy. Chronic subthreshold cortical stimulation is an individualized biomarker-informed open-loop continuous electrical stimulation approach targeting the seizure onset zone and surrounding areas. Before permanent implantation, trial stimulation is performed during invasive monitoring to assess stimulation efficacy as well as to optimize stimulation location and parameters by modifying interictal EEG biomarkers. We present clinical and neurophysiological results from a retrospective analysis of 21 patients, showing a median percent reduction in seizure frequency of 100% and responder rate of 89% with a median follow-up of 27 months. About 40% of patients were free of disabling seizures for a 12-month period or longer. We find that stimulation-induced decreases in delta (1–4 Hz) power and increases in alpha and beta (8–20 Hz) power during trial stimulation correlate with improved long-term clinical outcomes. These results suggest chronic subthreshold cortical stimulation may be an effective alternative approach to treating focal drug-resistant epilepsy and that short-term stimulation-related changes in spectral power may be a useful interictal biomarker and relate to long-term clinical outcome.

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Rapidly progressive hippocampal atrophy: evidence for a seizure-induced mechanism

Hippocampal formation atrophy (HFA) developed in an adult, who did not have epilepsy previously, after the occurrence of new-onset partial seizures from acute thrombosis of an ipsilateral parietal venous angioma. There was no evidence of hippocampal injury, and the patient had only one brief, generalized tonic-clonic seizure. Although HFA progressed rapidly over 5.5 months, the partial seizures did not become prolonged or secondarily generalized. Evidence from the patient indicates that partial seizure activity can cause rapid and progressive hippocampal atrophy.

Responsive neurostimulation with low-frequency stimulation

Deep brain stimulation and responsive neurostimulation (RNS) use high-frequency stimulation (HFS) per the pivotal trials and manufacturer-recommended therapy protocols. However, not all patients respond to HFS. In this retrospective case series, 10 patients implanted with the RNS System were programmed with low-frequency stimulation (LFS) to treat their seizures; nine of these patients were previously treated with HFS (100 Hz or greater). LFS was defined as frequency < 10 Hz. Burst duration was increased to at least 1000 ms. With HFS, patients had a median seizure reduction (MSR) of 13% (interquartile range [IQR] = -67 to 54) after a median of 19 months (IQR = 8-49). In contrast, LFS was associated with a 67% MSR (IQR = 13-95) when compared to HFS and 76% MSR (IQR = 43-91) when compared to baseline prior to implantation. Charge delivered per hour and pulses per day were not significantly different between HFS and LFS, although time stimulated per day was longer for LFS (228 min) than for HFS (7 min). There were no LFS-specific adverse effects reported by any of the patients. LFS could represent an alternative, effective method for delivering stimulation in focal drug-resistant epilepsy patients treated with the RNS System.