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ObjectiveWe present a semi-automated method for quantifying structural changes after epilepsy surgery that accounts for tissue deformation caused by resection. We demonstrate its utility by comparing the remote structural effects of two surgical approaches, the anterior temporal lobectomy (ATL) and the selective amygdalohippocampectomy (SAH).MethodsWe studied 37 temporal lobe epilepsy (TLE) patients who underwent resective surgery. Patients were treated with either an anterior temporal lobectomy (ATL, N=21) or a selective amygdalohippocampectomy (SAH, N=16). All patients received same-scanner MR imaging preoperatively and postoperatively (5+ months after surgery). To analyze structural changes in remote brain regions, we (1) implemented an automated method for segmenting resections with manual review, (2) applied cost function masking to the resection zone, and (3) estimated longitudinal cortical thickness changes using Advanced Normalization Tools (ANTs). We then compared post-operative changes in cortical thickness between the two surgical groups in brain regions outside the resected area.ResultsPatients treated with ATL exhibited significantly greater cortical thinning globally when compared to patients treated with SAH (p = 0.049). There were significant focal differences between the two treatment groups in the ipsilateral frontal lobe (superior medial and medial orbital regions) and insula (p > 0.001, α = 0.05 Bonferroni corrected). No significant effects were seen in the contralateral hemisphere.SignificanceWe present and share a semi-automated pipeline for quantifying remote longitudinal changes in cortical thickness after neurosurgery. The technique is applicable to a broad array of applications, including surgical planning and mapping neuropsychological function to brain structure. Using this tool, we demonstrate that patients treated with SAH for refractory temporal lobe epilepsy have less postoperative cortical thinning in remote brain regions than those treated with ATL. We share all algorithm code and results to accelerate collaboration and clinical translation of our work.KEY POINTS BOXDifferent epilepsy surgical approaches lead to distinct patterns of postoperative cortical atrophy in remote brain regionsPatients treated with SAH have less postoperative cortical thinning than patients treated with ATLThe insula and frontal lobe demonstrated the greatest focal differences in postoperative cortical thinning when comparing SAH and ATLPostoperative cortical thinning analyses may inform surgical planning and our understanding of cognitive sequelae

Objective: New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification. Methods: IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (P bt O 2 ), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application. Results: Sustained increases in ICP and concordant decreases in P bt O 2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r 2 0.633-0.781; p

Network neuroscience applied to epilepsy holds promise to map pathological networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of the epileptic brain because of sparse placement of intracranial electrodes may affect model results. In this study, we evaluate the sensitivity of several published network measures to incomplete spatial sampling and propose an algorithm using network subsampling to determine confidence in model results. We retrospectively evaluated intracranial EEG data from 28 patients implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after randomly and systematically removing subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics. This sensitivity was largely independent of whether seizure onset zone contacts were targeted or spared from removal. We present an algorithm using random subsampling to compute patient-specific confidence intervals for network localizations. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy., Author Summary Network neuroscience applied to epileptic brains seeks to identify pathological neural connections that promote and maintain seizures, and holds promise to guide surgical planning in patients with intractable epilepsy. However, sampling of the epileptic network in intracranial EEG recording is limited by the choice of where to place intracranial electrodes, which is highly variable within and between epilepsy centers. The effect of incomplete spatial sampling generated by sparse electrode placement on network statistics is unknown. Here, we determine the sensitivity of several network statistics to incomplete spatial sampling, and we propose a method using electrode subsampling to determine patient-specific confidence intervals in network model predictions.

Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual’s estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

The purpose of this study is to demonstrate a method for virtually evaluating novel imaging devices using machine learning and open-access datasets, here applied to a new, ultra-low-field strength (ULF), 64mT, portable MRI device. Paired 3T and 64mT brain images were used to develop and validate a transformation converting standard clinical images to ULF-quality images. Separately, 3T images were aggregated from open-source databases spanning four neuropathologies: low-grade glioma (LGG, N=76), high-grade glioma (HGG, N=259), stroke (N=28), and multiple sclerosis (MS, N=20). The transformation method was then applied to the open-source data to generate simulated ULF images for each pathology. Convolutional neural networks (DenseNet-121) were trained to detect pathology in axial slices from either 3T or simulated 64 mT images, and their relative performance was compared to characterize the potential diagnostic capabilities of ULF imaging. Algorithm performance was measured using area under the receiver operating characteristic curve. Across all cohorts, pathology detection was similar between 3T and simulated 64mT images (LGG: 0.97 vs. 0.98; HGG: 0.96 vs. 0.95; stroke: 0.94 vs. 0.94; MS: 0.90 vs 0.87). Pathology detection was further characterized as a function of lesion size, intensity, and contrast. Simulated images showed decreasing sensitivity for lesions smaller than 4 cm2(∼2.25 cm in diameter). While simulations cannot replace prospective trials during the evaluation of medical devices, they can provide guidance and justification for prospective studies. Simulated data derived from open-source imaging databases may facilitate testing and validation of new imaging devices.HighlightsUltra-low-field, point-of-care MRI has potential to detect a range of pathologies including brain tumors, strokes, and multiple sclerosis. However, determining the diagnostic capabilities and appropriate use case for such devices requires further prospective studies.Open-source image datasets provide a powerful tool for accelerating imaging research and enable simulated trials that can guide prospective clinical trials or device development.

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure-function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

ObjectiveTo determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.MethodsWe performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.ResultsThe best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1–3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4–6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.ConclusionsQEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.

Despite the success of responsive neurostimulation (RNS) for epilepsy, clinical outcomes vary significantly and are hard to predict. The ability to forecast clinical response to RNS therapy before device implantation would improve patient selection for RNS surgery and could prevent a costly and ineffective intervention. Determining and validating biomarkers predictive of RNS response is difficult, however, due to the heterogeneity of the RNS patient population and clinical procedures; large, multi-center datasets are needed to quantify patient variability and to account for stereotypy in the treatment paradigm of any one center. Here we use a distributed, cloud-based pipeline to analyze a federated dataset of intracranial EEG recordings, collected prior to RNS surgery, from a retrospective cohort of 30 patients across three major epilepsy centers. Based on recent work modelling the controllability of distributed brain networks, we hypothesize that broader brain network connectivity, beyond the seizure onset zone, can predict RNS response. We demonstrate how intracranial EEG recordings can be leveraged through network analysis to uncover biomarkers that predict response to RNS therapy. Our findings suggest that peri-ictal changes in synchronizability, a global network metric shown to accurately predict outcome from resective epilepsy surgery, can distinguish between good and poor RNS responders under the current RNS therapy guidelines (area under the receiver operating characteristic curve of 0.75). Furthermore, this study also provides a proof-of-concept roadmap for multicenter collaboration where practical considerations impede sharing datasets fully across centers.

Objective Patients with poorly controlled seizures are at elevated risk of epilepsy-related morbidity and mortality. For patients with drug-resistant epilepsy that is focal at onset, epilepsy surgery is the most effective treatment available and offers a 50–80% cure rate. Yet, it is estimated that only 1% of patients with drug-resistant epilepsy undergo surgery in a timely fashion, and delays to surgery completion are considerable. The aim of this study was to increase availability and decrease delay of surgical evaluation at our epilepsy center for patients with drug-resistant epilepsy by removing process barriers. Methods For this quality improvement (QI) initiative, we convened a multidisciplinary team to construct a presurgical pathway process map and complete root cause analysis. This inquiry revealed that the current condition allowed patients to proceed through the pathway without centralized oversight. Therefore, we appointed an epilepsy surgery nurse manager, and under her direction, multiple additional process improvement interventions were applied. We then retrospectively compared preintervention (2014–2015) and postintervention (2016–2017) cohorts of patient undergoing the presurgical pathway. The improvement measures were patient throughput and pathway sojourn times. As a balancing measure, we considered the proportion of potentially eligible patients (epilepsy monitoring unit (EMU) admissions) who ultimately completed epilepsy surgery. Results Following our intervention, patient throughput was substantially increased for each stage of the presurgical pathway (32%–96% growth). However, patient sojourn times were not improved overall. No difference was observed in the proportion of possible candidates who ultimately completed epilepsy surgery. Significance Although process improvement expanded the number of patients who underwent epilepsy surgical evaluation, we experienced concurrent prolongation of the time from pathway initiation to completion. Ongoing improvement cycles will focus on newly identified residual sources of bottleneck and delay.

Objective Recent research suggests that high frequency intracranial EEG (iEEG) may improve localization of epileptic networks. This study aims to determine whether recording macroelectrode iEEG with higher sampling rates improves seizure localization in clinical practice. Methods 14 iEEG seizures from 10 patients recorded with >2000 Hz sampling rate were downsampled to four sampling rates: 100, 200, 500, 1000 Hz. In the 56 seizures, seizure onset time and location was marked by 5 independent, blinded EEG experts. Results When reading iEEG under clinical conditions, there was no consistent difference in time or localization of seizure onset or number of electrodes involved in the seizure onset zone with sampling rates varying from 100 to 1000 Hz. Stratification of patients by outcome did not improve with higher sampling rate. Conclusion When utilizing standard clinical protocols, there was no benefit to acquiring iEEGs with sampling rate >100 Hz. Significant variability was noted in EEG marking both within and between individual expert EEG readers. Significance Although commercial equipment is capable of sampling much faster than 100 Hz, tools allowing visualization of subtle high frequency activity such as HFOs will be required to improve patient care. Quantitative methods may decrease reader variability, and potentially improve patient outcomes.

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson’s disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months., Brain Implants integrated with Local and Distributed Computing Devices provide a seamless interface between patients and physicians, and real-time intracranial EEG can be used to classify brain state (wake/sleep, pre-seizure, seizure), implement control policies for electrical stimulation, and track patient health. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

Supplemental Digital Content is available in the text., BACKGROUND: Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning–based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review. METHODS: We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning–based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients. RESULTS We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning–assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.

Summary Electroencephalography (EEG)—the direct recording of the electrical activity of populations of neurons—is a tremendously important tool for diagnosing, treating, and researching epilepsy. Although standard procedures for recording and analyzing human EEG exist and are broadly accepted, there are no such standards for research in animal models of seizures and epilepsy—recording montages, acquisition systems, and processing algorithms may differ substantially among investigators and laboratories. The lack of standard procedures for acquiring and analyzing EEG from animal models of epilepsy hinders the interpretation of experimental results and reduces the ability of the scientific community to efficiently translate new experimental findings into clinical practice. Accordingly, the intention of this report is twofold: (1) to review current techniques for the collection and software-based analysis of neural field recordings in animal models of epilepsy, and (2) to offer pertinent standards and reporting guidelines for this research. Specifically, we review current techniques for signal acquisition, signal conditioning, signal processing, data storage, and data sharing, and include applicable recommendations to standardize collection and reporting. We close with a discussion of challenges and future opportunities, and include a supplemental report of currently available acquisition systems and analysis tools. This work represents a collaboration on behalf of the American Epilepsy Society/International League Against Epilepsy (AES/ILAE) Translational Task Force (TASK1-Workgroup 5), and is part of a larger effort to harmonize video-EEG interpretation and analysis methods across studies using in vivo and in vitro seizure and epilepsy models.

Status epilepticus is an emergency; however, prompt treatment of patients with status epilepticus is challenging. Clinical trials, such as the ESETT (Established Status Epilepticus Treatment Trial), compare effectiveness of antiepileptic medications, and rigorous examination of effectiveness of care delivery is similarly warranted. We reviewed the medical literature on observed deviations from guidelines, clinical significance, and initiatives to improve timely treatment. We found pervasive, substantial gaps between recommended and "real-world" practice with regard to timing, dosing, and sequence of antiepileptic therapy. Applying quality improvement methodology at the institutional level can increase adherence to guidelines and may improve patient outcomes. Ann Neurol 2017;82:155-165.

See Kleen and Kirsch (doi:10.1093/awx178) for a scientific commentary on this article.Cognitive deficits are common among epilepsy patients. In these patients, interictal epileptiform discharges, also termed spikes, are seen routinely on electroencephalography and believed to be associated with transient cognitive impairments. In this study, we investigated the effect of spikes on memory encoding and retrieval, taking into account the spatial distribution of spikes in relation to the seizure onset zone as well as anatomical regions of the brain. Sixty-seven patients with medication refractory epilepsy undergoing continuous intracranial electroencephalography monitoring engaged in a delayed free recall task to test short-term memory. In this task, subjects were asked to memorize and recall lists of common nouns. We quantified the effect of each spike on the probability of successful recall using a generalized logistic mixed model. We found that in patients with left lateralized seizure onset zones, spikes outside the seizure onset zone impacted memory encoding, whereas those within the seizure onset zone did not. In addition, spikes in the left inferior temporal gyrus, middle temporal gyrus, superior temporal gyrus, and fusiform gyrus during memory encoding reduced odds of recall by as much as 15% per spike. Spikes also reduced the odds of word retrieval, an effect that was stronger with spikes outside of the seizure onset zone. These results suggest that seizure onset regions are dysfunctional at baseline, and support the idea that interictal spikes disrupt cognitive processes related to the underlying tissue.

Background:This study investigated the quality of care delivered by nurse practitioner (NP)–physician teams employed to expand clinic appointment availability for patients with epilepsy.Methods:We performed a retrospective observational cohort study of patients with epilepsy presenting to the Penn Epilepsy Center for a new patient appointment in 2014. During this time, patients were seen either by an NP–physician team care model or a more traditional physician-only care model. These care models were compared with regard to adherence to the 2014 American Academy of Neurology epilepsy quality measures at the initial visit. Clinical outcomes of seizure frequency, presentations to the Emergency Department, injury, and death were assessed over the subsequent year.Results:A total of 169 patients were identified by our inclusion and exclusion criteria: 65 patients in the NP–physician team care model cohort and 104 patients in the physician-only care model cohort. The NP–physician team care model saw, on average, 3 more patients per clinic session. There were no meaningful differences between these cohorts in baseline characteristics. The NP–physician team care model showed equivalent adherence to the physician-only care model for the epilepsy quality measures, with superior adherence to the counseling measures of querying for side effects, provision of personalized epilepsy safety education, and screening for behavioral health disorders. The 2 care models performed similarly in all clinical outcomes.Conclusions:An NP–physician team care model employed to increase availability of care could also improve quality of care delivered.

Focal epilepsy is a clinical condition arising from disordered brain networks. Network models hold promise to map these networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of epileptic brain due to sparse placement of intracranial electrodes may profoundly affect model results. In this study, we evaluate the robustness of several published network measures applied to intracranial electrode recordings and propose an algorithm, using network resampling, to determine confidence in model results. We retrospectively subsampled intracranial EEG data from 28 patients who were implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after both randomly and systematically resampling subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics, and that this sensitivity was independent of the distance of removed contacts from the seizure onset zone. We present an algorithm, using random resampling, to compute patient-specific confidence intervals for network localizations on both global and nodal network statistics. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward assessing the accuracy of intracranial electrode implants and translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.

Background Burst suppression occurs in the EEG during coma and under general anaesthesia. It has been assumed that burst suppression represents a deeper state of anaesthesia from which it is more difficult to recover. This has not been directly demonstrated, however. Here, we test this hypothesis directly by assessing relationships between EEG suppression in human volunteers and recovery of consciousness. Methods We recorded the EEG of 27 healthy humans (nine women/18 men) anaesthetised with isoflurane 1.3 minimum alveolar concentration (MAC) for 3 h. Periods of EEG suppression and non-suppression were separated using principal component analysis of the spectrogram. After emergence, participants completed the digit symbol substitution test and the psychomotor vigilance test. Results Volunteers demonstrated marked variability in multiple features of the suppressed EEG. In order to test the hypothesis that, for an individual subject, inclusion of features of suppression would improve accuracy of a model built to predict time of emergence, two types of models were constructed: one with a suppression-related feature included and one without. Contrary to our hypothesis, Akaike information criterion demonstrated that the addition of a suppression-related feature did not improve the ability of the model to predict time to emergence. Furthermore, the amounts of EEG suppression and decrements in cognitive task performance relative to pre-anaesthesia baseline were not significantly correlated. Conclusions These findings suggest that, in contrast to current assumptions, EEG suppression in and of itself is not an important determinant of recovery time or the degree of cognitive impairment upon emergence from anaesthesia in healthy adults.

Objective: Closed-loop implantable neural stimulators are an exciting treatment option for patients with medically refractory epilepsy, with a number of new devices in or nearing clinical trials. These devices must accurately detect a variety of seizure types in order to reliably deliver therapeutic stimulation. While effective, broadly-applicable seizure detection algorithms have recently been published, these methods are too computationally intensive to be directly deployed in an implantable device. We demonstrate a strategy that couples devices to cloud computing resources in order to implement complex seizure detection methods on an implantable device platform. Approach: We use a sensitive gating algorithm capable of running on-board a device to identify potential seizure epochs and transmit these epochs to a cloud-based analysis platform. A precise seizure detection algorithm is then applied to the candidate epochs, leveraging cloud computing resources for accurate seizure event detection. This seizure detection strategy was developed and tested on eleven human implanted device recordings generated using the NeuroVista Seizure Advisory System. Main Results: The gating algorithm achieved high-sensitivity detection using a small feature set as input to a linear classifier, compatible with the computational capability of next-generation implantable devices. The cloud-based precision algorithm successfully identified all seizures transmitted by the gating algorithm while significantly reducing the false positive rate. Across all subjects, this joint approach detected 99% of seizures with a false positive rate of 0.03/hr. Significance: We present a novel framework for implementing computationally intensive algorithms on human data recorded from an implanted device. By using telemetry to intelligently access cloud-based computational resources, the next generation of neuro-implantable devices will leverage sophisticated algorithms with potential to greatly improve device performance and patient outcomes.

ObjectiveTo characterize continuous EEG (cEEG) use patterns in the critically ill and to determine the association with hospitalization outcomes for specific diagnoses.MethodsWe performed a retrospective cross-sectional study with National Inpatient Sample data from 2004 to 2013. We sampled hospitalized adult patients who received intensive care and then compared patients who underwent cEEG to those who did not. We considered diagnostic subgroups of seizure/status epilepticus, subarachnoid or intracerebral hemorrhage, and altered consciousness. Outcomes were in-hospital mortality, hospitalization cost, and length of stay.ResultsIn total, 7,102,399 critically ill patients were identified, of whom 22,728 received cEEG. From 2004 to 2013, the proportion of patients who received cEEG increased from 0.06% (95% confidence interval [CI] 0.03%–0.09%) to 0.80% (95% CI 0.62%–0.98%). While the cEEG cohort appeared more ill, cEEG use was associated with reduced in-hospital mortality after adjustment for patient and hospital characteristics (odds ratio [OR] 0.83, 95% CI 0.75–0.93, p < 0.001). This finding held for the diagnoses of subarachnoid or intracerebral hemorrhage and for altered consciousness but not for the seizure/status epilepticus subgroup. Cost and length of hospitalization were increased for the cEEG cohort (OR 1.17 and OR 1.11, respectively, p < 0.001).ConclusionsThere was a >10-fold increase in cEEG use from 2004 to 2013. However, this procedure may still be underused; cEEG was associated with lower in-hospital mortality but used for only 0.3% of the critically ill population. While administrative claims analysis supports the utility of cEEG for critically ill patients, our findings suggest variable benefit by diagnosis, and investigation with greater clinical detail is warranted.

A major challenge in systems neuroscience is to understand how the brain’s structural architecture gives rise to its complex functional dynamics. Here, we address this challenge by examining the inter-ictal activity of five patients with medically refractory epilepsy during ∼ 15 hours of multi-channel intracranial recording. By constructing a pairwise maximum entropy model (MEM) of the observed neural dynamics, we seek to uncover the fundamental relationship between functional activity and its underlying structural substrate. Despite only incorporating the pairwise correlations in the observed neural activity, we find that the pairwise MEM robustly fits large-scale patterns of inter-ictal power dynamics across a wide range of frequency bands, notably displaying time-invariance and cross-frequency similarity. Furthermore, across all frequency bands, we demonstrate that the pairwise MEM accurately identifies the structural white matter connections between brain regions, outperforming other common model-free measures of functional connectivity. Together, our findings show that a simple pairwise MEM, which is explicitly ignorant of higher-order correlations between three or more brain regions, not only captures complex spatiotemporal patterns of neural activity across the frequency spectrum, but also suggests that the network of structural connections in the human brain is a plausible scaffold capable of supporting observed wide-band neural dynamics.

The Cleveland Neural Engineering Workshop (NEW) is a biennial meeting started in 2011 as an “unconference” to bring together leaders in the neural engineering and related fields. Since the first iteration of the meeting, NEW has evolved from “just getting together” to a more important purpose of creating, reviewing, and promoting a uniform strategic roadmap for the field. The purpose of this short report, as well as the companion 2015 and 2017 reports, is to provide a historical record of this meeting and the evolution of the roadmap. These reports more importantly establish a baseline for the next meeting to be held in June, 2019. The second Neural Engineering Workshop (NEW) was held in June 2013. The two-day workshop was hosted by the Cleveland Advanced Platform for Technology National Veterans Affairs Center, the Functional Electrical Stimulation National Veterans Affairs Center, and the Case Western Reserve University in Cleveland, Ohio. Participants identified seven areas of future focus in the field of neural engineering: active communications with users, advocacy (regulatory), network building (clinical practice), case studies (clinical and technical), early industrial feedback, value chain resources, engagement, and advocacy (funding). This proceedings document summarizes the meeting outcome.

See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article. Seizures are thought to arise from an identifiable pre-ictal state. Brinkmann et al. report the results of an online, open-access seizure forecasting competition using intracranial EEG recordings from canines with naturally occurring epilepsy and human patients undergoing presurgical monitoring. The winning algorithms forecast seizures at rates significantly greater than chance., See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article. Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.

ObjectivePatients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure-generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment.MethodsWe analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. We identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We associate these metrics with post-surgical outcomes.ResultsGreater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model.ConclusionsThis study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

ObjectiveTo determine the 30-day readmission rate after seizure-related discharge in a nationally representative sample, as well as patient, clinical, and hospital characteristics associated with readmission.MethodsRetrospective cohort study of adults discharged alive from a nonelective hospitalization for epilepsy or seizure, sampled from the Healthcare Cost and Utilization Project's 2014 Nationwide Readmissions Database. Descriptive statistics and logistic regression models were built to quantify and characterize nonelective readmission within 30 days.ResultsA total of 139,800 admissions met inclusion criteria, of which 15,094 (10.8%) were readmitted within 30 days. Patient characteristics associated with readmission included comorbid disease burden (Elixhauser score 2: adjusted odds ratio [AOR] [95% confidence interval (CI)] 1.38 [1.21–1.57]; Elixhauser score 3: AOR 1.52 [1.34–1.73]; Elixhauser score >4: AOR 2.28 [2.01–2.58] as compared to 1) and participation in public insurance programs (Medicare: AOR 1.39 [1.26–1.54]; Medicaid: AOR 1.39 [1.26–1.54] as compared to private insurance). Adverse events (AOR 1.17 [1.05–1.30]) and prolonged length of stay, as well as nonroutine discharge (AOR 1.32 [1.23–1.42]), were also associated with increased adjusted odds of readmission. The most common primary reason for readmission was epilepsy or convulsion (17%).ConclusionsPatients hospitalized with seizure are frequently readmitted. While readmitted patients are more likely to have multiple medical comorbidities, our study demonstrated that inpatient adverse events were also significantly associated with readmission. The most common reason for readmission was seizure or epilepsy. Together, these 2 findings suggest that a proportion of readmissions are related to modifiable care process factors and may therefore be avoidable. Further study into understanding preventable drivers of readmission in this population presents an opportunity to improve patient outcomes and health.

New technologies to probe the nervous system are propelling innovation and discovery at blinding speed, but are our trainees prepared to maximize this power? The growing role of engineering in research, such as materials, computing, electronics, and devices, compels us to rethink neuroscience education. Core technology requirements, cross-disciplinary education, open-source resources, and experiential learning are new ways we can efficiently equip future leaders to make the next disruptive discoveries.

The optimal epilepsy management device requires: 1) automated seizure detection 2) accurate automated electronic seizure diaries 3) seizure forecasting 4) active brain probing to track brain state and 5) automated brain stimulation therapy adjustment that can respond to changes in seizure probability to prevent seizures. In this paper, we describe an epilepsy management and therapy platform using Medtronic's investigational Activa RC+S Research System. The investigational Activa RC+S provides chronic nervous system stimulation, sensing, and embedded analytics. Continuous iEEG telemetry from the RC+S device can be received by a handheld or tablet computer providing more advanced analytical capability. The combined platform provides a unique opportunity for exploring and managing epileptic neurophysiology.

The optimal epilepsy management device requires: automated seizure detection, accurate automated electronic seizure diaries, seizure forecasting, active brain probing to track brain state, and automated brain stimulation therapy adjustment that can respond in a control law approach to seizure probability to prevent seizures. In this session, we want to describe and demonstrate an epilepsy management and therapy platform using Medtronic's investigational Activa RC+S Research System implanted in living canines with epilepsy. The investigational Activa RC+S provides chronic nervous system sensing and analytics using embedded scientific instrumentation and continuous iEEG telemetry, which can be communicated to a handheld device, providing a unique opportunity for exploring and managing epileptic neural networks.

Objective Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient's recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.

This study investigated continuity of neurological care for patients discharged from the epilepsy monitoring unit (EMU) with a diagnosis of psychogenic nonepileptic spells (PNES). Because PNES are seizure-like episodes that cannot be explained by abnormal electrical brain activity, they are challenging for patients to understand and accept. Consequently, after diagnosis, patients commonly fail to start recommended psychotherapy and instead pursue redundant medical care. As consistent relationships with healthcare providers may help, we instituted standard follow-up for patients diagnosed with PNES.We performed a retrospective observational cohort study of adults diagnosed with PNES in our EMU. In November 2013, we began routine scheduling of postdischarge follow-up neurology appointments. We compared preintervention (November 2010-October 2013) and postintervention (November 2013-May 2016) cohorts with regard to clinic attendance, understanding the diagnosis, compliance with recommendations, and event frequency.We identified 55 patients in the preintervention cohort and 123 patients in the postintervention cohort. We successfully implemented the intended practice changes; more patients had follow-up scheduled by discharge (preintervention 2% vs. postintervention 36%, p0.001), time to follow-up decreased (46days vs. 29, p=0.001), and providers more consistently queried understanding of diagnosis (38% vs. 67%, p=0.03). Explicit planning for continued care did not produce the anticipated patient-provider relationships, as follow-up in clinic was low (38% vs. 37%). For patients who attended clinic, the intervention did not improve establishment of psychiatric care, compliance with medication recommendations, understanding of diagnosis, or event frequency. The odds of reduced event frequency were nonsignificantly increased with understanding the diagnosis (OR 3.75, p=0.14). Recommending antiepileptic drug (AED) discontinuation was associated with increased odds of event freedom (OR 6.91, p0.01).Scheduling follow-up for patients diagnosed with PNES did not facilitate ongoing patient-provider relationships due to poor clinic attendance. As follow-up is unreliable, the inpatient visit is a critical window of opportunity for intervention.

Objectives To provide a general control system framework for neuromodulation, its practical challenges, and historical underpinnings in cardiac rhythm devices, and to illustrate the potential of closed-loop techniques in neuromodulation with a case study using an adaptive neural stimulation system that integrates sensing, actuation, and state estimation for the treatment of chronic pain through spinal cord stimulation. Materials and Methods The current state of neuromodulation can be viewed in a classical dynamic control framework: the nervous system is the classical “plant,” the neural stimulator is the actuator, tools to collect clinical data are the sensors, and the physician's judgment is the state estimator and mechanism for closing the therapy feedback loop. This framework highlights the opportunities available to advance neuromodulation. Results Technology has the capability to address key factors limiting the performance of current systems: observability, the ability of the device to monitor the state of the nervous system from sensor-based measurements in real time; and controllability, the ability of the device to drive the nervous system to a desired physiological state using suitable algorithms and actuation. Conclusions Technological advances in neuromodulation using such a control framework have the potential to improve neurologic therapies. Future opportunities for extending the role of these systems are briefly discussed.

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool., Highlights • We analyze interictal iEEG recordings and neuroimaging from epilepsy patients • We find that high interictal strength selectivity is associated with better outcomes • This effect appears to be driven largely by connectivity within the resection zone • Interictal recordings can guide identification of seizure-generating networks

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.

Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care.

SummaryObjective Epilepsy is a chronic disorder, but seizure recordings are usually obtained in the acute setting. The chronic behavior of seizures and the interictal bursts that sometimes initiate them is unknown. We investigate the variability of these electrographic patterns over an extended period of time using chronic intracranial recordings in canine epilepsy. Methods Continuous, yearlong intracranial electroencephalography (iEEG) recordings from four dogs with naturally occurring epilepsy were analyzed for seizures and interictal bursts. Following automated detection and clinician verification of interictal bursts and seizures, temporal trends of seizures, burst count, and burst–burst similarities were determined. One dog developed status epilepticus, the recordings of which were also investigated. Results Multiple seizure types, determined by onset channels, were observed in each dog, with significant temporal variation between types. The first 14 days of invasive recording, analogous to the average duration of clinical invasive recordings in humans, did not capture the entirety of seizure types. Seizures typically occurred in clusters, and isolated seizures were rare. The count and dynamics of interictal bursts form distinct groups and do not stabilize until several weeks after implantation. Significance There is significant temporal variability in seizures and interictal bursts after electrode implantation that requires several weeks to reach steady state. These findings, comparable to those reported in humans implanted with the NeuroPace Responsive Neurostimulator System (RNS) device, suggest that transient network changes following electrode implantation may need to be taken into account when interpreting or analyzing iEEG during evaluation for epilepsy surgery. Chronic, ambulatory iEEG may be better suited to accurately map epileptic networks in appropriate individuals.

Here we present a unique, but important seizure variant directly related to placement of subdural grids. Two distinct epileptogenic zones were identified, one which correlated with the patient's baseline seizures and a separate zone associated with atypical semiology and localization. Inspection of this zone at surgery revealed cortical deformation from the grid itself. The patient underwent successful surgical resection of the primary epileptogenic zone, but not that of the atypical zone. She remains seizure free at two years following surgery. Recognition of grid-induced seizures is important as they may confound the interpretation of intracranial electroencephalograms (iEEG) and mislead resective surgery.

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (p

Localizing an epileptic network is essential for guiding neurosurgery and antiepileptic medical devices as well as elucidating mechanisms that may explain seizure-generation and epilepsy. There is increasing evidence that pathological oscillations may be specific to diseased networks in patients with epilepsy and that these oscillations may be a key biomarker for generating and indentifying epileptic networks. We present a semi-automated method that detects, maps, and mines pathological gamma (30–100 Hz) oscillations (PGOs) in human epileptic brain to possibly localize epileptic networks. We apply the method to standard clinical iEEG (

We present results from continuous intracranial electroencephalographic (iEEG) monitoring in 6 dogs with naturally occurring epilepsy, a disorder similar to the human condition in its clinical presentation, epidemiology, electrophysiology and response to therapy. Recordings were obtained using a novel implantable device wirelessly linked to an external, portable real-time processing unit. We demonstrate previously uncharacterized intracranial seizure onset patterns in these animals that are strikingly similar in appearance to human partial onset epilepsy. We propose: (1) canine epilepsy as an appropriate model for testing human antiepileptic devices and new approaches to epilepsy surgery, and (2) this new technology as a versatile platform for evaluating seizures and response to therapy in the natural, ambulatory setting.

OBJECTIVE: To classify patients by age based upon information extracted from their electro-cardiograms (ECGs). To develop and compare the performance of Bayesian classifiers. METHODS AND MATERIAL: We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA). RESULTS AND CONCLUSIONS: The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.

When neuroimaging reveals a brain lesion, drug-resistant epilepsy patients show better outcomes after resective surgery than do the one-third of drug-resistant epilepsy patients who have normal brain magnetic resonance imaging (MRI). We applied a glutamate imaging method, GluCEST (glutamate chemical exchange saturation transfer), to patients with nonlesional temporal lobe epilepsy based on conventional MRI. GluCEST correctly lateralized the temporal lobe seizure focus on visual and quantitative analyses in all patients. MR spectra, available for a subset of patients and controls, corroborated the GluCEST findings. Hippocampal volumes were not significantly different between hemispheres. GluCEST allowed high-resolution functional imaging of brain glutamate and has potential to identify the epileptic focus in patients previously deemed nonlesional. This method may lead to improved clinical outcomes for temporal lobe epilepsy as well as other localization-related epilepsies.

Technological advances are dramatically advancing translational research in Epilepsy. Neurophysiology, imaging and meta-data are now recorded digitally in most centers, enabling quantitative analysis. Basic and translational research opportunities to use these data are exploding, but academic and funding cultures a preventing this potential from being realized. Research on epileptogenic networks, anti-epileptic devices and biomarkers could progress rapidly, if collaborative efforts to digest this “big neuro data” could be organized. Higher temporal and spatial resolution data are driving the need for novel multi-dimensional visualization and analysis tools. Crowd-sourced science, the same that drives innovation in computer science, could easily be mobilized for these tasks, were it not for competition for funding, attribution and lack of standard data formats and platforms. As these efforts mature, there is a great opportunity to advance Epilepsy research through data sharing, and increase collaboration between within the international research community.

Background/Aims: Transorbital approaches traditionally have focused on skull base and cavernous sinus lesions medial to the globe. Lateral orbital approaches to the temporal lobe have not been widely explored despite several theoretical advantages compared to open craniotomy. Recently, we demonstrated the feasibility of the lateral transorbital technique in cadaveric specimens with endoscopic visualization. We describe our initial clinical experience with the endoscope-assisted lateral transorbital approach to lesions in the temporal lobe. Methods: Two patients with mesial temporal lobe pathology presenting with seizures underwent surgery. The use of a transpalpebral or Stallard-Wright eyebrow incision enabled access to the intraorbital compartment, and a lateral orbital wall ‘keyhole' opening permitted visualization of the anterior temporal pole. Results: This approach afforded adequate access to the surgical target and surrounding structures and was well tolerated by the patients. To the best of our knowledge, this report constitutes the first case series describing the endoscope-assisted lateral transorbital approach to the temporal lobe. We discuss the limits of exposure, the nuances of opening and closing, and comparisons to open craniotomy. Conclusion: Further prospective investigation of this approach is warranted for comparison to traditional approaches to the mesial temporal lobe.

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 2.4 s 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.