Future of seizure prediction and intervention: closing the loop

The bidirectional relationship between the cerebellum and seizure networks: a double-edged sword

Epilepsy is highly prevalent and notoriously pharmacoresistant. New therapeutic interventions are urgently needed, both for preventing the seizures themselves as well as negative outcomes and comorbidities associated with chronic epilepsy. While the cerebellum is not traditionally associated with epilepsy or seizures, research over the past decade has outlined the cerebellum as a brain region that is uniquely suited for both therapeutic needs. This review discusses our current understanding of the cerebellum as a key node within seizure networks, capable of both attenuating seizures in several animal models, and conversely, prone to altered structure and function in chronic epilepsy. Critical next steps are to advance therapeutic modulation of the cerebellum more towards translation, and to provide a more comprehensive characterization of how the cerebellum is impacted by chronic epilepsy, in order to subvert negative outcomes.

Optogenetic stimulation of the superior colliculus suppresses genetic absence seizures

While anti-seizure medications are effective for many patients, nearly one-third of individuals have seizures that are refractory to pharmacotherapy. Prior studies using evoked preclinical seizure models have shown that pharmacological activation or excitatory optogenetic stimulation of the deep and intermediate layers of the superior colliculus (DLSC) display multi-potent anti-seizure effects. Here we monitored and modulated DLSC activity to suppress spontaneous seizures in the WAG/Rij genetic model of absence epilepsy. Female and male WAG/Rij adult rats were employed as study subjects. For electrophysiology studies, we recorded single unit activity from microwire arrays placed within the DLSC. For optogenetic experiments, animals were injected with virus coding for channelrhodopsin-2 or a control vector, and we compared the efficacy of continuous neuromodulation to that of closed-loop neuromodulation paradigms. For each, we compared three stimulation frequencies on a within-subject basis (5, 20, 100 Hz). For closed-loop stimulation, we detected seizures in real time based on the EEG power within the characteristic frequency band of spike-and-wave discharges (SWDs). We quantified the number and duration of each SWD during each 2 h-observation period. Following completion of the experiment, virus expression and fibre-optic placement was confirmed. We found that single-unit activity within the DLSC decreased seconds prior to SWD onset and increased during and after seizures. Nearly 40% of neurons displayed suppression of firing in response to the start of SWDs. Continuous optogenetic stimulation of the DLSC (at each of the three frequencies) resulted in a significant reduction of SWDs in males and was without effect in females. In contrast, closed-loop neuromodulation was effective in both females and males at all three frequencies. These data demonstrate that activity within the DLSC is suppressed prior to SWD onset, increases at SWD onset, and that excitatory optogenetic stimulation of the DLSC exerts anti-seizure effects against absence seizures. The striking difference between open- and closed-loop neuromodulation approaches underscores the importance of the stimulation paradigm in determining therapeutic effects.

Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy

Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.

Computational modeling allows unsupervised classification of epileptic brain states across species

Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.

The cerebellum's understated role and influences in the epilepsies

Approximately 1 in 26 people will develop epilepsy in their lifetime, but current treatment options leave as many as half of all epilepsy patients with uncontrolled seizures. In addition to the burden of the seizures themselves, chronic epilepsy can be associated with cognitive deficits, structural changes, and devastating negative outcomes such as sudden unexpected death in epilepsy (SUDEP). Thus, major challenges in epilepsy research surround the need to both develop new therapeutic targets for intervention as well as shed light on the mechanisms by which chronic epilepsy can lead to comorbidities and negative outcomes. Despite not being traditionally associated with epilepsy or seizures, the cerebellum has emerged as not only a brain region that can serve as an important target for seizure control, but one that may also be profoundly impacted by chronic epilepsy. Here, we discuss targeting the cerebellum for potential therapeutic intervention and discuss pathway insights gained from recent optogenetic studies. We then review observations of cerebellar alterations during seizures and in chronic epilepsy, as well as the potential for the cerebellum to be a seizure focus. Cerebellar alterations in epilepsy may be critical to patient outcomes, highlighting the need for a more comprehensive understanding and appreciation of the cerebellum in the epilepsies.

Multielectrode Cortical Stimulation Selectively Induces Unidirectional Wave Propagation of Excitatory Neuronal Activity in Biophysical Neural Model

Cortical stimulation is emerging as an experimental tool in basic research and a promising therapy for a range of neuropsychiatric conditions. As multielectrode arrays enter clinical practice, the possibility of using spatiotemporal patterns of electrical stimulation to induce desired physiological patterns has become theoretically possible, but in practice can only be implemented by trial-and-error because of a lack of predictive models. Experimental evidence increasingly establishes traveling waves as fundamental to cortical information-processing, but we lack an understanding of how to control wave properties despite rapidly improving technologies. This study uses a hybrid biophysical-anatomical and neural-computational model to predict and understand how a simple pattern of cortical surface stimulation could induce directional traveling waves via asymmetric activation of inhibitory interneurons. We found that pyramidal cells and basket cells are highly activated by the anodal electrode and minimally activated by the cathodal electrodes, while Martinotti cells are moderately activated by both electrodes but exhibit a slight preference for cathodal stimulation. Network model simulations found that this asymmetrical activation results in a traveling wave in superficial excitatory cells that propagates unidirectionally away from the electrode array. Our study reveals how asymmetric electrical stimulation can easily facilitate traveling waves by relying on two distinct types of inhibitory interneuron activity to shape and sustain the spatiotemporal dynamics of endogenous local circuit mechanisms.SIGNIFICANCE STATEMENT Electrical brain stimulation is becoming increasingly useful to probe the workings of brain and to treat a variety of neuropsychiatric disorders. However, stimulation is currently performed in a trial-and-error fashion as there are no methods to predict how different electrode arrangements and stimulation paradigms will affect brain functioning. In this study, we demonstrate a hybrid modeling approach, which makes experimentally testable predictions that bridge the gap between the microscale effects of multielectrode stimulation and the resultant circuit dynamics at the mesoscale. Our results show how custom stimulation paradigms can induce predictable, persistent changes in brain activity, which has the potential to restore normal brain function and become a powerful therapy for neurological and psychiatric conditions.

LINCs Are Vulnerable to Epileptic Insult and Fail to Provide Seizure Control via On-Demand Activation

Temporal lobe epilepsy (TLE) is notoriously pharmacoresistant, and identifying novel therapeutic targets for controlling seizures is crucial. Long-range inhibitory neuronal nitric oxide synthase-expressing cells (LINCs), a population of hippocampal neurons, were recently identified as a unique source of widespread inhibition in CA1, able to elicit both GABAA-mediated and GABAB-mediated postsynaptic inhibition. We therefore hypothesized that LINCs could be an effective target for seizure control. LINCs were optogenetically activated for on-demand seizure intervention in the intrahippocampal kainate (KA) mouse model of chronic TLE. Unexpectedly, LINC activation at 1 month post-KA did not substantially reduce seizure duration in either male or female mice. We tested two different sets of stimulation parameters, both previously found to be effective with on-demand optogenetic approaches, but neither was successful. Quantification of LINCs following intervention revealed a substantial reduction of LINC numbers compared with saline-injected controls. We also observed a decreased number of LINCs when the site of initial insult (i.e., KA injection) was moved to the amygdala [basolateral amygdala (BLA)-KA], and correspondingly, no effect of light delivery on BLA-KA seizures. This indicates that LINCs may be a vulnerable population in TLE, regardless of the site of initial insult. To determine whether long-term circuitry changes could influence outcomes, we continued testing once a month for up to 6 months post-KA. However, at no time point did LINC activation provide meaningful seizure suppression. Altogether, our results suggest that LINCs are not a promising target for seizure inhibition in TLE.

Toward automated prediction of sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.

Online Seizure Prediction System: A Novel Probabilistic Approach for Efficient Prediction of Epileptic Seizure with iEEG Signal

Background:

1% of people around the world are suffering from epilepsy. It is, therefore crucial to propose an efficient automated seizure prediction tool implemented in a portable device that uses the electroencephalogram (EEG) signal to enhance epileptic patients’ life quality.

Methods:

In this study, we focused on time-domain features to achieve discriminative information at a low CPU cost extracted from the intracranial electroencephalogram (iEEG) signals of six patients. The probabilistic framework based on XGBoost classifier requires the mean and maximum probability of the non-seizure and the seizure occurrence period segments. Once all these parameters are set for each patient, the medical decision maker can send alarm based on well-defined thresholds.

Results:

While finding a unique model for all patients is really challenging, and our modelling results demonstrated that the proposed algorithm can be an efficient tool for reliable and clinically relevant seizure forecasting. Using iEEG signals, the proposed algorithm can forecast seizures, informing a patient about 75 minutes before a seizure would occur, a period large enough for patients to take practical actions to minimize the potential impacts of the seizure.

Conclusion:

We posit that the ability to distinguish interictal intracranial EEG from pre-ictal signals at some low computational cost may be the first step towards an implanted portable semi-automatic seizure suppression system in the near future. It is believed that our seizure prediction technique can conceivably be coupled with treatment techniques aimed at interrupting the process even prior to a seizure initiates to develop.

Using Constrained Square-Root Cubature Kalman Filter for Quantifying the Severity of Epileptic Activities in Mice

(1) Background: Quantification of severity of epileptic activities, especially during electrical stimulation, is an unmet need for seizure control and evaluation of therapeutic efficacy. In this study, a parameter ratio derived from constrained square-root cubature Kalman filter (CSCKF) was formulated to quantify the excitability of local neural network and compared with three commonly used indicators, namely, band power, Teager energy operator, and sample entropy, to objectively determine their effectiveness in quantifying the severity of epileptiform discharges in mice. (2) Methods: A set of one normal and four types of epileptic EEGs was generated by a mathematical model. EEG data of epileptiform discharges during two types of electrical stimulation were recorded in 20 mice. Then, EEG segments of 5 s in length before, during and after the real and sham stimulation were collected. Both simulated and experimental data were used to compare the consistency and differences among the performance indicators. (3) Results: For the experimental data, the results of the four indicators were inconsistent during both types of electrical stimulation, although there was a trend that seizure severity changed with the indicators. For the simulated data, when the simulated EEG segments were used, the results of all four indicators were similar; however, this trend did not match the trend of excitability of the model network. In the model output which retained the DC component, except for the CSCKF parameter ratio, the results of the other three indicators were almost identical to those using the simulated EEG. For CSCKF, the parameter ratio faithfully reflected the excitability of the neural network. (4) Conclusion: For common EEG, CSCKF did not outperform other commonly used performance indicators. However, for EEG with a preserved DC component, CSCKF had the potential to quantify the excitability of the neural network and the associated severity of epileptiform discharges.

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

BACKGROUND

Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.

METHODS

Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.

RESULTS

These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.

CONCLUSION

The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

The cerebellum and epilepsy

Epilepsy is the fourth most common neurological disorder, but current treatment options provide limited efficacy and carry the potential for problematic adverse effects. There is an immense need to develop new therapeutic interventions in epilepsy, and targeting areas outside the seizure focus for neuromodulation has shown therapeutic value. While not traditionally associated with epilepsy, anatomical, clinical, and electrophysiological studies suggest the cerebellum can play a role in seizure networks, and importantly, may be a potential therapeutic target for seizure control. However, previous interventions targeting the cerebellum in both preclinical and clinical studies have produced mixed effects on seizures. These inconsistent results may be due in part to the lack of specificity inherent with open-loop electrical stimulation interventions. More recent studies, using more targeted closed-loop optogenetic approaches, suggest the possibility of robust seizure inhibition via cerebellar modulation for a range of seizure types. Therefore, while the mechanisms of cerebellar inhibition of seizures have yet to be fully elucidated, the cerebellum should be thoroughly revisited as a potential target for therapeutic intervention in epilepsy. This article is part of the Special Issue "NEWroscience 2018.

Stimulating Solutions for Intractable Epilepsy

Implantable devices for controlling medically intractable seizures nondestructively are rapidly advancing. These offer reversible, potentially, restorative options beyond traditional, surgical procedures, which rely, largely on resection or ablation of selected brain sites. Several lines of, investigation aimed at improving efficacy of these devices are discussed, ranging from identifying novel subcortical, white matter, or cell-type specific targets to engineering advances for adaptive techniques based- on continuous, dynamic system analysis.

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.

Active probing to highlight approaching transitions to ictal states in coupled neural mass models

The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using two coupled populations of the well-studied Wendling neural mass model. Different model settings are evaluated, shifting parameters (excitability, slow inhibition, or inter-population coupling gains) from normal towards ictal states while probing stimuli are applied every 2 seconds to the input of either one or both populations. The correlation between the extracted features and the ictogenic parameter shifting indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were mostly restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems' resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions.

Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review

Biological neuronal cells communicate using neurochemistry and electrical signals. The same phenomena also allow us to probe and manipulate neuronal systems and communicate with them. Neuronal system malfunctions cause a multitude of symptoms and functional deficiencies that can be assessed and sometimes alleviated by electrical stimulation. Our working hypothesis is that real-time closed-loop full-duplex measurement and stimulation paradigms can provide more in-depth insight into neuronal networks and enhance our capability to control diseases of the nervous system. In this study, we review extracellular electrical stimulation methods used in in vivo, in vitro, and in silico neuroscience research and in the clinic (excluding methods mainly aimed at neuronal growth and other similar effects) and highlight the potential of closed-loop measurement and stimulation systems. A multitude of electrical stimulation and measurement-based methods are widely used in research and the clinic. Closed-loop methods have been proposed, and some are used in the clinic. However, closed-loop systems utilizing more complex measurement analysis and adaptive stimulation systems, such as artificial intelligence systems connected to biological neuronal systems, do not yet exist. Our review promotes the research and development of intelligent paradigms aimed at meaningful communications between neuronal and information and communications technology systems, "dialogical paradigms," which have the potential to take neuroscience and clinical methods to a new level.

Diverse Applications of Artificial Intelligence in Neuroradiology

Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."

Seizure Detection Algorithms in Critically Ill Children: A Comparative Evaluation

OBJECTIVES

To evaluate the performance of commercially available seizure detection algorithms in critically ill children.

DESIGN

Diagnostic accuracy comparison between commercially available seizure detection algorithms referenced to electroencephalography experts using quantitative electroencephalography trends.

SETTING

Multispecialty quaternary children's hospital in Canada.

SUBJECTS

Critically ill children undergoing electroencephalography monitoring.

INTERVENTIONS

Continuous raw electroencephalography recordings (n = 19) were analyzed by a neurophysiologist to identify seizures. Those recordings were then converted to quantitative electroencephalography displays (amplitude-integrated electroencephalography and color density spectral array) and evaluated by six independent electroencephalography experts to determine the sensitivity and specificity of the amplitude-integrated electroencephalography and color density spectral array displays for seizure identification in comparison to expert interpretation of raw electroencephalography data. Those evaluations were then compared with four commercial seizure detection algorithms: ICTA-S (Stellate Harmonie Version 7; Natus Medical, San Carlos, CA), NB (Stellate Harmonie Version 7; Natus Medical), Persyst 11 (Persyst Development, Prescott, AZ), and Persyst 13 (Persyst Development) to determine sensitivity and specificity in comparison to amplitude-integrated electroencephalography and color density spectral array.

MEASUREMENTS AND MAIN RESULTS

Of the 379 seizures identified on raw electroencephalography, ICTA-S detected 36.9%, NB detected 92.3%, Persyst 11 detected 75.9%, and Persyst 13 detected 74.4%, whereas electroencephalography experts identified 76.5% of seizures using color density spectral array and 73.7% using amplitude-integrated electroencephalography. Daily false-positive rates averaged across all recordings were 4.7 with ICTA-S, 126.3 with NB, 5.1 with Persyst 11, 15.5 with Persyst 13, 1.7 with color density spectral array, and 1.5 with amplitude-integrated electroencephalography. Both Persyst 11 and Persyst 13 had sensitivity comparable to that of electroencephalography experts using amplitude-integrated electroencephalography and color density spectral array. Although Persyst 13 displayed the highest sensitivity for seizure count and seizure burden detected, Persyst 11 exhibited the best trade-off between sensitivity and false-positive rate among all seizure detection algorithms.

CONCLUSIONS

Some commercially available seizure detection algorithms demonstrate performance for seizure detection that is comparable to that of electroencephalography experts using quantitative electroencephalography displays. These algorithms may have utility as early warning systems that prompt review of quantitative electroencephalography or raw electroencephalography tracings, potentially leading to more timely seizure identification in critically ill patients.

Seizure prediction and intervention

Epilepsy treatment is challenging due to a lack of essential diagnostic tools, including methods for reliable seizure detection in the ambulatory setting, to assess seizure risk over time and to monitor treatment efficacy. This lack of objective diagnostics constitutes a significant barrier to better treatments, raises methodological concerns about the antiseizure medication evaluation process and, to patients, is a main issue contributing to the disease burden. Recent years have seen rapid progress towards better diagnostics that meet these needs of epilepsy patients and clinicians. Availability of comprehensive data and the rise of more powerful computational analysis methods have driven progress in this area. Here, we provide an overview on data- and theory-driven approaches aimed at identifying methods to reliably detect and forecast seizures as well as to monitor brain excitability and treatment efficacy in epilepsy. We provide a particular account on neural criticality, the hypothesis that cortical networks may be poised in a critical state at the boundary between different types of dynamics, and discuss its role in informing diagnostics to track cortex excitability and seizure risk in recent experiments. With the further expansion of digitalization in medicine, tele-medicine and long-term, ambulatory monitoring, these computationally based methods may gain more relevance in epilepsy in the future. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Big data in status epilepticus

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".

Ultra Broad Band Neural Activity Portends Seizure Onset in a Rat Model of Epilepsy

Epilepsy affects over 70 million people worldwide and 30% of patients' seizures cannot be controlled with medications, motivating the development of alternative therapies such as electrical stimulation. Current stimulation strategies attempt to stop seizures after they start, but none aim to prevent seizures altogether. Preventing seizures requires knowing when the brain is entering a preictal state (i.e., approaching seizure onset). Here we show that such preictal activity can be detected by an informative neural signal that progressively and monotonically changes as the brain approaches a seizure event. Specifically, we use local field potentials (LFP) from a rat model of epilepsy to develop an innovative measure of signal novelty relative to nonseizure activity, that shows the presence of progressive neural dynamics in an ultra broad band (4 Hz - 5 kHz). The measure is extracted from functional connectivity features computed from the LFPs which are used as an input to a one-class Support Vector Machine (SVM). The SVM outputs a scalar signal which quantifies how novel the current activity looks relative to baseline (non-seizure) activity and shows a progression towards seizure onset minutes ahead of time. The use of ultra broad band multivariate features into the SVM results in a novelty signal that has a significantly higher slope in the progression to seizure onset when compared to using power in conventional frequency bands (4 - 500 Hz) on individual channels as input features to the SVM. Functional connectivity in conjunction with the SVM is a strategy that generates a new measurement of novelty that can be used by closed-loop systems for seizure forecasting and prevention.

Selective recruitment of cortical neurons by electrical stimulation

Despite its critical importance in experimental and clinical neuroscience, at present there is no systematic method to predict which neural elements will be activated by a given stimulation regime. Here we develop a novel approach to model the effect of cortical stimulation on spiking probability of neurons in a volume of tissue, by applying an analytical estimate of stimulation-induced activation of different cell types across cortical layers. We utilize the morphology and properties of axonal arborization profiles obtained from publicly available anatomical reconstructions of the twelve main categories of neocortical neurons to derive the dependence of activation probability on cell type, layer and distance from the source. We then propagate this activity through the local network incorporating connectivity, synaptic and cellular properties. Our work predicts that (a) intracranial cortical stimulation induces selective activation across cell types and layers; (b) superficial anodal stimulation is more effective than cathodal at cell activation; (c) cortical surface stimulation focally activates layer I axons, and (d) there is an optimal stimulation intensity capable of eliciting cell activation lasting beyond the end of stimulation. We conclude that selective effects of cortical electrical stimulation across cell types and cortical layers are largely driven by their different axonal arborization and myelination profiles.

Brain stimulation is widely used to probe the neural system to learn about its properties, to normalize dysfunction (e.g., deep brain stimulation for Parkinsonian patients), or to manipulate brain activity, including enhancing memory and learning. Despite its critical importance in experimental and clinical neuroscience, at present there are no systematic methods to predict which neural elements of the brain will be activated by a given stimulation regime. To address this question, we propose a novel theoretical framework that models the effect of cortical stimulation on the spiking probability of a neuron based on its location, type and morphology. Our study predicts that short-lived superficial electrical stimulation has the ability to trigger spiking in layer IV pyramidal cells, and to evoke network activity that could persist for hundreds of milliseconds. It further predicts a much higher spiking response to anodal stimulation compared to cathodal one, as the existence of an optimal stimulation intensity, capable of inducing a maximal response in a population of cortical cells. The results of our study can be directly taken into account in planning future electrical stimulation experiments.

The path from scientific discovery to cures for epilepsy

The epilepsies are a complex group of disorders that can be caused by a myriad of genetic and acquired factors. As such, identifying interventions that will prevent development of epilepsy, as well as cure the disorder once established, will require a multifaceted approach. Here we discuss the progress in scientific discovery propelling us towards this goal, including identification of genetic risk factors and big data approaches that integrate clinical and molecular 'omics' datasets to identify common pathophysiological signatures and biomarkers. We discuss the many animal and cellular models of epilepsy, what they have taught us about pathophysiology, and the cutting edge cellular, optogenetic, chemogenetic and anti-seizure drug screening approaches that are being used to find new cures in these models. Finally, we reflect on the work that still needs to be done towards identify at-risk individuals early, targeting and stopping epileptogenesis, and optimizing promising treatment approaches. Ultimately, developing and implementing cures for epilepsy will require a coordinated and immense effort from clinicians and basic scientists, as well as industry, and should always be guided by the needs of individuals affected by epilepsy and their families. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Can absence seizures be predicted by vigilance states?: Advanced analysis of sleep-wake states and spike-wave discharges' occurrence in rats

Spike-wave discharges (SWDs) are the main manifestation of absence epilepsy. Their occurrence is dependent on the behavioral state, and they preferentially occur during unstable vigilance periods. The present study investigated whether the occurrence of SWDs can be predicted by the preceding behavioral state and whether this relationship is different between the light and the dark phases of the 24-h day. Twenty-four-hour (12:12 light/dark phases) electroencephalographic (EEG) recordings of 12 Wistar Albino Glaxo, originally bred in Rijswijk (WAG/Rij) rats, a well-known genetic model of absence epilepsy, were analyzed and transformed into sequences of 2-s length intervals of the following 6 possible states: active wakefulness (AW), passive wakefulness (PW), deep slow-wave sleep (DSWS), light slow-wave sleep (LSWS), rapid eye movement (REM) sleep, and SWDs, given discrete series of categorical data. Probabilities of all transitions between states and Shannon entropy of transitions were calculated for the light and dark phases separately and statistically analyzed. Common differences between the light and the dark phases were found regarding the time spent in AW, LSWS, DSWS, and SWDs. The most probable transitions were that AW was preceded and followed by PW and vice versa regardless of the phase of the photoperiod. A similar relationship was found for light and deep slow-wave sleep. The most probable transitions to and from SWDs were AW and LSWS, respectively, with these transition likelihoods being consistent across both circadian phases. The second most probable transitions around SWDs appeared more variable between light and dark. During the light phase, SWDs occurred around PW and participated exclusively in sleep initiation; in the dark phase, SWDs were seen on both, ascending and descending steps towards and from sleep. Conditional Shannon entropy showed that AW and DSWS are the most predictable events, while the possible prediction horizon of SWDs is not larger than 4 s and despite the higher occurrence of SWDs in the dark phase, did not differ between phases. It can be concluded that although SWDs show a stable, strong circadian rhythm with a peak in number during the dark phase, their occurrence cannot be reliably predicted by the preceding behavioral state, except at a very short time base.

Outgrowing seizures in Childhood Absence Epilepsy: time delays and bistability

We formulate a conductance-based model for a 3-neuron motif associated with Childhood Absence Epilepsy (CAE). The motif consists of neurons from the thalamic relay (TC) and reticular nuclei (RT) and the cortex (CT). We focus on a genetic defect common to the mouse homolog of CAE which is associated with loss of GABA_A receptors on the TC neuron, and the fact that myelination of axons as children age can increase the conduction velocity between neurons. We show the combination of low GABA_A mediated inhibition of TC neurons and the long corticothalamic loop delay gives rise to a variety of complex dynamics in the motif, including bistability. This bistability disappears as the corticothalamic conduction delay shortens even though GABA_A activity remains impaired. Thus the combination of deficient GABA_A activity and changing axonal myelination in the corticothalamic loop may be sufficient to account for the clinical course of CAE.

Dog alerting and/or responding to epileptic seizures: A scoping review

Recently, there has been a rising interest in service dogs for people with epilepsy. Dogs have been reported as being sensitive to epileptic episodes in their owners, alerting before and/or responding during or after a seizure, with or without specific training. The purpose of this review is to present a comprehensive overview of the scientific research on seizure-alert/response dogs for people with epilepsy. We aimed to identify the existing scientific literature on the topic, describe the characteristics of seizure-alert/response dogs, and evaluate the state of the evidence base and outcomes. Out of 28 studies published in peer-reviewed journals dealing with this topic, only 5 (one prospective study and four self-reported questionnaires) qualified for inclusion according to PRISMA guidelines. Reported times of alert before seizure varied widely among dogs (with a range from 10 seconds to 5 hours) but seemed to be reliable (accuracy from ≥70% to 85% according to owner reports). Alerting behaviors were generally described as attention-getting. The alert applied to many seizure types. Dogs mentioned as being seizure-alert dogs varied in size and breed. Training methods differed between service animal programs, partially relying on hypothesized cues used by dogs (e.g., variations in behavior, scent, heart rate). Most studies indicated an increase in quality of life and a reduction in the seizure frequency when living with a dog demonstrating seizure-related behavior. However, the level of methodological rigor was generally poor. In conclusion, scientific data are still too scarce and preliminary to reach any definitive conclusion regarding the success of dogs in alerting for an impending seizure, the cues on which this ability may be based, the best type of dog, and associated training. While these preliminary data suggest that this is a promising topic, further research is needed.

Biomimetic extracellular matrix coatings improve the chronic biocompatibility of microfabricated subdural microelectrode arrays

Intracranial electrodes are a vital component of implantable neurodevices, both for acute diagnostics and chronic treatment with open and closed-loop neuromodulation. Their performance is hampered by acute implantation trauma and chronic inflammation in response to implanted materials and mechanical mismatch between stiff synthetic electrodes and pulsating, natural soft host neural tissue. Flexible electronics based on thin polymer films patterned with microscale conductive features can help alleviate the mechanically induced trauma; however, this strategy alone does not mitigate inflammation at the device-tissue interface. In this study, we propose a biomimetic approach that integrates microscale extracellular matrix (ECM) coatings on microfabricated flexible subdural microelectrodes. Taking advantage of a high-throughput process employing micro-transfer molding and excimer laser micromachining, we fabricate multi-channel subdural microelectrodes primarily composed of ECM protein material and demonstrate that the electrochemical and mechanical properties match those of standard, uncoated controls. In vivo ECoG recordings in rodent brain confirm that the ECM microelectrode coatings and the protein interface do not alter signal fidelity. Astrogliotic, foreign body reaction to ECM coated devices is reduced, compared to uncoated controls, at 7 and 30 days, after subdural implantation in rat somatosensory cortex. We propose microfabricated, flexible, biomimetic electrodes as a new strategy to reduce inflammation at the device-tissue interface and improve the long-term stability of implantable subdural electrodes.

Gauging seizure risk

The current paradigm for treatment of epilepsy begins with trials of antiepileptic drugs, followed by evaluation for resective brain surgery in drug-resistant patients. If surgery is not possible or fails to control seizures, some patients benefit from implanted neurostimulation devices. In addition to their therapeutic benefit, some of these devices have diagnostic capability enabling recordings of brain activity with unprecedented chronicity. Two recent studies using different devices for chronic EEG (i.e., over months to years) yielded convergent findings of daily and multiday cycles of brain activity that help explain seizure timing. Knowledge of these patient-specific cycles can be leveraged to gauge and forecast seizure risk, empowering patients to adopt risk-stratified treatment strategies and behavioral modifications. We review evidence that epilepsy is a cyclical disorder, and we argue that implanted monitoring devices should be offered earlier in the treatment paradigm. Chronic EEG would allow pharmacologic treatments tailored to days of high seizure risk-here termed chronotherapy-and would help characterize long timescale seizure dynamics to improve subsequent surgical planning. Coupled with neuromodulation, the proposed approach could improve quality of life for patients and decrease the number ultimately requiring resective surgery. We outline challenges for chronic monitoring and seizure forecasting that demand close collaboration among engineers, neurosurgeons, and neurologists.

Circadian rhythm and epilepsy

Advances in diagnostic technology, including chronic intracranial EEG recordings, have confirmed the clinical observation of different temporal patterns of epileptic activity and seizure occurrence over a 24-h period. The rhythmic patterns in epileptic activity and seizure occurrence are probably related to vigilance states and circadian variation in excitatory and inhibitory balance. Core circadian genes BMAL1 and CLOCK, which code for transcription factors, have been shown to influence excitability and seizure threshold. Despite uncertainties about the relative contribution of vigilance states versus circadian rhythmicity, including circadian factors such as seizure timing improves sensitivity of seizure prediction algorithms in individual patients. Improved prediction of seizure occurrence opens the possibility for personalised antiepileptic drug-dosing regimens timed to particular phases of the circadian cycle to improve seizure control and to reduce side-effects and risks associated with seizures. Further studies are needed to clarify the pathways through which rhythmic patterns of epileptic activity are generated, because this might also inform future treatment options.

Controlling Seizures with Technology: Researchers Are Working to Predict and Prevent Epileptic Seizures Before They Happen

Mike McKenna was tired of epilepsy controlling his life. For years, he tried different medications and therapies to no avail; his seizures, which occurred every three to six days, dictated what he could do and where he could live. Then, about ten years ago, he joined a clinical trial for a new, implantable medical device from a company called NeuroPace. The RNS System monitors brain activity, detects patterns that indicate an imminent seizure, and responds by sending brief electrical pulses to disrupt the abnormal brain activity, stopping seizures in their tracks.

Specificity, Versatility, and Continual Development: The Power of Optogenetics for Epilepsy Research

Optogenetics is a powerful and rapidly expanding set of techniques that use genetically encoded light sensitive proteins such as opsins. Through the selective expression of these exogenous light-sensitive proteins, researchers gain the ability to modulate neuronal activity, intracellular signaling pathways, or gene expression with spatial, directional, temporal, and cell-type specificity. Optogenetics provides a versatile toolbox and has significantly advanced a variety of neuroscience fields. In this review, using recent epilepsy research as a focal point, we highlight how the specificity, versatility, and continual development of new optogenetic related tools advances our understanding of neuronal circuits and neurological disorders. We additionally provide a brief overview of some currently available optogenetic tools including for the selective expression of opsins.

Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression

Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study, human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern, which successfully abated 92% of seizures. Finally, in a fully responsive, or closed-loop, neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.

Evaluation of dynamic scaling of growing interfaces in EEG fluctuations of seizures in animal model of temporal lobe epilepsy

Epileptic seizures, as a dynamic phenomenon in brain behavior, obey a scale-free behavior, frequently analyzed by electrical activity recording. This recording can be seen as a surface that roughens with time. Dynamic scaling studies roughening processes or growing interfaces. In this theory, a set of exponents -obtained from scale invariance properties- characterize rough interfaces growth. The aim of the present study was to investigate scaling behavior in EEG time series fluctuations of a chemical animal model of temporal lobe epilepsy, with dynamic scaling to detect changes on seizure onset. We analyzed local variables in different sampling intervals and estimated rough, scaling and dynamic exponents. Results exhibited long-range correlations in interictal activity. Results of renormalization and data collapsing confirmed that each epoch of EEG fluctuations for interictal, preictal and postictal collapse in a curve in different scales, each segment independently; remarkably, we found non-scaling behavior in seizures epochs. Data for the different sampling intervals for ictal period do not collapse in one curve, which implies that ictal activity does not exhibit the same scaling behavior than the other epochs. Statistical significant differences of growth exponent were found between interictal and ictal segment, while for scaling exponent, significant differences were found between interictal and postictal segment. These results confirm the potential of scaling exponents as characteristic parameters to detect changes on seizure onset, which suggests their use as inputs for analysis methods for seizure detection in long-term recordings, while changes in growth exponent are potentially useful for prediction purposes.

Absence Seizure Control by a Brain Computer Interface

The ultimate goal of epileptology is the complete abolishment of epileptic seizures. This might be achieved by a system that predicts seizure onset combined with a system that interferes with the process that leads to the onset of a seizure. Seizure prediction remains, as of yet, unresolved in absence-epilepsy, due to the sudden onset of seizures. We have developed a real-time absence seizure prediction algorithm, evaluated it and implemented it in an on-line, closed-loop brain stimulation system designed to prevent the spike-wave-discharges (SWDs), typical for absence epilepsy, in a genetic rat model. The algorithm corretly predicted 88% of the SWDs while the remaining were quickly detected. A high number of false-positive detections occurred mainly during light slow-wave-sleep. Inclusion of criteria to prevent false-positives greatly reduced the false alarm rate but decreased the sensitivity of the algoritm. Implementation of the latter version into a closed-loop brain-stimulation-system resulted in a 72% decrease in seizure activity. In contrast to long standing beliefs that SWDs are unpredictable, these results demonstrate that they can be predicted and that the development of closed-loop seizure prediction and prevention systems is a feasable step towards interventions to attain control and freedom from epileptic seizures.

A Consistent Definition of Phase Resetting Using Hilbert Transform

A phase resetting curve (PRC) measures the transient change in the phase of a neural oscillator subject to an external perturbation. The PRC encapsulates the dynamical response of a neural oscillator and, as a result, it is often used for predicting phase-locked modes in neural networks. While phase is a fundamental concept, it has multiple definitions that may lead to contradictory results. We used the Hilbert Transform (HT) to define the phase of the membrane potential oscillations and HT amplitude to estimate the PRC of a single neural oscillator. We found that HT's amplitude and its corresponding instantaneous frequency are very sensitive to membrane potential perturbations. We also found that the phase shift of HT amplitude between the pre- and poststimulus cycles gives an accurate estimate of the PRC. Moreover, HT phase does not suffer from the shortcomings of voltage threshold or isochrone methods and, as a result, gives accurate and reliable estimations of phase resetting.

Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System

Conventional transcranial direct current stimulation (tDCS) protocols rely on applying electrical current at a fixed intensity and duration without using surrogate markers to direct the interventions. This has led to some mixed results; especially because tDCS induced effects may vary depending on the ongoing level of brain activity. Therefore, the objective of this preliminary study was to assess the feasibility of an EEG-triggered tDCS system based on EEG online analysis of its frequency bands. Six healthy volunteers were randomized to participate in a double-blind sham-controlled crossover design to receive a single session of 10[Formula: see text]min 2[Formula: see text]mA cathodal and sham tDCS. tDCS trigger controller was based upon an algorithm designed to detect an increase in the relative beta power of more than 200%, accompanied by a decrease of 50% or more in the relative alpha power, based on baseline EEG recordings. EEG-tDCS closed-loop-system was able to detect the predefined EEG magnitude deviation and successfully triggered the stimulation in all participants. This preliminary study represents a proof-of-concept for the development of an EEG-tDCS closed-loop system in humans. We discuss and review here different methods of closed loop system that can be considered and potential clinical applications of such system.

The Effects of Closed-Loop Medical Devices on the Autonomy and Accountability of Persons and Systems

Closed-loop medical devices such as brain-computer interfaces are an emerging and rapidly advancing neurotechnology. The target patients for brain-computer interfaces (BCIs) are often severely paralyzed, and thus particularly vulnerable in terms of personal autonomy, decisionmaking capacity, and agency. Here we analyze the effects of closed-loop medical devices on the autonomy and accountability of both persons (as patients or research participants) and neurotechnological closed-loop medical systems. We show that although BCIs can strengthen patient autonomy by preserving or restoring communicative abilities and/or motor control, closed-loop devices may also create challenges for moral and legal accountability. We advocate the development of a comprehensive ethical and legal framework to address the challenges of emerging closed-loop neurotechnologies like BCIs and stress the centrality of informed consent and refusal as a means to foster accountability. We propose the creation of an international neuroethics task force with members from medical neuroscience, neuroengineering, computer science, medical law, and medical ethics, as well as representatives of patient advocacy groups and the public.

Predictability of uncontrollable multifocal seizures - towards new treatment options

Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

Analyzing 7000 texts on deep brain stimulation: what do they tell us?

The enormous increase in numbers of scientific publications in the last decades requires quantitative methods for obtaining a better understanding of topics and developments in various fields. In this exploratory study, we investigate the emergence, trends, and connections of topics within the whole text corpus of the deep brain stimulation (DBS) literature based on more than 7000 papers (title and abstracts) published between 1991 to 2014 using a network approach. Taking the co-occurrence of basic terms that represent important topics within DBS as starting point, we outline the statistics of interconnections between DBS indications, anatomical targets, positive, and negative effects, as well as methodological, technological, and economic issues. This quantitative approach confirms known trends within the literature (e.g., regarding the emergence of psychiatric indications). The data also reflect an increased discussion about complex issues such as personality connected tightly to the ethical context, as well as an apparent focus on depression as important DBS indication, where the co-occurrence of terms related to negative effects is low both for the indication as well as the related anatomical targets. We also discuss consequences of the analysis from a bioethical perspective, i.e., how such a quantitative analysis could uncover hidden subject matters that have ethical relevance. For example, we find that hardware-related issues in DBS are far more robustly connected to an ethical context compared to impulsivity, concrete side-effects or death/suicide. Our contribution also outlines the methodology of quantitative text analysis that combines statistical approaches with expert knowledge. It thus serves as an example how innovative quantitative tools can be made useful for gaining a better understanding in the field of DBS.