Behavioral state classification in epileptic brain using intracranial electrophysiology

A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy

Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy, but the optimal stimulation parameters remain unclear. We developed a neurotechnology platform for tracking seizures and MMS to enable data streaming between an investigational brain sensing-stimulation implant, mobile devices, and a cloud environment. Artificial Intelligence algorithms provided accurate catalogs of seizures, interictal epileptiform spikes, and wake-sleep brain states. Remotely administered memory and mood assessments were used to densely sample cognitive and behavioral response during ANT-DBS. We evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. They both reduced seizures, but low-frequency ANT-DBS showed greater reductions and better sleep and memory. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapy.

Microglia enhance post-anesthesia neuronal activity by shielding inhibitory synapses

Microglia are resident immune cells of the central nervous system and play key roles in brain homeostasis. During anesthesia, microglia increase their dynamic process surveillance and interact more closely with neurons. However, the functional significance of microglial process dynamics and neuronal interaction under anesthesia is largely unknown. Using in vivo two-photon imaging in mice, we show that microglia enhance neuronal activity after the cessation of isoflurane anesthesia. Hyperactive neuron somata are contacted directly by microglial processes, which specifically colocalize with GABAergic boutons. Electron-microscopy-based synaptic reconstruction after two-photon imaging reveals that, during anesthesia, microglial processes enter into the synaptic cleft to shield GABAergic inputs. Microglial ablation or loss of microglial β2-adrenergic receptors prevents post-anesthesia neuronal hyperactivity. Our study demonstrates a previously unappreciated function of microglial process dynamics, which enable microglia to transiently boost post-anesthesia neuronal activity by physically shielding inhibitory inputs.

Impaired microglial phagocytosis promotes seizure development

In the central nervous system, triggering receptor expressed on myeloid cells 2 (TREM2) is exclusively expressed by microglia and is critical for microglial proliferation, migration, and phagocytosis. TREM2 plays an important role in neurodegenerative diseases, such as Alzheimer's disease and amyotrophic lateral sclerosis. However, little is known about the role TREM2 plays in epileptogenesis. To investigate this, we utilized TREM2 knockout (KO) mice within the murine intra-amygdala kainic acid seizure model. Electroencephalographic analysis, immunocytochemistry, and RNA sequencing revealed that TREM2 deficiency significantly promoted seizure-induced pathology. We found that TREM2 KO increased both acute status epilepticus and spontaneous recurrent seizures characteristic of chronic focal epilepsy. Mechanistically, phagocytic clearance of damaged neurons by microglia was impaired in TREM2 KO mice and the reduced phagocytic capacity correlated with increased spontaneous seizures. Analysis of human tissue from patients who underwent surgical resection for drug resistant temporal lobe epilepsy also showed a negative correlation between microglial phagocytic activity and focal to bilateral tonic-clonic generalized seizure history. These results indicate that microglial TREM2 and phagocytic activity may be important to epileptogenesis and the progression of focal temporal lobe epilepsy.

Impedance Rhythms in Human Limbic System

The impedance is a fundamental electrical property of brain tissue, playing a crucial role in shaping the characteristics of local field potentials, the extent of ephaptic coupling, and the volume of tissue activated by externally applied electrical brain stimulation. We tracked brain impedance, sleep-wake behavioral state, and epileptiform activity in five people with epilepsy living in their natural environment using an investigational device. The study identified impedance oscillations that span hours to weeks in the amygdala, hippocampus, and anterior nucleus thalamus. The impedance in these limbic brain regions exhibit multiscale cycles with ultradian (∼1.5-1.7 h), circadian (∼21.6-26.4 h), and infradian (∼20-33 d) periods. The ultradian and circadian period cycles are driven by sleep-wake state transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Limbic brain tissue impedance reaches a minimum value in NREM sleep, intermediate values in REM sleep, and rises through the day during wakefulness, reaching a maximum in the early evening before sleep onset. Infradian (∼20-33 d) impedance cycles were not associated with a distinct behavioral correlate. Brain tissue impedance is known to strongly depend on the extracellular space (ECS) volume, and the findings reported here are consistent with sleep-wake-dependent ECS volume changes recently observed in the rodent cortex related to the brain glymphatic system. We hypothesize that human limbic brain ECS changes during sleep-wake state transitions underlie the observed multiscale impedance cycles. Impedance is a simple electrophysiological biomarker that could prove useful for tracking ECS dynamics in human health, disease, and therapy.SIGNIFICANCE STATEMENT The electrical impedance in limbic brain structures (amygdala, hippocampus, anterior nucleus thalamus) is shown to exhibit oscillations over multiple timescales. We observe that impedance oscillations with ultradian and circadian periodicities are associated with transitions between wakefulness, NREM, and REM sleep states. There are also impedance oscillations spanning multiple weeks that do not have a clear behavioral correlate and whose origin remains unclear. These multiscale impedance oscillations will have an impact on extracellular ionic currents that give rise to local field potentials, ephaptic coupling, and the tissue activated by electrical brain stimulation. The approach for measuring tissue impedance using perturbational electrical currents is an established engineering technique that may be useful for tracking ECS volume.

Louis EK, Kremen V, Worrell GA. Automated sleep classification with chronic neural implants in freely behaving canines

Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.

Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.

Decoding Intracranial EEG With Machine Learning: A Systematic Review

Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.

SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only

OBJECTIVE

To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction.

APPROACH

Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch.

MAIN RESULTS

An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%.

SIGNIFICANCE

The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.

Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.

Intravenous and Intramuscular Allopregnanolone for Early Treatment of Status Epilepticus: Pharmacokinetics, Pharmacodynamics, and Safety in Dogs

Allopregnanolone (ALLO) is a neurosteroid that modulates synaptic and extrasynaptic GABAA receptors. We hypothesize that ALLO may be useful as first-line treatment of status epilepticus (SE). Our objectives were to (1) characterize ALLO pharmacokinetics-pharmacodynamics PK-PD after intravenous (IV) and intramuscular (IM) administration and (2) compare IV and IM ALLO safety and tolerability. Three healthy dogs and two with a history of epilepsy were used. Single ALLO IV doses ranging from 1-6 mg/kg were infused over 5 minutes or injected IM. Blood samples, vital signs, and sedation assessment were collected up to 8 hours postdose. Intracranial EEG (iEEG) was continuously recorded in one dog. IV ALLO exhibited dose-proportional increases in exposure, which were associated with an increase in absolute power spectral density in all iEEG frequency bands. This relationship was best described by an indirect link PK-PD model where concentration-response was described by a sigmoidal maximum response (Emax) equation. Adverse events included site injection pain with higher IM volumes and ataxia and sedation associated with higher doses. IM administration exhibited incomplete absorption and volume-dependent bioavailability. Robust iEEG changes after IM administration were not observed. Based on PK-PD simulations, a 2 mg/kg dose infused over 5 minutes is predicted to achieve plasma concentrations above the EC50, but below those associated with heavy sedation. This study demonstrates that ALLO is safe and well tolerated when administered at 1-4 mg/kg IV and up to 2 mg/kg IM. The rapid onset of effect after IV infusion suggests that ALLO may be useful in the early treatment of SE. SIGNIFICANCE STATEMENT: The characterization of the pharmacokinetics and pharmacodynamics of allopregnanolone is essential in order to design clinical studies evaluating its effectiveness as an early treatment for status epilepticus in dogs and people. This study has proposed a target dose/therapeutic range for a clinical trial in canine status epilepticus.

Leveraging electrophysiologic correlates of word encoding to map seizure onset zone in focal epilepsy: Task-dependent changes in epileptiform activity, spectral features, and functional connectivity

OBJECTIVE

Verbal memory dysfunction is common in focal, drug-resistant epilepsy (DRE). Unfortunately, surgical removal of seizure-generating brain tissue can be associated with further memory decline. Therefore, localization of both the circuits generating seizures and those underlying cognitive functions is critical in presurgical evaluations for patients who may be candidates for resective surgery. We used intracranial electroencephalographic (iEEG) recordings during a verbal memory task to investigate word encoding in focal epilepsy. We hypothesized that engagement in a memory task would exaggerate local iEEG feature differences between the seizure onset zone (SOZ) and neighboring tissue as compared to wakeful rest ("nontask").

METHODS

Ten participants undergoing presurgical iEEG evaluation for DRE performed a free recall verbal memory task. We evaluated three iEEG features in SOZ and non-SOZ electrodes during successful word encoding and compared them with nontask recordings: interictal epileptiform spike (IES) rates, power in band (PIB), and relative entropy (REN; a functional connectivity measure).

RESULTS

We found a complex pattern of PIB and REN changes in SOZ and non-SOZ electrodes during successful word encoding compared to nontask. Successful word encoding was associated with a reduction in local electrographic functional connectivity (increased REN), which was most exaggerated in temporal lobe SOZ. The IES rates were reduced during task, but only in the non-SOZ electrodes. Compared with nontask, REN features during task yielded marginal improvements in SOZ classification.

SIGNIFICANCE

Previous studies have supported REN as a biomarker for epileptic brain. We show that REN differences between SOZ and non-SOZ are enhanced during a verbal memory task. We also show that IESs are reduced during task in non-SOZ, but not in SOZ. These findings support the hypothesis that SOZ and non-SOZ respond differently to task and warrant further exploration into the use of cognitive tasks to identify functioning memory circuits and localize SOZ.

Seizure onset detection based on detection of changes in brain activity quantified by evolutionary game theory model

BACKGROUND AND OBJECTIVE

Epilepsy is one of the most common diseases of the nervous system, affecting about 1% of the world's population. The unpredictable nature of the epilepsy seizures deprives the patients and those around them of living a normal life. Therefore, the development of new methods that can help these patients will increase the life quality of these people and can bring a lot of economic savings in the health sector.

METHODS

In this study, we introduced a new framework for seizure onset detection. Our framework provides a new modelling for brain activity using evolutionary game theory and Kalman filter. If the patterns in the electroencephalogram (EEG) signal violate the predicted patterns by the proposed model, using a novel detection algorithm that has been also introduced in this paper, it can be determined whether the observed violation is the result of the onset of an epileptic seizure or not.

RESULTS

The proposed approach was able to detect the onset of all the seizures in CHB-MIT dataset with an average delay of -0.8 s and a false alarm of 0.39 per hour. Also, our proposed approach is about 20 times faster compared to recent studies.

CONCLUSIONS

The experimental results of applying the proposed framework on the CHB-MIT dataset show that our framework not only performed well with respect to the sensitivity, delay, and false alarm metrics but also performed much better in terms of run time compared to recent studies. This appropriate run time, along with other suitable metrics, makes it possible to use this framework in many cases where processing power or energy is limited and to think about creating new and inexpensive solutions for the treatment and care of people diagnosed with epilepsy.

Perspectives on the current developments with neuromodulation for the treatment of epilepsy

Introduction: As deep brain stimulation revolutionized the treatment of movement disorders in the late 80s, neuromodulation in the treatment of epilepsy will undoubtedly undergo transformative changes in the years to come with the exponential growth of technological development moving into mainstream practice; the appearance of companies such as Facebook, Google, Neuralink within the realm of brain-computer interfaces points to this trend.Areas covered: This perspective piece will talk about the history of brain stimulation in epilepsy, current-approved treatments, technical developments and the future of neurostimulation.Expert opinion: Further understanding of the brain alongside machine learning and innovative technology will be the future of neuromodulation for the treatment of epilepsy. All of these innovations and advances should pave the way toward overcoming the vexing underutilization of surgery in the therapeutic armamentarium against medically refractory seizures, given the implicit advantage of a neuromodulatory rather than neurodestructive approach.

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".

Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance

Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metric. We validated our results with anatomical localization revealing significantly greater distribution of active electrodes in brain regions that support verbal memory processing. We propose our machine-learning framework for objective and efficient classification and interpretation of electrophysiological signals of brain activities supporting memory and cognition.

Multi-feature localization of epileptic foci from interictal, intracranial EEG

OBJECTIVE

When considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data.

METHODS

We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording.

RESULTS

The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95.

CONCLUSION

SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects.

SIGNIFICANCE

In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.

Slowing less than 1 Hz is decreased near the seizure onset zone

Focal slowing (<4 Hz) of brain waves is often associated with focal cerebral dysfunction and is assumed to be increased closest to the location of dysfunction. Prior work suggests that slowing may be comprised of at least two distinct neural mechanisms: slow oscillation activity (<1 Hz) may reflect primarily inhibitory cortical mechanisms while power in the delta frequency (1-4 Hz) may correlate with local synaptic strength. In focal epilepsy patients, we examined slow wave activity near and far from the seizure onset zone (SOZ) during wake, sleep, and postictal states using intracranial electroencephalography. We found that slow oscillation (0.3-1 Hz) activity was decreased near the SOZ, while delta activity (2-4 Hz) activity was increased. This finding was most prominent during sleep, and accompanied by a loss of long-range intra-hemispheric synchrony. In contrast to sleep, postictal slowing was characterized by a broadband increase of spectral power, and showed a reduced modulatory effect of slow oscillations on higher frequencies. These results suggest slow oscillation focal slowing is reduced near the seizure onset zone, perhaps reflecting reduced inhibitory activity. Dissociation between slow oscillation and delta slowing could help localize the seizure onset zone from interictal intracranial recordings.

Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials

Deep brain stimulation (DBS) is an established treatment for patients with Parkinson's disease (PD). Sleep disorders are common complications of PD and affected by subthalamic DBS treatment. To achieve more precise neuromodulation, chronicsleepmonitoringand closed-loop DBS toward sleep-wake cycles could potentially be utilized. Local field potential (LFP) signals that are sensed by the DBS electrode could be processed as primary feedback signals. This is the first study to systematically investigate the sleep-stage classification based on LFPs in subthalamic nucleus (STN). With our newly developed recording and transmission system, STN-LFPs were collected from 12 PD patients during wakefulness and nocturnal polysomnography sleep monitoring at one month after DBS implantation. Automatic sleep-stage classificationmodels were built with robust and interpretable machine learning methods (support vector machine and decision tree). The accuracy, sensitivity, selectivity, and specificity of the classification reached high values (above90% at most measures) at group and individual levels. Features extracted in alpha (8-13 Hz), beta (13-35 Hz), and gamma (35-50 Hz) bandswere found to contribute the most to the classification. These results will directly guide the engineering development of implantable sleepmonitoring and closed-loopDBS and pave the way for a better understanding of the STN-LFP sleep patterns.

Cloud computing for seizure detection in implanted neural devices

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 h^-1.

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.

A Chronically Implantable Neural Coprocessor for Investigating the Treatment of Neurological Disorders

Developing new tools to better understand disorders of the nervous system, with a goal to more effectively treat them, is an active area of bioelectronic medicine research. Future tools must be flexible and configurable, given the evolving understanding of both neuromodulation mechanisms and how to configure a system for optimal clinical outcomes. We describe a system, the Summit RC+S "neural coprocessor," that attempts to bring the capability and flexibility of a microprocessor to a prosthesis embedded within the nervous system. This paper describes the updated system architecture for the Summit RC+S system, the five custom integrated circuits required for bi-directional neural interfacing, the supporting firmware/software ecosystem, and the verification and validation activities to prepare for human implantation. Emphasis is placed on design changes motivated by experience with the CE-marked Activa PC+S research tool; specifically, enhancement of sense-stim performance for improved bi-directional communication to the nervous system, implementation of rechargeable technology to extend device longevity, and application of MICS-band telemetry for algorithm development and data management. The technology was validated in a chronic treatment paradigm for canines with naturally occurring epilepsy, including free ambulation in the home environment, which represents a typical use case for future human protocols.

Automated unsupervised behavioral state classification using intracranial electrophysiology

OBJECTIVE

Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography.

APPROACH

Data from eight patients undergoing evaluation for epilepsy surgery (age [Formula: see text], three female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1-235 Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3.

MAIN RESULTS

Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%).

SIGNIFICANCE

Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.

Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System

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.

Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy

OBJECTIVE

An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity.

APPROACH

Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy.

MAIN RESULTS

Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90 min and 2 h are sufficient to localize SOZs with accuracies that may prove clinically relevant.

SIGNIFICANCE

The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.

Bimodal coupling of ripples and slower oscillations during sleep in patients with focal epilepsy

OBJECTIVE

Differentiating pathologic and physiologic high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80-150 Hz) that occur during the up-down transition more often occur in the seizure-onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ.

METHODS

Using an automated ripple detector, we identified ripple events in 40-60 min intracranial electroencephalography (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling.

RESULTS

We found that at some individual recording sites, ripple event amplitude was coupled with the sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p < 0.05). The distribution of the pooled mean preferred phase angle, defined by combining the means from each cluster at each individual recording site, also exhibited two distinct clusters (p < 0.05). Based on the range of preferred phase angles defined by these two clusters, we partitioned each ripple event at each recording site into two groups: depth iEEG peak-trough and trough-peak. The mean ripple rates of the two groups in the SOZ and non-SOZ (NSOZ) were compared. We found that in the frontal (spindle, p = 0.009; theta, p = 0.006, slow, p = 0.004) and parietal lobe (theta, p = 0.007, delta, p = 0.002, slow, p = 0.001) the SOZ incidence rate for the ripples occurring during the trough-peak transition was significantly increased.

SIGNIFICANCE

Phase-event amplitude coupling between ripples and sleep oscillations may be useful to distinguish pathologic and physiologic events in patients with frontal and parietal SOZ.