Distributed brain co-processor for tracking spikes, seizures and behavior during electrical brain stimulation

Circadian changes in aperiodic activity are correlated with seizure reduction in patients with mesial temporal lobe epilepsy treated with responsive neurostimulation

OBJECTIVES

Responsive neurostimulation (RNS) is an established therapy for drug-resistant epilepsy that delivers direct electrical brain stimulation in response to detected epileptiform activity. However, despite an overall reduction in seizure frequency, clinical outcomes are variable, and few patients become seizure-free. The aim of this retrospective study was to evaluate aperiodic electrophysiological activity, associated with excitation/inhibition balance, as a novel electrographic biomarker of seizure reduction to aid early prognostication of the clinical response to RNS.

METHODS

We identified patients with intractable mesial temporal lobe epilepsy who were implanted with the RNS System between 2015 and 2021 at the University of Utah. We parameterized the neural power spectra from intracranial RNS System recordings during the first 3 months following implantation into aperiodic and periodic components. We then correlated circadian changes in aperiodic and periodic parameters of baseline neural recordings with seizure reduction at the most recent follow-up.

RESULTS

Seizure reduction was correlated significantly with a patient's average change in the day/night aperiodic exponent (r = .50, p = .016, n = 23 patients) and oscillatory alpha power (r = .45, p = .042, n = 23 patients) across patients for baseline neural recordings. The aperiodic exponent reached its maximum during nighttime hours (12 a.m. to 6 a.m.) for most responders (i.e., patients with at least a 50% reduction in seizures).

SIGNIFICANCE

These findings suggest that circadian modulation of baseline broadband activity is a biomarker of response to RNS early during therapy. This marker has the potential to identify patients who are likely to respond to mesial temporal RNS. Furthermore, we propose that less day/night modulation of the aperiodic exponent may be related to dysfunction in excitation/inhibition balance and its interconnected role in epilepsy, sleep, and memory.

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.

Acute to long-term characteristics of impedance recordings during neurostimulation in humans

Objective.This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus, and posterior hippocampus over an extended period.Approach.Impedance was periodically sampled every 5-15 min over several months in five subjects with drug-resistant epilepsy using an investigational neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24 h impedance cycle throughout the multi-month recording.Main results.Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 d to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24 h impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures.Significance.Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.

Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.

High and low frequency anterior nucleus of thalamus deep brain stimulation: Impact on memory and mood in five patients with treatment resistant temporal lobe epilepsy

High frequency anterior nucleus of the thalamus deep brain stimulation (ANT DBS) is an established therapy for treatment resistant focal epilepsies. Although high frequency-ANT DBS is well tolerated, patients are rarely seizure free and the efficacy of other DBS parameters and their impact on comorbidities of epilepsy such as depression and memory dysfunction remain unclear. The purpose of this study was to assess the impact of low vs high frequency ANT DBS on verbal memory and self-reported anxiety and depression symptoms. Five patients with treatment resistant temporal lobe epilepsy were implanted with an investigational brain stimulation and sensing device capable of ANT DBS and ambulatory intracranial electroencephalographic (iEEG) monitoring, enabling long-term detection of electrographic seizures. While patients received therapeutic high frequency (100 and 145 Hz continuous and cycling) and low frequency (2 and 7 Hz continuous) stimulation, they completed weekly free recall verbal memory tasks and thrice weekly self-reports of anxiety and depression symptom severity. Mixed effects models were then used to evaluate associations between memory scores, anxiety and depression self-reports, seizure counts, and stimulation frequency. Memory score was significantly associated with stimulation frequency, with higher free recall verbal memory scores during low frequency ANT DBS. Self-reported anxiety and depression symptom severity was not significantly associated with stimulation frequency. These findings suggest the choice of ANT DBS stimulation parameter may impact patients' cognitive function, independently of its impact on seizure rates.

Acute to long-term characteristics of impedance recordings during neurostimulation in humans

Objective

This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus (AMG-HPC), and posterior hippocampus (post-HPC) over an extended period.

Approach

Impedance was periodically sampled every 5-15 minutes over several months in five subjects with drug-resistant epilepsy using an experimental neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24-hour impedance cycle throughout the multi-month recording.

Main results

Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 days to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24-hour impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures.

Significance

Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.

Artificial intelligence in epilepsy phenotyping

Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.

Individualized Seizure Cluster Prediction Using Machine Learning and Chronic Ambulatory Intracranial EEG

Epilepsy patients often experience acute repetitive seizures, known as seizure clusters, which can progress to prolonged seizures or status epilepticus if left untreated. Predicting the onset of seizure clusters is crucial to enable patients to receive preventative treatments. Additionally, studying the patterns of seizure clusters can help predict the seizure type (isolated or cluster) after observing a just occurred seizure. This paper presents machine learning models that use bivariate intracranial EEG (iEEG) features to predict seizure clustering. Specifically, we utilized relative entropy (REN) as a bivariate feature to capture potential differences in brain region interactions underlying isolated and cluster seizures. We analyzed a large ambulatory iEEG dataset collected from 15 patients and spanned up to 2 years of recordings for each patient, consisting of 3341 cluster seizures (from 427 clusters) and 369 isolated seizures. The dataset's substantial number of seizures per patient enabled individualized analyses and predictions. We observed that REN was significantly different between isolated and cluster seizures in majority of the patients. Machine learning models based on REN: 1) predicted whether a seizure will occur soon after a given seizure with up to 69.5% Area under the ROC Curve (AUC), 2) predicted if a seizure is the first one in a cluster with up to 55.3% AUC, outperforming baseline techniques. Overall, our findings could be beneficial in addressing the clinical burden associated with seizure clusters, enabling patients to receive timely treatments and improving their quality of life.

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.

Translational veterinary epilepsy: A win-win situation for human and veterinary neurology

Epilepsy is a challenging multifactorial disorder with a complex genetic background. Our current understanding of the pathophysiology and treatment of epilepsy has substantially increased due to animal model studies, including canine studies, but additional basic and clinical research is required. Drug-resistant epilepsy is an important problem in both dogs and humans, since seizure freedom is not achieved with the available antiseizure medications. The evaluation and exploration of pharmacological and particularly non-pharmacological therapeutic options need to remain a priority in epilepsy research. Combined efforts and sharing knowledge and expertise between human medical and veterinary neurologists are important for improving the treatment outcomes or even curing epilepsy in dogs. Such interactions could offer an exciting approach to translate the knowledge gained from people and rodents to dogs and vice versa. In this article, a panel of experts discusses the similarities and knowledge gaps in human and animal epileptology, with the aim of establishing a common framework and the basis for future translational epilepsy research.

Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research

Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.