Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Stimulation-induced respiratory enhancement in corticothalamic regions.

OBJECTIVE: We aimed to identify corticothalamic areas and electrical stimulation paradigms that optimally enhance breathing. METHODS: Twenty-nine patients with medically intractable epilepsy were prospectively recruited in an epilepsy monitoring unit while undergoing stereoelectroencephalographic evaluation. Direct electrical stimulation in cortical and thalamic regions was carried out using low (<1 Hz) and high (≥10 Hz) frequencies, and low (<5 mA) and high (≥5 mA) current intensities, with pulse width of .1 ms. Electrocardiography, arterial oxygen saturation (SpO2 ), end-tidal carbon dioxide (ETCO2 ), oronasal airflow, and abdominal and thoracic plethysmography were monitored continuously during stimulations. Airflow signal was used to estimate breathing rate, tidal volume, and minute ventilation (MV) changes during stimulation, compared to baseline. RESULTS: Electrical stimulation increased MV in the amygdala, anterior cingulate, anterior insula, temporal pole, and thalamus, with an average increase in MV of 20.8% ± 28.9% (range = 0.2%-165.6%) in 19 patients. MV changes were associated with SpO2 and ETCO2 changes (p < .001). Effects on respiration were parameter and site dependent. Within amygdala, low-frequency stimulation of the medial region produced 78.49% greater MV change (p < .001) compared to high-frequency stimulation. Longer stimulation produced greater MV changes (an increase of 4.47% in MV for every additional 10 s, p = .04). SIGNIFICANCE: Stimulation of amygdala, anterior cingulate gyrus, anterior insula, temporal pole, and thalamus, using certain stimulation paradigms, enhances respiration. Among tested paradigms, low-frequency, low-intensity, long-duration stimulation of the medial amygdala is the most effective breathing enhancement stimulation strategy. Such approaches may pave the way for the future development of neuromodulatory techniques that aid rescue from seizure-related apnea, potentially as a targeted sudden unexpected death in epilepsy prevention method.

Forecasting seizure clusters from chronic ambulatory electrocorticography.

Seizure clusters are seizures that occur in rapid succession during periods of heightened seizure risk and are associated with substantial morbidity and sudden unexpected death in epilepsy. The objective of this feasibility study was to evaluate the performance of a novel seizure cluster forecasting algorithm. Chronic ambulatory electrocorticography recorded over an average of 38 months in 10 subjects with drug-resistant epilepsies was analyzed pseudoprospectively by dividing data into training (first 85%) and validation periods. For each subject, the probability of seizure clustering, derived from the Kolmogorov-Smirnov statistic using a novel algorithm, was forecasted in the validation period using individualized autoregressive models that were optimized from training data. The primary outcome of this study was the mean absolute scaled error (MASE) of 1-day horizon forecasts. From 10 subjects, 394 ± 142 (mean ± SD) electrocorticography-based seizure events were extracted for analysis, representing a span of 38 ± 27 months of recording. MASE across all subjects was .74 ± .09, .78 ± .09, and .83 ± .07 at .5-, 1-, and 2-day horizons. The feasibility study demonstrates that seizure clusters are quasiperiodic and can be forecasted to clinically meaningful horizons. Pending validation in larger cohorts, the forecasting approach described herein may herald chronotherapy during imminent heightened seizure vulnerability.

Utilizing thalamic deep brain stimulation for an electronic seizure diary in a definite sudden unexpected death in epilepsy case.

OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is a serious threat to individuals with intractable epilepsies, contributing to premature mortality. Understanding the elusive pathophysiological mechanisms of SUDEP, especially in cases without observable terminal events, remains a crucial area for investigation. This study aimed to shed light on the burden of epileptiform activity preceding SUDEP by utilizing an automated electronic seizure diary derived from a sensing-enabled thalamic deep brain stimulator (DBS). METHODS: Herein, we present the case of a 57-year-old man afflicted with intractable multifocal epilepsy secondary to cortical dysplasia and encephalomalacia resulting from severe traumatic brain injury. Despite an initial successful resection and subsequent resurgence of seizures necessitating DBS treatment, the patient tragically succumbed to SUDEP. RESULTS: In-depth analysis of the patient's electronic seizure diary, complemented by data from the sensing-enabled DBS, unveiled a terminal electrographic seizure. Notably, we observed a significant increase in power within specific frequency bands recorded from the thalamus preceding the terminal event. Furthermore, these heightened band power events displayed a discernible temporal clustering pattern, primarily manifesting during specific morning and evening hours. An autopsy conclusively confirmed the diagnosis of definite SUDEP. INTERPRETATION: This unique case report underscores the feasibility of harnessing thalamic DBS sensing capabilities to monitor seizure burden and, potentially, to tailor interventions aimed at reducing seizure frequency and associated mortality risks.

Spindle oscillations in communicating axons within a reconstituted hippocampal formation are strongest in CA3 without thalamus.

Spindle-shaped waves of oscillations emerge in EEG scalp recordings during human and rodent non-REM sleep. The association of these 10-16 Hz oscillations with events during prior wakefulness suggests a role in memory consolidation. Human and rodent depth electrodes in the brain record strong spindles throughout the cortex and hippocampus, with possible origins in the thalamus. However, the source and targets of the spindle oscillations from the hippocampus are unclear. Here, we employed an in vitro reconstruction of four subregions of the hippocampal formation with separate microfluidic tunnels for single axon communication between subregions assembled on top of a microelectrode array. We recorded spontaneous 400-1000 ms long spindle waves at 10-16 Hz in single axons passing between subregions as well as from individual neurons in those subregions. Spindles were nested within slow waves. The highest amplitudes and most frequent occurrence suggest origins in CA3 neurons that send feed-forward axons into CA1 and feedback axons into DG. Spindles had 50-70% slower conduction velocities than spikes and were not phase-locked to spikes suggesting that spindle mechanisms are independent of action potentials. Therefore, consolidation of declarative-cognitive memories in the hippocampus may be separate from the more easily accessible consolidation of memories related to thalamic motor function.

The flow of axonal information among hippocampal sub-regions 2: patterned stimulation sharpens routing of information transmission.

The sub-regions of the hippocampal formation are essential for episodic learning and memory formation, yet the spike dynamics of each region contributing to this function are poorly understood, in part because of a lack of access to the inter-regional communicating axons. Here, we reconstructed hippocampal networks confined to four subcompartments in 2D cultures on a multi-electrode array that monitors individual communicating axons. In our novel device, somal, and axonal activity was measured simultaneously with the ability to ascertain the direction and speed of information transmission. Each sub-region and inter-regional axons had unique power-law spiking dynamics, indicating differences in computational functions, with abundant axonal feedback. After stimulation, spiking, and burst rates decreased in all sub-regions, spikes per burst generally decreased, intraburst spike rates increased, and burst duration decreased, which were specific for each sub-region. These changes in spiking dynamics post-stimulation were found to occupy a narrow range, consistent with the maintenance of the network at a critical state. Functional connections between the sub-region neurons and communicating axons in our device revealed homeostatic network routing strategies post-stimulation in which spontaneous feedback activity was selectively decreased and balanced by decreased feed-forward activity. Post-stimulation, the number of functional connections per array decreased, but the reliability of those connections increased. The networks maintained a balance in spiking and bursting dynamics in response to stimulation and sharpened network routing. These plastic characteristics of the network revealed the dynamic architecture of hippocampal computations in response to stimulation by selective routing on a spatiotemporal scale in single axons.

Hippocampal network axons respond to patterned theta burst stimulation with lower activity of initially higher spike train similarity from EC to DG and later similarity of axons from CA1 to EC.

Objective. Decoding memory functions for each hippocampal subregion involves extensive understanding of how each hippocampal subnetwork processes input stimuli. Theta burst stimulation (TBS) recapitulates natural brain stimuli which potentiates synapses in hippocampal circuits. TBS is typically applied to a bundle of axons to measure the immediate response in a downstream subregion like the cornu ammonis 1 (CA1). Yet little is known about network processing in response to stimulation, especially because individual axonal transmission between subregions is not accessible.Approach. To address these limitations, we reverse engineered the hippocampal network on a micro-electrode array partitioned by a MEMS four-chambered device with interconnecting microfluidic tunnels. The micro tunnels allowed monitoring single axon transmission which is inaccessible in slices orin vivo. The four chambers were plated separately with entorhinal cortex (EC), dentate gyrus (DG), CA1, and CA3 neurons. The patterned TBS was delivered to the EC hippocampal gateway. Evoked spike pattern similarity in each subregions was quantified with Jaccard distance metrics of spike timing.Main results. We found that the network subregion produced unique axonal responses to different stimulation patterns. Single site and multisite stimulations caused distinct information routing of axonal spikes in the network. The most spatially similar output at axons from CA3 to CA1 reflected the auto association within CA3 recurrent networks. Moreover, the spike pattern similarities shifted from high levels for axons to and from DG at 0.2 s repeat stimuli to greater similarity in axons to and from CA1 for repetitions at 10 s intervals. This time-dependent response suggested that CA3 encoded temporal information and axons transmitted the information to CA1.Significance. Our design and interrogation approach provide first insights into differences in information transmission between the four subregions of the structured hippocampal network and the dynamic pattern variations in response to stimulation at the subregional level to achieve probabilistic pattern separation and novelty detection.

Advancing the frontiers of thalamic neuromodulation: A review of emerging targets and paradigms.

The thalamus is a key structure that plays a crucial role in initiating and propagating seizures. Recent advancements in neuroimaging and neurophysiology have identified the thalamus as a promising target for neuromodulation in drug-resistant epilepsies. This review article presents the latest innovations in thalamic targets and neuromodulation paradigms being explored in pilot or pivotal clinical trials. Multifocal temporal plus or posterior quadrant epilepsies are evaluated with pulvinar thalamus neuromodulation, while centromedian thalamus is explored in generalized epilepsies and Lennox Gastaut syndrome. Multinodal thalamocortical neuromodulation with novel stimulation paradigms such as long bursting or low-frequency stimulation is being investigated to quench the epileptic network excitability. Beyond seizure control, thalamic neuromodulation to restore consciousness is being studied. This review highlights the promising potential of thalamic neuromodulation in epilepsy treatment, offering hope to patients who have not responded to conventional medical therapies. However, it also emphasizes the need for larger randomized controlled trials and personalized stimulation paradigms to improve patient outcomes further.

Pulvinar neuromodulation for seizure monitoring and network modulation in temporal plus epilepsy.

Deep brain stimulation (DBS) is a promising treatment for drug-refractory epilepsies (DRE) when targeting the anterior nuclei of thalamus (ANT). However, targeting other thalamic nuclei, such as the pulvinar, shows therapeutic promise. Our pioneering case study presents the application of ambulatory seizure monitoring using spectral fingerprinting (12.15-17.15 Hz) recorded through Medtronic Percept DBS implanted bilaterally in the medial pulvinar thalami. This technology offers unprecedented opportunities for real-time monitoring of seizure burden and thalamocortical network modulation for effective seizure reduction in patients with bilateral mesial temporal and temporal plus epilepsies that are not suitable for resection.

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing.

The tri-synaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information inputs largely flow from the entorhinal cortex (EC) to the dentate gyrus (DG), and then are processed further in the CA3 and CA1 before returning to the EC. Here, we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed for monitoring the directionality of individual axons between the subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in microfluidic tunnels. The majority of axons from the EC to the DG operated in the feed-forward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s, indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of inter-burst intervals. This suggested the presence of up-states and down-states in every region, with the least up-states in the DG to CA3 feed-forward axons and the CA3 subregion. The peaks of the log-normal distributions of intra-burst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also log-normally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled the generation of structural connectivity graphs, not possible previously without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational artificial intelligence (AI) networks, neuromorphic hardware, and stimulation and decoding from cognitive implants.