Assessing the feasibility of digital keypress statistics to detect seizures and capture cognitive impairment in patients with epilepsy: A pilot study.

BACKGROUND: Efficient, non-invasive monitoring may provide a more accurate and comprehensive understanding of seizure frequency and the development of some comorbidities in people with epilepsy. Novel keyboard technology measuring digital keypress statistics has demonstrated its practical value for neurodegenerative diseases including Parkinson's Disease and Dementia. Smartphones integrated into daily life may serve as a low-burden longitudinal monitoring system for patients with epilepsy. OBJECTIVE: This study aimed to assess the feasibility of keyboard statistics as an objective measure of seizure frequency for patients with epilepsy, in addition to tracking differences between cognitively normal and cognitively impaired patients. METHODS: Six adult patients admitted to the Epilepsy Monitoring Unit (EMU) at Mayo Clinic in Rochester, Minnesota were studied. The keyboard was installed on the patient's smartphone. In the EMU, typing statistics were correlated to electroencephalogram (EEG) confirmed seizures. After discharge, participants continued using their keyboards and kept a seizure log. We also analyzed the key press/release times and usage of participants' keyboards for adherence. RESULTS: Keyboard sessions during and after seizures assessed for key press/release differences versus baseline showed no statistically significant difference (p = 0.44). Using one-way ANOVA, cognitive impairment's potential impact on keyboard statistics was explored in patients who had neuropsychological testing (N = 3). Significant differences were found between patients with and without cognitive impairment (p < 0.001). No significant difference was noted between patients with mild intellectual disability and normal cognitive function (p = 0.55).

User experience of a seizure risk forecasting app: A mixed methods investigation.

OBJECTIVE: Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy. METHODS: Active app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively. RESULTS: Surveys were completed by 111 users. Responders consisted of "app users" (n = 58), and "app and forecast users" (n = 53). Of the "app and forecast users", 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which 'frustrations with lack of direction' (regarding their current epilepsy management approach), 'benefits of increased self-knowledge' and 'current and anticipated usefulness of forecasting' were the most common. SIGNIFICANCE: Preliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.

How accurate do self-reported seizures need to be for effective medication management in epilepsy?

Studies suggest that self-reported seizure diaries suffer from 50% under-reporting on average. It is unknown to what extent this impacts medication management. This study used simulation to predict the seizure outcomes of a large heterogeneous clinic population treated with a standardized algorithm based on self-reported seizures. Using CHOCOLATES, a state-of-the-art realistic seizure diary simulator, 100 000 patients were simulated over 10 years. A standard algorithm for medication management was employed at 3 month intervals for all patients. The impact on true seizure rates, expected seizure rates, and time-to-steady-dose were computed for self-reporting sensitivities 0%-100%. Time-to-steady-dose and medication use mostly did not depend on sensitivity. True seizure rate decreased minimally with increasing self-reporting in a non-linear fashion, with the largest decreases at low sensitivity rates (0%-10%). This study suggests that an extremely wide range of sensitivity will have similar seizure outcomes when patients are clinically treated using an algorithm similar to the one presented. Conversely, patients with sensitivity ≤10% would be expected to benefit (via lower seizure rates) from objective devices that provide even small improvements in seizure sensitivity.

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.

Minimum clinical utility standards for wearable seizure detectors: A simulation study.

OBJECTIVE: Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.

Spike ripples localize the epileptogenic zone best: an international intracranial study.

We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone (EZ) compared to other leading interictal biomarkers in a multicenter, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centers who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection (ILAE 1 outcome) and those with persistent seizures (ILAE 2-6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80-500 Hz), ripple (80-250 Hz), and fast ripple (250-500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001, P < 0.001). Among ILAE 1 subjects, the mean spike ripple rate was higher in the RV (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared to ILAE 2-6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01), or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicenter cohort, when surgical resection was successful, the majority of spike ripples were removed. Further, automatically detected spike ripples have improved specificity for epileptogenic tissue compared to spikes, spike-gamma, wideband HFOs, ripples, and fast ripples.

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 (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.

Technical Considerations in EEG Source Imaging.

SUMMARY: EEG source imaging is an established technique for identifying the origin of interictal and ictal epileptiform discharges in patients with epilepsy, and it is an important tool in neurophysiology research. Accurate and reliable EEG source imaging requires appropriate choices of how the head, skull, and scalp are modeled, and understanding of the different approaches to modeling is important to guide these choices. Similarly, numerous different approaches to modeling the electrical sources within the brain exist, and appropriate understanding of the strengths and limitations of each are essential to obtaining accurate, reliable, and interpretable solutions. This review aims to describe the essential theoretical basis for these head and source models while also discussing the practical implications of each in clinical or research applications.

Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics.

This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p<0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p>0.05). There was a significant difference between ovulating and non-ovulating cycles (p<0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (µS), respectively.

Implications of intracranial hemorrhage associated with stereo-EEG.

OBJECTIVE: Intracranial hemorrhage (ICH) is a known complication during stereo-electroencephalography (sEEG) however true rates remain unknown. We provide a comprehensive review of ICH during sEEG regardless of clinical symptoms. Secondly, we analyzed sEEG recordings to identify electrographic correlates of ICH. METHODS: This is a retrospective study of patients undergoing sEEG between January 2016 and April 2022 at the Mayo Clinic in Rochester. We reviewed medical records and imaging studies to identify ICH. We analyzed ICH by type, electrode trajectories, timing, sEEG findings and outcomes. RESULTS: There were a total of 201 sEEG implants, of which 23 (11%) cases or 0.9% electrodes implanted had evidence of ICH. The majority of affected patients (82%) were either asymptomatic or had mild clinical neurological manifestations. In 90% of patients who proceeded with surgical treatments, outcomes were favorable. The most common sEEG finding in contacts in proximity of ICH was either focal slowing with interictal discharges or focal electrographic seizures. CONCLUSIONS: ICH associated with sEEG is likely under-reported in literature. We present electroencephalographic correlates of ICH that may aid identification of ICH in the course of performing sEEG monitoring. SIGNIFICANCE: Our data provides clinically relevant information on potential risks and outcomes of ICH. Furthermore, our findings aid identification of ICH during sEEG.

EEG Source Localization in Temporal Encephaloceles: Concordance With Surgical Resection and Clinical Outcomes.

PURPOSE: Temporal encephaloceles are a cause of drug-resistant temporal lobe epilepsy; however, their relationship with epileptogenesis is unclear, and optimal surgical resection is uncertain. EEG source localization (ESL) may guide surgical decision-making. METHODS: We reviewed patients at Mayo Clinic Rochester with drug-resistant temporal lobe epilepsy and temporal encephaloceles, who underwent limited resection and had 1-year outcomes. EEG source localization was performed using standard density scalp EEG of ictal and interictal activity. Distance from dipole and standardized low-resolution brain electromagnetic tomography (sLORETA) solutions to the encephalocele were measured. Concordance of ESL with encephalocele and surgical resection was compared with 1-year surgical outcomes. RESULTS: Seventeen patients met criteria. The mean distances from ESL results to encephalocele center for dipole and sLORETA analyses were 23 mm (SD 9) and 22 mm (SD 11), respectively. Ten patients (55.6%) had Engel I outcomes at 1 year. Dipole-encephalocele distance and sLORETA-encephalocele distance were significantly longer in patients with Engel I outcome and patients whose encephalocele was contained by sLORETA had worse outcome as well; however, multiple logistic regression analysis found that only containment of encephalocele by the sLORETA current density was significant (P < 0.05), odds ratio 0.12 (95% confidence interval [0.021, 0.71]). CONCLUSIONS: EEG source localization of scalp EEG localizes near encephaloceles, however, typically not in the encephalocele itself; this may be due to scalp EEG sampling propagated activity or alternatively that the seizure onset zone extends beyond the herniated cortex. Surprisingly, we observed increased ESL to encephalocele distances in patients with excellent surgical outcomes. Larger cohort studies including intracranial EEG data are needed to further explore this finding.

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.

Signatures of Electrical Stimulation Driven Network Interactions in the Human Limbic System.

Stimulation-evoked signals are starting to be used as biomarkers to indicate the state and health of brain networks. The human limbic network, often targeted for brain stimulation therapy, is involved in emotion and memory processing. Previous anatomic, neurophysiological, and functional studies suggest distinct subsystems within the limbic network (Rolls, 2015). Studies using intracranial electrical stimulation, however, have emphasized the similarities of the evoked waveforms across the limbic network. We test whether these subsystems have distinct stimulation-driven signatures. In eight patients (four male, four female) with drug-resistant epilepsy, we stimulated the limbic system with single-pulse electrical stimulation. Reliable corticocortical evoked potentials (CCEPs) were measured between hippocampus and the posterior cingulate cortex (PCC) and between the amygdala and the anterior cingulate cortex (ACC). However, the CCEP waveform in the PCC after hippocampal stimulation showed a unique and reliable morphology, which we term the "limbic Hippocampus-Anterior nucleus of the thalamus-Posterior cingulate, HAP-wave." This limbic HAP-wave was visually distinct and separately decoded from the CCEP waveform in ACC after amygdala stimulation. Diffusion MRI data show that the measured end points in the PCC overlap with the end points of the parolfactory cingulum bundle rather than the parahippocampal cingulum, suggesting that the limbic HAP-wave may travel through fornix, mammillary bodies, and the anterior nucleus of the thalamus (ANT). This was further confirmed by stimulating the ANT, which evoked the same limbic HAP-wave but with an earlier latency. Limbic subsystems have unique stimulation-evoked signatures that may be used in the future to help network pathology diagnosis.SIGNIFICANCE STATEMENT The limbic system is often compromised in diverse clinical conditions, such as epilepsy or Alzheimer's disease, and characterizing its typical circuit responses may provide diagnostic insight. Stimulation-evoked waveforms have been used in the motor system to diagnose circuit pathology. We translate this framework to limbic subsystems using human intracranial stereo EEG (sEEG) recordings that measure deeper brain areas. Our sEEG recordings describe a stimulation-evoked waveform characteristic to the memory and spatial subsystem of the limbic network that we term the "limbic HAP-wave." The limbic HAP-wave follows anatomic white matter pathways from hippocampus to thalamus to the posterior cingulum and shows promise as a distinct biomarker of signaling in the human brain memory and spatial limbic network.

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.

Temporo-Parietal Extraventricular Approach for Deep Brain Stimulation Targeting the Anterior Nucleus of the Thalamus: Institutional Experience.

BACKGROUND AND OBJECTIVES: The anterior nucleus of the thalamus (ANT) is a common target for deep brain stimulation (DBS) for drug-resistant epilepsy (DRE). However, the surgical approach to the ANT remains challenging because of its unique anatomy. This study aims to summarize our experience with the posterior temporo-parietal extraventricular (TPEV) approach targeting the ANT for DBS in DRE. METHODS: We performed a retrospective analysis of patients with DRE who underwent ANT-DBS using the TPEV approach between January 2011 and February 2021. Subjects with at least 6-month follow-up were eligible. The final lead position and number of active contacts targeting the anteroventral nucleus (AV) of the ANT were assessed using Lead-DBS. Mean seizure frequency reduction percentage and responder rate (≥50% decrease in seizure frequency) were determined. RESULTS: Thirty-one patients (mean age: 32.9 years; 52% female patients) were included. The mean follow-up period was 27.6 months ± 13.9 (29, 16-36). The mean seizure frequency reduction percentage was 65% ± 26 (75, 50-82). Twenty-six of 31 participants (83%) were responders, P < .001. Two subjects (6%) were seizure-free for at least 6 months at the last evaluation. Antiepileptic drugs dose and/or number decreased in 17/31 subjects (55%). The success rate for placing at least 1 contact at AV was 87% (27/31 patients) bilaterally. The number of active contacts at the AV was significantly greater in the responder group, 3.1 ± 1.3 (3, 2-4) vs 1.8 ± 1.1 (2, 1-2.5); P = .041 with a positive correlation between the number of active contacts and seizure reduction percentage; r = 0.445, R 2 = 0.198, P = .012. CONCLUSION: The TPEV trajectory is a safe and effective approach to target the ANT for DBS. Future studies are needed to compare the clinical outcomes and target accuracy with the standard approaches.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study.

BACKGROUND: Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. METHOD: Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. FINDINGS: The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. INTERPRETATION: The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. FUNDING: This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography.

OBJECTIVE: Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments. METHODS: Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). RESULTS: Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures. SIGNIFICANCE: Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.

Temporal encephalocele: An epileptogenic focus confirmed by direct intracranial electroencephalography.

Several studies have suggested the epileptogenic potential of temporal encephaloceles. However, there is limited literature describing the results of intracranial EEG monitoring for patients with temporal encephaloceles. We describe a 19 year-old right-handed woman with drug-resistant epilepsy who presented with seizure onset at age 16 in the setting of a left temporal encephalocele where the seizure onset zone was confirmed to be the encephalocele via stereo EEG (sEEG). She had focal impaired awareness seizures occurring weekly that would progress to focal to bilateral tonic-clonic seizures monthly. Imaging showed a left anterior inferior temporal lobe encephalocele and a left choroidal fissure cyst that were stable on repeat imaging. Prolonged scalp recorded video EEG recorded seizures that showed either near simultaneous onset in the bitemporal head regions or a transitional left temporal sharp wave followed by maximum evolution in the left temporal region. Invasive monitoring with sEEG electrodes targeting primarily the left limbic system with one electrode directly in the encephalocele captured seizures with onset in the left temporal pole encephalocele. A limited resection was performed based on the results of the sEEG and except for one seizure in the immediate postop period in the setting of infection, patient remains seizure free at her 4 month follow up. This report describes a case of drug-resistant focal epilepsy where sEEG monitoring confirmed a temporal encephalocele to be the seizure onset zone without simultaneous onset at mesial temporal or other neocortical structures that were sampled. Our findings support the potential for epileptogenicity within an encephalocele with direct intracranial monitoring.