Citations

Abstract Objective Novel subcutaneous electroencephalography (sqEEG) systems enable prolonged, near‐continuous cerebral monitoring in real‐world conditions. Nevertheless, the feasibility, acceptability and overall clinical utility of these systems remain unclear. We report on the longest observational study using ultra‐long‐term sqEEG to date. Methods We conducted a 15‐month prospective, observational study including 10 adult people with treatment‐resistant epilepsy. After device implantation, patients were asked to record sqEEG, to use an electronic seizure diary, and to complete acceptability and usability questionnaires. sqEEG seizures were annotated visually, aided by automated detection. Individualized temporal patterns of seizure occurrence were assessed via circadian circular statistics and via Fano factor analysis. Results Over a median duration of 438 days, 10 patients recorded a median 18.8 h/day, totaling 71 984 h of real‐world sqEEG data. Adherence and acceptability remained high throughout the study. Although 754 sqEEG seizures were recorded across patients, more than half (52%) of these were not reported in the patient diary. Of the 140 (27%) diary reports not associated with an identifiable sqEEG seizure, the majority (68%) were reported as seizures with preserved awareness. The sqEEG to diary F1 agreement score was highly variable, ranging from .06 to .97. Patient‐specific patterns of circadian seizure occurrence and seizure clustering were found, including several relevant discrepancies between sqEEG and diary. Significance We demonstrate feasibility and high acceptability of ultra‐long‐term (months–years) sqEEG monitoring. These systems help provide real‐world, more objective seizure counting compared to patient diaries. It is possible to objectively monitor individual temporal fluctuations of seizure occurrence.

Abstract Despite the growing interest in network-guided neuromodulation for epilepsy, uncertainty about the safety and long-term efficacy of thalamocortical stimulation persists. Our evaluation focused on the use of a four-lead open-loop implantable pulse generator for thalamocortical network neuromodulation. We retrospectively reviewed seven patients with diverse seizure networks—poorly localized, regional, or multifocal—undergoing thalamocortical neuromodulation. Employing a four-lead system, electrodes targeted both thalamic and cortical seizure network nodes. We assessed seizure severity, life satisfaction and sleep quality on a 10-point scale, and seizure frequency was assessed via telephone interviews and chart review. Outcomes were assessed by the Wilcoxon sign-rank test at the 0.05 significance level. Seven patients with a median age at implant of 22 years (range 14–42 years) had a median disabling seizure reduction of 93% (range 50–100%, P = 0.0156), with 100% responder rate (≥50% reduction in seizure frequency) after a median of 17 months post-implantation (range 13–60). The median improvement in seizure severity was 3.5 out of 10 points (P = 0.0312), life satisfaction 4.5 points (P = 0.0312) and quality of sleep 3 points (P = 0.062). No perioperative complications occurred. Transient seizure exacerbations (n = 2) and stimulation-related sensory/motor side-effects (n = 2) quickly resolved with parameter adjustments. One patient required surgical revision due to delayed infection. Six patients had permanent electrode placement refined by intracranial EEG trial stimulation; five patients had >90% reduction in seizure frequency during trial stimulation. Thalamocortical network neuromodulation using a four-lead open-loop system is safe and associated with significant improvements in seizure control and patient quality of life. Trial stimulation during intracranial EEG shows promise for enhancing seizure network engagement and parameter optimization but requires further study. Prospective controlled trials are needed to further characterize and validate the efficacy and side-effect profile of thalamocortical network neuromodulation for epilepsy.

Abstract Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures that often originate within limbic networks involving amygdala and hippocampus. The limbic network is involved in crucial physiologic functions involving memory, emotion and sleep. Temporal lobe epilepsy is frequently drug-resistant, and people often experience comorbidities related to memory, mood and sleep. Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is an established therapy for temporal lobe epilepsy. However, the optimal stimulation parameters and their impact on memory, mood and sleep comorbidities remain unclear. We used an investigational brain sensing-stimulation implanted device to accurately track seizures, interictal epileptiform spikes (IES), and memory, mood and sleep comorbidities in five ambulatory subjects. Wireless streaming of limbic network local field potentials (LFPs) and subject behaviour were captured on a mobile device integrated with a cloud environment. Automated algorithms applied to the continuous LFPs were used to accurately cataloged seizures, IES and sleep-wake brain state. Memory and mood assessments were remotely administered to densely sample cognitive and behavioural response during ANT-DBS in ambulatory subjects living in their natural home environment. We evaluated the effect of continuous low-frequency and duty cycle high-frequency ANT-DBS on epileptiform activity and memory, mood and sleep comorbidities. Both low-frequency and high-frequency ANT-DBS paradigms reduced seizures. However, continuous low-frequency ANT-DBS showed greater reductions in IES, electrographic seizures and better sleep and memory outcomes. These results highlight the potential of synchronized brain sensing and dense behavioural tracking during ANT-DBS for optimizing neuromodulation therapy. While studies with larger patient numbers are needed to validate the benefits of low-frequency ANT-DBS, these findings are potentially translatable to individuals currently implanted with ANT-DBS systems.

Summary: The lack of reliable seizure detection remains a significant challenge for epilepsy care. A clinical deep brain stimulation (DBS) system provides constrained ambulatory brain recordings; however, limited data exist on the use of DBS recordings for seizure detection and lateralization. We present the case of an 18-year-old patient with drug-resistant focal epilepsy, who had seizure detection and lateralization by DBS recordings. Prior stereotactic-EEG, including a thalamus lead, identified independent left orbitofrontal and mesial temporal onset seizures. Notably, low-frequency thalamic ictal power was significantly elevated relative to baseline awake and sleep states. The patient was subsequently implanted with an anterior nucleus of the thalamus DBS system. Postimplantation, low-frequency power-in-band (5.3–10.3 Hz) recordings were initiated. Nursing staff identified four typical clinical seizures during the inpatient DBS recording period. Thalamic DBS trends contained relative peaks that were coincident with each nurse-reported seizure. Peri-ictal power was uniformly maximal ipsilateral to the seizure network. This case demonstrates the feasibility of seizure detection and lateralization by a thalamic DBS system for some individuals, and suggests DBS sensing parameter selection may be guided by thalamic stereotactic EEG. Further research is necessary to assess the generalizability of DBS seizure detection across individuals and diverse seizure networks.

Abstract Objective Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri‐ictal periods in performing a feature‐based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. Methods This study assesses the signal quality of wearable data during peri‐ictal phases of generalized tonic–clonic and focal to bilateral tonic–clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri‐ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. Results We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre‐ and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Significance Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist‐worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

Abstract Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer’s disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10–20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer’s disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer’s Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59–0.91) and Alzheimer’s disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer’s subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

AbstractStudies 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.