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Seizure Cycles under Pharmacotherapy. Friedrichs-Maeder C, Proix T, Tcheng TK, Skarpaas T, Rao VR, Baud MO. Ann Neurol. 2024;95(4):743–753 Objective: This study was undertaken to determine the effects of antiseizure medications (ASMs) on multidien (multiday) cycles of interictal epileptiform activity (IEA) and seizures and evaluate their potential clinical significance. Methods: We retrospectively analyzed up to 10 years of data from 88 of the 256 total adults with pharmacoresistant focal epilepsy who participated in the clinical trials of the RNS System, an intracranial device that keeps records of IEA counts. Following adjunctive ASM trials, we evaluated changes over months in (1) rates of self-reported disabling seizures and (2) multidien IEA cycle strength (spectral power for periodicity between 4 and 40 days). We used a survival analysis and the receiver operating characteristics to assess changes in IEA as a predictor of seizure control. Results: Among 56 (33.3%) of the 168 adjunctive ASM trials suitable for analysis, ASM introduction was followed by an average 50% to 70% decrease in multidien IEA cycle strength and a concomitant 50% to 70% decrease in relative seizure rate for up to 12 months. Individuals with a ≥50% decrease in IEA cycle strength in the first 3 months of an ASM trial had a higher probability of remaining seizure responders (≥50% seizure rate reduction, p < 10−7) or super-responders (≥90%, p < 10−8) over the next 12 months. Interpretation: In this large cohort, a decrease in multidien IEA cycle strength following initiation of an adjunctive ASM correlated with seizure control for up to 12 months, suggesting that fluctuations in IEA mirror "disease activity" in pharmacoresistant focal epilepsy and may have clinical utility as a biomarker to predict treatment response. [ABSTRACT FROM AUTHOR]

Epilepsy, characterized by recurrent seizures, is influenced by biological rhythms, such as circadian, seasonal, and menstrual cycles. These rhythms affect the frequency, severity, and timing of seizures, although the precise mechanisms underlying these associations remain unclear. This review examines the role of biological clocks, particularly the core circadian genes Bmal1, Clock, Per, and Cry, in regulating neuronal excitability and epilepsy susceptibility. We explore how the sleep-wake cycle, particularly non-rapid eye movement sleep, increases the risk of seizures, and discuss the circadian modulation of neurotransmitters like gamma-aminobutyric acid and glutamate. We explore clinical implications, including chronotherapy which refers to the practice of timing medical treatments to align with the body's natural biological rhythms, such as the circadian rhythm. Chronotherapy aligns anti-seizure medication administration with biological rhythms. We also discuss rhythm-based neuromodulation strategies, such as adaptive deep brain stimulation, which may dynamically change stimulation in response to predicted seizures in patients, provide additional therapeutic options. This review emphasizes the potential of integrating biological rhythm analysis into personalized epilepsy management, offering novel approaches to optimize treatment and improve patient outcomes. Future research should focus on understanding individual variability in seizure rhythms and harnessing technological innovations to enhance seizure prediction, precision treatment, and long-term management. [ABSTRACT FROM AUTHOR]

Copyright of Clinical Epileptology / Zertifizierte Fortbildung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Clinical Epileptology / Zertifizierte Fortbildung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Clinical Epileptology / Zertifizierte Fortbildung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Non‐invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure‐day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. Methods: Seventy‐eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non‐rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high‐frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo‐prospective seizure‐day forecasting, with forecasting horizons of 16‐ and 24‐h to include both diurnal and nocturnal seizures (24‐h) or diurnal seizures only (16‐h). The algorithm's performance was assessed using a nested leave‐one‐patient‐out cross‐validation approach. Results: Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16‐ and 24‐h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16‐h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24‐h horizon. Significance: Smart shirt‐based nocturnal sleep analysis holds promise as a non‐invasive approach for seizure‐day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long‐term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. Plain Language Summary: Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach. [ABSTRACT FROM AUTHOR]

Bioelectronic Medicine (BEM), which uses implantable electronic medical devices to interface with electrically active tissues, aspires to revolutionize the way we understand and fight disease. By leveraging knowledge from microelectronics, materials science, information technology, neuroscience and medicine, BEM promises to offer novel solutions that address unmet clinical needs and change the concept of therapeutics. This perspective communicates our vision for the future of BEM and presents the necessary steps that need to be taken and the challenges that need to be faced before this new technology can flourish. [ABSTRACT FROM AUTHOR]

Intrinsic biological clocks drive the circadian rhythm, which coordinates the physiological and pathophysiological processes in the body. Recently, a bidirectional relationship between circadian rhythms and several neurological diseases has been reported. Neurological diseases can lead to the disruption of circadian homeostasis, thereby increasing disease severity. Therefore, optimizing the current treatments through circadian-based approaches, including adjusted dosing, changing lifestyle, and targeted interventions, offer a promising opportunity for better clinical outcomes and precision medicine. In this review, we provide detailed implications of the circadian rhythm in neurological diseases through bench-to-bedside approaches. Furthermore, based on the unsatisfactory clinical outcomes, we critically discuss the potential of circadian-based interventions, which may encourage more studies in this discipline, with the hope of improving treatment efficacy in neurological diseases. [ABSTRACT FROM AUTHOR]

Introduction: Neurologic circadian influences, including sleep/wake transitions, processes (e.g., hormonal variation), and behavioral patterns (e.g., consumption of food and oral medications), may affect seizure patterns. Specific circadian patterns of seizures have been reported depending on type, onset location, and severity; however, data on patterns for patients with seizure clusters and effectiveness of rescue therapy by time of day are limited. Methods: We conducted post hoc analyses using patient diary data from the phase 3 safety study of diazepam nasal spray, which is indicated for acute treatment of seizure clusters in patients with epilepsy aged =6 years. Patients were administered age- and weight-based doses; second doses could be administered if needed to control a seizure cluster. We assessed clock timing of seizure-cluster onset along with second-dose use as a proxy for effectiveness. Treatment-emergent adverse events were recorded. Results: Seizure-cluster onset was observed to be generally highest during mornings and late evenings and lowest in the early evening and middle of the night. Second-dose use was not consistently associated with a specific time of day. The safety profile was consistent with that expected from previous studies of diazepam nasal spray. Conclusion: These results suggest that diazepam nasal spray can be effectively administered at any time of day. [ABSTRACT FROM AUTHOR]

This study aims to explore the relationship between the circadian rhythms of critically ill patients and the incidence of Status Epilepticus (SE), and to develop a predictive model based on circadian rhythm indicators and clinical factors. We conducted a diurnal rhythm analysis of vital sign data from 4413 patients, discovering significant differences in the circadian rhythms of body temperature, blood oxygen saturation, and heart rate between the SE and non-SE groups, which were correlated with the incidence of SE. We also employed various machine learning algorithms to identify the ten most significant variables and developed a predictive model with strong performance and clinical applicability. Our research provides a new perspective and methodology for the study of biological rhythms in critically ill patients, offering new evidence and tools for the prevention and treatment of SE. Our findings are consistent or similar to some in the literature, while differing from or supplementing others. We observed significant differences in the vital signs of epileptic patients at different times of the day across various diagnostic time groups, reflecting the regulatory effects of circadian rhythms. We suggest heightened monitoring and intervention of vital signs in critically ill patients, especially during late night to early morning hours, to reduce the risk of SE and provide more personalized treatment plans. [ABSTRACT FROM AUTHOR]

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. Author summary: Circadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) and seizures in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in people with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications. [ABSTRACT FROM AUTHOR]

Objective: Rescue benzodiazepine medication can be used to treat seizure clusters, which are intermittent, stereotypic episodes of frequent seizure activity that are distinct from a patient's usual seizure pattern. The NeuroPace RNS® System is a device that detects abnormal electrographic activity through intracranial electrodes and administers electrical stimulation to control seizures. Reductions in electrographic activity over days to weeks have been associated with the longer‐term efficacy of daily antiseizure medications (ASMs). In this pilot study, electrographic activity over hours to days was examined to assess the impact of a single dose of a proven rescue therapy (diazepam nasal spray) with a rapid onset of action. Methods: Adult volunteers (>18 years old) with clinically indicated RNS (stable settings and ASM usage) received a weight‐based dose of diazepam nasal spray in the absence of a clinical seizure. Descriptive statistics for a number of detections and a sum of durations of detections at 10‐min, hourly, and 24‐h intervals during the 7‐day (predose) baseline period were calculated. Post‐dose detections at each time interval were compared with the respective baseline‐detection intervals using a 1 SD threshold. The number of long episodes that occurred after dosing also were compared with the baseline. Results: Five participants were enrolled, and four completed the study; the excluded participant had recurrent seizures during the study. There were no consistent changes (difference >1 SD) in detections between post‐dose and mean baseline values. Although variability was high (1 SD was often near or exceeded the mean), three participants showed possible trends for reductions in one or more electrographic variables following treatment. Significance: RNS‐assessed electrographic detections and durations were not shown to be sensitive measures of short‐term effects associated with a single dose of rescue medication in this small group of participants. The variability of detections may have masked a measurable drug effect. Plain Language Summary: Rescue drugs are used to treat seizure clusters. Responsive neurostimulation (RNS) devices detect and record epilepsy brain waves and then send a pulse to help stop seizures. This pilot study looked at whether one dose of a rescue treatment changes brain activity detected by RNS. There was a very wide range of detections, which made it difficult to see if or how the drug changed brain activity. New studies should look at other types of brain activity, multiple doses, and larger patient groups. [ABSTRACT FROM AUTHOR]

In this third installment of our four-part historical series, we evaluate contributions that shaped our understanding of heat and cold stress during occupational and athletic pursuits. Our first topic concerns how we tolerate, and sometimes fail to tolerate, exercise-heat stress. By 1900, physical activity with clothing- and climate-induced evaporative impediments led to an extraordinarily high incidence of heat stroke within the military. Fortunately, deep-body temperatures > 40 °C were not always fatal. Thirty years later, water immersion and patient treatments mimicking sweat evaporation were found to be effective, with the adage of cool first, transport later being adopted. We gradually acquired an understanding of thermoeffector function during heat storage, and learned about challenges to other regulatory mechanisms. In our second topic, we explore cold tolerance and intolerance. By the 1930s, hypothermia was known to reduce cutaneous circulation, particularly at the extremities, conserving body heat. Cold-induced vasodilatation hindered heat conservation, but it was protective. Increased metabolic heat production followed, driven by shivering and non-shivering thermogenesis, even during exercise and work. Physical endurance and shivering could both be compromised by hypoglycaemia. Later, treatments for hypothermia and cold injuries were refined, and the thermal after-drop was explained. In our final topic, we critique the numerous indices developed in attempts to numerically rate hot and cold stresses. The criteria for an effective thermal stress index were established by the 1930s. However, few indices satisfied those requirements, either then or now, and the surviving indices, including the unvalidated Wet-Bulb Globe-Thermometer index, do not fully predict thermal strain. [ABSTRACT FROM AUTHOR]

In a one-way design with unknown and unequal error variances, we are interested in testing the null hypothesis H 0 : μ 1 = μ 2 = ⋯ = μ k against the ordered alternative H 1 : μ 1 ≤ μ 2 ≤ ⋯ ≤ μ k (with at least one strict inequality). We propose the likelihood ratio test (LRT) as well as two Min-T tests (Min-T and AMin-T) and study their asymptotic behaviour. For better finite sample properties, a parametric bootstrap procedure is used. Asymptotic accuracy of the parametric bootstrap is established. Simulation results show that all the tests control the nominal level in case of small, moderate, and highly unbalanced sample sizes. This even holds for combinations with large variances. However, the asymptotic likelihood ratio test (ALRT) achieves the nominal size only for large samples and k = 3 . Power comparisons between LRT and Min-T test show that the LRT has more power than Min-T. However, the Min-T test is easier to use as it does not require evaluation of MLEs. Similar observations are made while comparing ALRT and AMin-T test. The robustness of these tests under departure from normality is also investigated by considering five non-normal distributions (t-distribution, Laplace, exponential, Weibull, and lognormal). When high variances are combined with small or highly unbalanced sample sizes, the tests are observed to be robust only for symmetric distributions. If variances of groups are equal and sample sizes of each group are at least 10, the proposed tests perform satisfactorily for symmetric and skewed distributions. R packages are developed for all tests and a practical example is provided to illustrate their application. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years‐long electroencephalographic (EEG) recordings, we previously found that patient‐reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal–ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. Methods: We used retrospective long‐term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24‐h horizon. Results: With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) =.70 and.69 and median Brier skill score (BSS) =.07 and.08. In direct comparison, individualized models had similar median performance (AUC =.67, BSS =.08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. Significance: Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient‐reported vs. electrographic) or IEA (icEEG vs. sqEEG). [ABSTRACT FROM AUTHOR]

Direct cortical stimulation has been applied in epilepsy for nearly a century and has experienced a renaissance, given unprecedented opportunities to probe, excite, and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug‐resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, and is further complicated by epilepsy being characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 Conference (International Conference on Technology and Analysis for Seizures), we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on the usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimizing use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long‐term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive electroencephalographic recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker‐informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities. [ABSTRACT FROM AUTHOR]

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies. [ABSTRACT FROM AUTHOR]

Objective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient‐specific seizure forecasting is possible using remote, minimally invasive ultra‐long‐term subcutaneous EEG. Methods: We analyzed a two‐center cohort of ultra‐long‐term subcutaneous EEG recordings, including six patients with drug‐resistant focal epilepsy monitored for 46–230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject‐specific long short‐term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of.65–.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient‐specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof‐of‐principle for the possibility of subject‐specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra‐long‐term at‐home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG. [ABSTRACT FROM AUTHOR]

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal‐processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)–enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data‐sharing initiatives. [ABSTRACT FROM AUTHOR]

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures—ICTALS 2022—convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long‐suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi‐day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large‐scale network disorder yielded novel perspectives on the pre‐ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure‐forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well‐being. [ABSTRACT FROM AUTHOR]

The prevalence of rapid weight loss (RWL) among martial arts athletes including judo is very high. Many applied RWL strategies could be dangerous to health and even lead to death. Therefore, the International Judo Federation (IJF) introduced changes in the weigh-in rules, changing the official weigh-in for the day before the competition. Thus, the purpose of this study was to examine the impact of the new IJF rules on hydration status and weight loss strategies among professional judo athletes. Seventeen elite judo athletes participated in the study. Body mass and hydration status, were analyzed before the competition. Moreover, competition result and practice of RWL survey were collected. All subjects reached their weight category limits for the competition. RWL resulted in body mass changes (p < 0.001, ηp2 = 0.79) and dehydration among participants (urine osmolality > 700 [mOsmol*kg]−1 and urine specific gravity > 1.020 [g*cm3]−1). However, urine osmolality (p > 0.05, ηp2 = 0.18), as well as urine specific gravity (p > 0.05, ηp2 = 0.16), at subsequent time points of measurement revealed no statistical differences. The prevalence of RWL was 100%, and only 17.6% of the athletes declared that they would compete in a different weight category if the competition would be conducted on the same day of the weigh-in. All judo athletes applied RWL procedures using traditional methods to achieve the required body mass (i.e., increased exercise, reduced fluid, and food intake). Dehydration state was not associated with competitive performance (p > 0.05). [ABSTRACT FROM AUTHOR]

Introduction: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) and responsive neurostimulation (RNS) of the hippocampus are the predominant approaches to brain stimulation for treating mesial temporal lobe epilepsy (MTLE). Both are similarly effective at reducing seizures in drug-resistant patients, but the underlying mechanisms are poorly understood. In rare cases where it is clinically indicated to use RNS and DBS simultaneously, ambulatory electrophysiology from RNS may provide the opportunity to measure the effects of ANT DBS in the putative seizure onset zone and identify biomarkers associated with clinical improvement. Here, one such patient became seizure free, allowing us to identify and compare the changes in hippocampal electrophysiology associated with ANT stimulation and seizure freedom. Methods: Ambulatory electrocorticography and clinical history were retrospectively analyzed for a patient treated with RNS and DBS for MTLE. DBS artifacts were used to identify ANT stimulation periods on RNS recordings and measure peri-stimulus electrographic changes. Clinical history was used to determine the chronic electrographic changes associated with seizure freedom. Results: ANT stimulation acutely suppressed hippocampal gamma (25–90Hz) power, with minimal theta (4–8Hz) suppression and without clear effects on seizure frequency. Eventually, the patient became seizure free alongside the emergence of chronic gamma increase and theta suppression, which started at the same time as clobazam was introduced. Both seizure freedom and the associated electrophysiology persisted after inadvertent DBS discontinuation, further implicating the clobazam relationship. Unexpectedly, RNS detections and long episodes increased, although they were not considered to be electrographic seizures, and the patient remained clinically seizure free. Conclusion: ANT stimulation and seizure freedom were associated with distinct, dissimilar spectral changes in RNS-derived electrophysiology. The time course of these changes supported a new medication as the most likely cause of clinical improvement. Broadly, this work showcases the use of RNS recordings to interpret the effects of multimodal therapy. Specifically, it lends additional credence to hippocampal theta suppression as a biomarker previously associated with seizure reduction in RNS patients. [ABSTRACT FROM AUTHOR]

The aging effects on circadian rhythms have diverse implications including changes in the pattern of rhythmic expressions, such as a wide fragmentation of the rhythm of rest-activity and decrease in amplitude of activity regulated by the suprachiasmatic nucleus (SCN). The study of blue light on biological aspects has received great current interest due, among some aspects, to its positive effects on psychiatric disorders in humans. This study aims to evaluate the effect of blue light therapy on the SCN functional aspects, through the evaluation of the rest-activity rhythm, in aging rats. For this, 33 sixteen-months-old male Wistar rats underwent continuous records of locomotor activity and were exposed to periods of 6 hours of blue light during the first half of the light phase (Zeitgeber times 0–6) for 14 days. After this, the rats were maintained at 12h:12h light:dark cycle to check the long-term effect of blue light for 14 days. Blue light repeated exposure showed positive effects on the rhythmic variables of locomotor activity in aged rats, particularly the increase in amplitude, elevation of rhythmic robustness, phase advance in acrophase, and greater consolidation of the resting phase. This effect depends on the presence of daily blue light exposure. In conclusion, our results indicate that blue light is a reliable therapy to reduce circadian dysfunctions in aged rats, but other studies assessing how blue light modulates the neural components to modulate this response are still needed. [ABSTRACT FROM AUTHOR]

Objective: Circadian and multidien cycles of seizure occurrence are increasingly discussed as to their biological underpinnings and in the context of seizure forecasting. This study analyzes if patient reported seizures provide valid data on such cyclical occurrence. Methods: We retrospectively studied if circadian cycles derived from patient‐based reporting reflect the objective seizure documentation in 2003 patients undergoing in‐patient video‐EEG monitoring. Results: Only 24.1% of more than 29000 seizures documented were accompanied by patient notifications. There was cyclical underreporting of seizures with a maximum during nighttime, leading to significant deviations in the circadian distribution of seizures. Significant cyclical deviations were found for focal epilepsies originating from both, frontal and temporal lobes, and for different seizure types (in particular, focal unaware and focal to bilateral tonic–clonic seizures). Interpretation: Patient seizure diaries may reflect a cyclical reporting bias rather than the true circadian seizure distributions. Cyclical underreporting of seizures derived from patient‐based reports alone may lead to suboptimal treatment schemes, to an underestimation of seizure‐associated risks, and may pose problems for valid seizure forecasting. This finding strongly supports the use of objective measures to monitor cyclical distributions of seizures and for studies and treatment decisions based thereon. [ABSTRACT FROM AUTHOR]

Infrared neuromodulation (INM) is a promising neuromodulation tool that utilizes pulsed or continuous-wave near-infrared (NIR) laser light to produce an elevation of the background temperature of the neural tissue. The INM-based cortical heating has been proven as an effective modality to induce changes in neuronal activities. In this paper, we investigate the effect of INM-based cortical heating on the characteristics of interictal epileptiform discharges (IEDs) induced by penicillin in anesthetized rats. Cortical heating was conducted using a NIR laser light guided through a needle-like silicon-based waveguide probe. We detected penicillin-induced cortical IEDs from preprocessed micro-electrocorticography (μ ECoG) recordings, then we assessed changes in various temporal and spectral features of IEDs due to INM. Our findings show that the fast cortical heating phase obtained with continuous-wave NIR light is highly associated with a reduction of IED amplitudes, small but significant changes in the negative amplitude of IEDs compared with the baseline, and a proportional increase in the power of frequency bands related to delta/theta (2–8 Hz) and gamma (28–80 Hz) oscillations. Furthermore, a low rate of cortical heating with pulsed NIR illumination has a more inhibitory impact on the sharp negative polarity of IEDs. Our findings do not indicate a clear reduction in the frequency of IEDs in anesthetized rodents. In contrast, 2–4 min of continuous laser illumination leads to a notable increase in IED frequency. This effect of INM could potentially restrict its use in therapeutic applications related to epilepsy. However, the thermal effect of INM on cortical neurons induces changes in other characteristics of IEDs, which could prove beneficial for future applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Managing the progress of drug‐resistant epilepsy patients implanted with the Responsive Neurostimulation (RNS) System requires the manual evaluation of hundreds of hours of intracranial recordings. The generation of these large amounts of data and the scarcity of experts' time for evaluation necessitate the development of automatic tools to detect intracranial electroencephalographic (iEEG) seizure patterns (iESPs) with expert‐level accuracy. We developed an intelligent system for identifying the presence and onset time of iESPs in iEEG recordings from the RNS device. Methods: An iEEG dataset from 24 patients (36 293 recordings) recorded by the RNS System was used for training and evaluating a neural network model (iESPnet). The model was trained to identify the probability of seizure onset at each sample point of the iEEG. The reliability of the net was assessed and compared to baseline methods, including detections made by the device. iESPnet performance was measured using balanced accuracy and the F1 score for iESP detection. The prediction time was assessed via both the error and the mean absolute error. The model was evaluated following a hold‐one‐out strategy, and then validated in a separate cohort of 26 patients from a different medical center. Results: iESPnet detected the presence of an iESP with a mean accuracy value of 90% and an onset time prediction error of approximately 3.4 s. There was no relationship between electrode location and prediction outcome. Model outputs were well calibrated and unbiased by the RNS detections. Validation on a separate cohort further supported iESPnet applicability in real clinical scenarios. Importantly, RNS device detections were found to be less accurate and delayed in nonresponders; therefore, tools to improve the accuracy of seizure detection are critical for increasing therapeutic efficacy. Significance: iESPnet is a reliable and accurate tool with the potential to alleviate the time‐consuming manual inspection of iESPs and facilitate the evaluation of therapeutic response in RNS‐implanted patients. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disorder, affecting patients of all ages, reducing the quality of life, and associated with several comorbidities. Sleep impairment is a frequent condition in patients with epilepsy (PWE), and the relation between sleep and epilepsy has been considered bidirectional, as one can significantly influence the other, and vice versa. The orexin system was described more than 20 years ago and is implicated in several neurobiological functions other than in controlling the sleep–wake cycle. Considering the relation between epilepsy and sleep, and the significant contribution of the orexin system in regulating the sleep–wake cycle, it is conceivable that the orexin system may be affected in PWE. Preclinical studies investigated the impact of the orexin system on epileptogenesis and the effect of orexin antagonism on seizures in animal models. Conversely, clinical studies are few and propose heterogeneous results also considering the different methodological approaches to orexin levels quantification (cerebrospinal‐fluid or blood samples). Because orexin system activity can be modulated by sleep, and considering the sleep impairment documented in PWE, the recently approved dual orexin receptor antagonists (DORAs) have been suggested for treating sleep impairment and insomnia in PWE. Accordingly, sleep improvement can be a therapeutic strategy for reducing seizures and better managing epilepsy. The present review analyzes the preclinical and clinical evidence linking the orexin system to epilepsy, and hypothesizes a model in which the antagonism to the orexin system by DORAs can improve epilepsy by both a direct and a sleep‐mediated (indirect) effect. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm. [ABSTRACT FROM AUTHOR]

Objective: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. Methods: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. Results: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. Significance: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care. [ABSTRACT FROM AUTHOR]

Distinct lines of research in both humans and animals point to a specific role of the hippocampus in both spatial and episodic memory function. The discovery of concept cells in the hippocampus and surrounding medial temporal lobe (MTL) regions suggests that the MTL maps physical and semantic spaces with a similar neural architecture. Here, we studied the emergence of such maps using MTL microwire recordings from 20 patients (9 female, 11 male) navigating a virtual environment featuring salient landmarks with established semantic meaning. We present several key findings. The array of local field potentials in the MTL contains sufficient information for above-chance decoding of subjects' instantaneous location in the environment. Closer examination revealed that as subjects gain experience with the environment the field potentials come to represent both the subjects' locations in virtual space and in high-dimensional semantic space. Similarly, we observe a learning effect on temporal sequence coding. Over time, field potentials come to represent future locations, even after controlling for spatial proximity. This predictive coding of future states, more so than the strength of spatial representations per se, is linked to variability in subjects' navigation performance. Our results thus support the conceptualization of the MTL as a memory space, representing both spatial- and nonspatial information to plan future actions and predict their outcomes. [ABSTRACT FROM AUTHOR]

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high‐frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: Stimulation of the thalamus is gaining favor in the treatment of medically refractory multifocal and generalized epilepsy. Implanted brain stimulators capable of recording ambulatory local field potentials (LFPs) have recently been introduced, but there is little information to guide their use in thalamic stimulation for epilepsy. This study sought to assess the feasibility of chronically recording ambulatory interictal LFP from the thalamus in patients with epilepsy. Methods: In this pilot study, ambulatory LFP was recorded from patients who underwent sensing-enabled deep brain stimulation (DBS, 2 participants) or responsive neurostimulation (RNS, 3 participants) targeting the anterior nucleus of the thalamus (ANT, 2 electrodes), centromedian nucleus (CM, 7 electrodes), or medial pulvinar (PuM, 1 electrode) for multifocal or generalized epilepsy. Time-domain and frequency-domain LFP was investigated for epileptiform discharges, spectral peaks, circadian variation, and peri-ictal patterns. Results: Thalamic interictal discharges were visible on ambulatory recordings from both DBS and RNS. At-home interictal frequency-domain data could be extracted from both devices. Spectral peaks were noted at 10–15 Hz in CM, 6–11 Hz in ANT, and 19–24 Hz in PuM but varied in prominence and were not visible in all electrodes. In CM, 10–15 Hz power exhibited circadian variation and was attenuated by eye opening. Conclusion: Chronic ambulatory recording of thalamic LFP is feasible. Common spectral peaks can be observed but vary between electrodes and across neural states. DBS and RNS devices provide a wealth of complementary data that have the potential to better inform thalamic stimulation for epilepsy. [ABSTRACT FROM AUTHOR]

Mobile health (mHealth) technology is revolutionizing epilepsy care and management by providing personalized, accessible, and effective approaches to monitoring and treating the condition. This special issue of Epilepsia highlights key advancements in mHealth for epilepsy, including home video-EEG monitoring, wearable devices for automated seizure detection, and seizure forecasting. These developments have been accelerated by the COVID-19 pandemic and the acceptance of telemedicine. The use of mHealth devices has shown high patient satisfaction, improved accuracy of seizure diaries, and enhanced quality of life for individuals with epilepsy. These advancements have the potential to improve treatment strategies and deepen our understanding of the condition. [Extracted from the article]

Contribution of Somatic Ras/Raf/Mitogen-Activated Protein Kinase Variants in the Hippocampus in Drug-Resistant Mesial Temporal Lobe Epilepsy Khoshkhoo S, Wang Y, Chahine Y, Erson-Omay EZ, Robert SM, Kiziltug E, Damisah EC, Nelson-Williams C, Zhu G, Kong W, Huang AY, Stronge E, Phillips HW, Chhouk BH, Bizzotto S, Chen MH, Adikari TN, Ye Z, Witkowski T, Lai D, Lee N, Lokan J, Scheffer IE, Berkovic SF, Haider S, Hildebrand MS, Yang E, Gunel M, Lifton RP, Richardson RM, Blümcke I, Alexandrescu S, Huttner A, Heinzen EL, Zhu J, Poduri A, DeLanerolle N, Spencer DD, Lee EA, Walsh CA, Kahle KT. JAMA Neurol. 2023;80(6):578-587. doi:10.1001/jamaneurol.2023.0473 Importance: Mesial temporal lobe epilepsy (MTLE) is the most common focal epilepsy subtype and is often refractory to antiseizure medications. While most patients with MTLE do not have pathogenic germline genetic variants, the contribution of postzygotic (i.e., somatic) variants in the brain is unknown. Objective: To test the association between pathogenic somatic variants in the hippocampus and MTLE. Design, Setting, and Participants: This case-control genetic association study analyzed the DNA derived from hippocampal tissue of neurosurgically treated patients with MTLE and age-matched and sex-matched neurotypical controls. Participants treated at level 4 epilepsy centers were enrolled from 1988 through 2019, and clinical data were collected retrospectively. Whole-exome and gene-panel sequencing (each genomic region sequenced more than 500 times on average) were used to identify candidate pathogenic somatic variants. A subset of novel variants was functionally evaluated using cellular and molecular assays. Patients with nonlesional and lesional (mesial temporal sclerosis, focal cortical dysplasia, and low-grade epilepsy–associated tumors) drug-resistant MTLE who underwent anterior medial temporal lobectomy were eligible. All patients with available frozen tissue and appropriate consent were included. Control brain tissue was obtained from neurotypical donors at brain banks. Data were analyzed from June 2020 to August 2022. Exposures: Drug-resistant MTLE. Main Outcomes and Measures: Presence and abundance of pathogenic somatic variants in the hippocampus vs the unaffected temporal neocortex. Results: Of 105 included patients with MTLE, 53 (50.5%) were female, and the median (IQR) age was 32 (26-44) years; of 30 neurotypical controls, 11 (36.7%) were female, and the median (IQR) age was 37 (18-53) years. Eleven pathogenic somatic variants enriched in the hippocampus relative to the unaffected temporal neocortex (median [IQR] variant allele frequency, 1.92 [1.5-2.7] vs 0.3 [0-0.9]; P =.01) were detected in patients with MTLE but not in controls. Ten of these variants were in PTPN11, SOS1, KRAS, BRAF, and NF1, all predicted to constitutively activate Ras/Raf/mitogen-activated protein kinase (MAPK) signaling. Immunohistochemical studies of variant-positive hippocampal tissue demonstrated increased Erk1/2 phosphorylation, indicative of Ras/Raf/MAPK activation, predominantly in glial cells. Molecular assays showed abnormal liquid-liquid phase separation for the PTPN11 variants as a possible dominant gain-of-function mechanism. Conclusions and Relevance: Hippocampal somatic variants, particularly those activating Ras/Raf/MAPK signaling, may contribute to the pathogenesis of sporadic, drug-resistant MTLE. These findings may provide a novel genetic mechanism and highlight new therapeutic targets for this common indication for epilepsy surgery. [ABSTRACT FROM AUTHOR]

Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease. [ABSTRACT FROM AUTHOR]

People with diabetes can wear a device that measures blood glucose and delivers just the amount of insulin needed to return the glucose level to within bounds. Currently, people with epilepsy do not have access to an equivalent wearable device that measures a systemic indicator of an impending seizure and delivers a rapidly acting medication or other intervention (e.g., an electrical stimulus) to terminate or prevent a seizure. Given that seizure susceptibility is reliably increased in systemic inflammatory states, we propose a novel closed‐loop device where release of a fast‐acting therapy is governed by sensors that quantify the magnitude of systemic inflammation. Here, we review the evidence that patients with epilepsy have raised levels of systemic indicators of inflammation than controls, and that some anti‐inflammatory drugs have reduced seizure occurrence in animals and humans. We then consider the options of what might be incorporated into a responsive anti‐seizure system. [ABSTRACT FROM AUTHOR]

Background: Local field potentials (LFPs) represent the summation of periodic (oscillations) and aperiodic (fractal) signals. Although previous studies showed changes in beta band oscillations and burst characteristics of the subthalamic nucleus (STN) in Parkinson's disease (PD), how aperiodic activity in the STN is related to PD pathophysiology is unknown. Objectives: The study aimed to characterize the long‐term effects of STN–deep brain stimulation (DBS) and dopaminergic medications on aperiodic activities and beta bursts. Methods: A total of 10 patients with PD participated in this longitudinal study. Simultaneous bilateral STN‐LFP recordings were conducted in six separate visits during a period of 18 months using the Activa PC + S device in the off and on dopaminergic medication states. We used irregular‐resampling auto‐spectral analysis to separate oscillations and aperiodic components (exponent and offset) in the power spectrum of STN‐LFP signals in beta band. Results: Our results revealed a systematic increase in both the exponent and the offset of the aperiodic spectrum over 18 months following the DBS implantation, independent of the dopaminergic medication state of patients with PD. In contrast, beta burst durations and amplitudes were stable over time and were suppressed by dopaminergic medications. Conclusions: These findings indicate that oscillations and aperiodic activities reflect at least partially distinct yet complementary neural mechanisms, which should be considered in the design of robust biomarkers to optimize adaptive DBS. Given the link between increased gamma‐aminobutyric acidergic (GABAergic) transmission and higher aperiodic activity, our findings suggest that long‐term STN‐DBS may relate to increased inhibition in the basal ganglia. © 2022 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]