Circadian rhythm and epilepsy

Small Differences and Big Changes: The Many Variables of MicroRNA Expression and Function in the Brain

MicroRNAs are emerging as crucial regulators within the complex, dynamic environment of the synapse, and they offer a promising new avenue for the treatment of neurological disease. These small noncoding RNAs modify gene expression in several ways, including posttranscriptional modulation via binding to complementary and semicomplementary sites on target mRNAs. This rapid, finely tuned regulation of gene expression is essential to meet the dynamic demands of the synapse. Here, we provide a detailed review of the multifaceted world of synaptic microRNA regulation. We discuss the many mechanisms by which microRNAs regulate gene expression at the synapse, particularly in the context of neuronal plasticity. We also describe the various factors, such as age, sex, and neurological disease, that can influence microRNA expression and activity in neurons. In summary, microRNAs play a crucial role in the intricate and quickly changing functional requirements of the synapse, and context is essential in the study of microRNAs and their potential therapeutic applications.

Dysregulation of the Suprachiasmatic Nucleus Disturbs the Circadian Rhythm and Aggravates Epileptic Seizures by Inducing Hippocampal GABAergic Dysfunction in C57BL/6 Mice

The interplay between circadian rhythms and epilepsy has gained increasing attention. The suprachiasmatic nucleus (SCN), which acts as the master circadian pacemaker, regulates physiological and behavioral rhythms through its complex neural networks. However, the exact role of the SCN and its Bmal1 gene in the development of epilepsy remains unclear. In this study, we utilized a lithium-pilocarpine model to induce epilepsy in mice and simulated circadian disturbances by creating lesions in the SCN and specifically knocking out the Bmal1 gene in the SCN neurons. We observed that the pilocarpine-induced epileptic mice experienced increased daytime seizure frequency, irregular oscillations in core body temperature, and circadian gene alterations in both the SCN and the hippocampus. Additionally, there was enhanced activation of GABAergic projections from the SCN to the hippocampus. Notably, SCN lesions intensified seizure activity, concomitant with hippocampal neuronal damage and GABAergic signaling impairment. Further analyses using the Gene Expression Omnibus database and gene set enrichment analysis indicated reduced Bmal1 expression in patients with medial temporal lobe epilepsy, potentially affecting GABA receptor pathways. Targeted deletion of Bmal1 in SCN neurons exacerbated seizures and pathology in epilepsy, as well as diminished hippocampal GABAergic efficacy. These results underscore the crucial role of the SCN in modulating circadian rhythms and GABAergic function in the hippocampus, aggravating the severity of seizures. This study provides significant insights into how circadian rhythm disturbances can influence neuronal dysfunction and epilepsy, highlighting the therapeutic potential of targeting SCN and the Bmal1 gene within it in epilepsy management.

Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.

Rapid eye movement sleep and epilepsy: exploring interactions and therapeutic prospects

While research interest in the relationship between sleep and epilepsy is growing, it primarily centres on the effects of non-rapid eye movement (NREM) sleep in favouring seizures. Nonetheless, a noteworthy aspect is the observation that, in the lives of patients with epilepsy, REM sleep represents the moment with the least epileptic activity and the lowest probability of having a seizure. Studies demonstrate a suppressive effect of phasic REM sleep on interictal epileptiform discharges, potentially offering insights into epilepsy localisation and management. Furthermore, epilepsy impacts REM sleep, with successful treatment correlating with improved REM sleep quality. Novel therapeutic strategies aim to harness REM's anti-epileptic effects, including pharmacological approaches targeting orexinergic systems and neuromodulation techniques promoting cortical desynchronisation. These findings underscore the intricate relationship between REM sleep and epilepsy, highlighting avenues for further research and therapeutic innovation in epilepsy management.

The influence of biological rhythms on the initial onset of status epilepticus in critically ill inpatients and the study of its predictive Model

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.

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients' quality of life, as timely intervention can mitigate the impact of seizures. In this research field, it is critical to identify the preictal interval, the transition from regular brain activity to a seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies for prediction, few have been clinically applicable. Recent studies have underlined the dynamic nature of EEG data, characterised by data changes with time, known as concept drifts, highlighting the need for automated methods to detect and adapt to these changes. In this study, we investigate the effectiveness of automatic concept drift adaptation methods in seizure prediction. Three patient-specific seizure prediction approaches with a 10-minute prediction horizon are compared: a seizure prediction algorithm incorporating a window adjustment method by optimising performance with Support Vector Machines (Backwards-Landmark Window), a seizure prediction algorithm incorporating a data-batch (seizures) selection method using a logistic regression (Seizure-batch Regression), and a seizure prediction algorithm with a dynamic integration of classifiers (Dynamic Weighted Ensemble). These methods incorporate a retraining process after each seizure and use a combination of univariate linear features and SVM classifiers. The Firing Power was used as a post-processing technique to generate alarms before seizures. These methodologies were compared with a control approach based on the typical machine learning pipeline, considering a group of 37 patients with Temporal Lobe Epilepsy from the EPILEPSIAE database. The best-performing approach (Backwards-Landmark Window) achieved results of 0.75 ± 0.33 for sensitivity and 1.03 ± 1.00 for false positive rate per hour. This new strategy performed above chance for 89% of patients with the surrogate predictor, whereas the control approach only validated 46%.

Microglia in Microbiota-Gut-Brain Axis: A Hub in Epilepsy

There is growing concern about the role of the microbiota-gut-brain axis in neurological illnesses, and it makes sense to consider microglia as a critical component of this axis in the context of epilepsy. Microglia, which reside in the central nervous system, are dynamic guardians that monitor brain homeostasis. Microglia receive information from the gut microbiota and function as hubs that may be involved in triggering epileptic seizures. Vagus nerve bridges the communication in the axis. Essential axis signaling molecules, such as gamma-aminobutyric acid, 5-hydroxytryptamin, and short-chain fatty acids, are currently under investigation for their participation in drug-resistant epilepsy (DRE). In this review, we explain how vagus nerve connects the gut microbiota to microglia in the brain and discuss the emerging concepts derived from this interaction. Understanding microbiota-gut-brain axis in epilepsy brings hope for DRE therapies. Future treatments can focus on the modulatory effect of the axis and target microglia in solving DRE.

Ultradian rhythms in accelerometric and autonomic data vary based on seizure occurrence in paediatric epilepsy patients

Ultradian rhythms are physiological oscillations that resonate with period lengths shorter than 24 hours. This study examined the expression of ultradian rhythms in patients with epilepsy, a disease defined by an enduring seizure risk that may vary cyclically. Using a wearable device, we recorded heart rate, body temperature, electrodermal activity and limb accelerometry in patients admitted to the paediatric epilepsy monitoring unit. In our case-control design, we included recordings from 29 patients with tonic-clonic seizures and 29 non-seizing controls. We spectrally decomposed each signal to identify cycle lengths of interest and compared average spectral power- and period-related markers between groups. Additionally, we related seizure occurrence to the phase of ultradian rhythm in patients with recorded seizures. We observed prominent 2- and 4-hour-long ultradian rhythms of accelerometry, as well as 4-hour-long oscillations in heart rate. Patients with seizures displayed a higher peak power in the 2-hour accelerometry rhythm (U = 287, P = 0.038) and a period-lengthened 4-hour heart rate rhythm (U = 291.5, P = 0.037). Those that seized also displayed greater mean rhythmic electrodermal activity (U = 261; P = 0.013). Most seizures occurred during the falling-to-trough quarter phase of accelerometric rhythms (13 out of 27, χ2 = 8.41, P = 0.038). Fluctuations in seizure risk or the occurrence of seizures may interrelate with ultradian rhythms of movement and autonomic function. Longitudinal assessments of ultradian patterns in larger patient samples may enable us to understand how such rhythms may improve the temporal precision of seizure forecasting models.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Analysis of Plasma-Derived Exosomal MicroRNAs as Potential Biomarkers for Canine Idiopathic Epilepsy

Epilepsy is one of the most prevalent complex neurological diseases in both the canine and human species, with the idiopathic form as its most common diagnosis. MicroRNAs (miRNAs) are small, noncoding RNA molecules that play a role in gene regulation processes and appear to be a promising biological target for convulsion control. These molecules have been reported as constituents of the internal content of exosomes, which are small extracellular vesicles released by cells. In this study, exosome samples were isolated from the plasma of 23 dogs, including 9 dogs with epilepsy responsive to treatment, 6 dogs with drug-resistant epilepsy, and 8 control dogs. Plasma exosomes were then characterized by electron transmission microscopy, nanoparticle tracking analysis, and dot blotting. Afterwards, the microRNA-enriched RNA content of exosomes was isolated, and miRNA quantification was performed by quantitative real-time PCR. Seven circulating miRNAs that have been previously described in the literature as potential diagnostic or prognostic biomarkers for epilepsy were evaluated. We observed significant differences in miR-16 (p < 0.001), miR-93-5p (p < 0.001), miR-142 (p < 0.001), miR-574 (p < 0.01), and miR-27 (p < 0.05) levels in dogs with refractory epilepsy compared to the control group. In drug-sensitive epileptic dogs, miR-142 (p < 0.01) showed significant differences compared to healthy dogs. Moreover, distinct levels of miR-16 (p < 0.05), miR-93-5p (p < 0.01), miR-132 (p < 0.05), and miR-574 (p < 0.05) were also found between drug-sensitive and drug-resistant epileptic dogs. Our results present plasma-circulating exosomes as an advantageous source of epileptic biomarkers, highlighting the potential of miRNAs as prognostic and diagnostic biomarkers of canine idiopathic epilepsy.

Seizure forecasting: Bifurcations in the long and winding road

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.

Learning to generalize seizure forecasts

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).

Microstructural evaluation of the brain with advanced magnetic resonance imaging techniques in cases of electrical status epilepticus during sleep (ESES)

Background/aim

The cause and treatment of electrical status epilepticus during sleep (ESES), one of the epileptic encephalopathies of childhood, is unclear. The aim of this study was to evaluate possible microstructural abnormalities in the brain using advanced magnetic resonance imaging (MRI) techniques in ESES patients with and without genetic mutations.

Materials and methods

This research comprised 12 ESES patients without structural thalamic lesions (6 with genetic abnormalities and 6 without) and 12 healthy children. Whole-exome sequencing was used for the genetic mutation analysis. Brain MRI data were evaluated using tractus-based spatial statistics, voxel-based morphometry, a local gyrification index, subcortical shape analysis, FreeSurfer volume, and cortical thickness. The data of the groups were compared.

Results

The mean age in the control group was 9.05 ± 1.85 years, whereas that in the ESES group was 9.45 ± 2.72 years. Compared to the control group, the ESES patients showed higher mean thalamus diffusivity (p < 0.05). ESES patients with genetic mutations had lower axial diffusivity in the superior longitudinal fasciculus and gray matter volume in the entorhinal region, accumbens area, caudate, putamen, cerebral white matter, and outer cerebellar areas. The superior and middle temporal cortical thickness increased in the ESES patients.

Conclusion

This study is important in terms of presenting the microstructural evaluation of the brain in ESES patients with advanced MRI analysis methods as well as comparing patients with and without genetic mutations. These findings may be associated with corticostriatal transmission, ictogenesis, epileptogenesis, neuropsychiatric symptoms, cognitive impairment, and cerebellar involvement in ESES. Expanded case-group studies may help to understand the physiology of the corticothalamic circuitry in its etiopathogenesis and develop secondary therapeutic targets for ESES.

Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration

Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

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.

Personalised virtual brain models in epilepsy

Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.

Clocking Epilepsies: A Chronomodulated Strategy-Based Therapy for Rhythmic Seizures

Epilepsy is a neurological disorder characterized by hypersynchronous recurrent neuronal activities and seizures, as well as loss of muscular control and sometimes awareness. Clinically, seizures have been reported to display daily variations. Conversely, circadian misalignment and circadian clock gene variants contribute to epileptic pathogenesis. Elucidation of the genetic bases of epilepsy is of great importance because the genetic variability of the patients affects the efficacies of antiepileptic drugs (AEDs). For this narrative review, we compiled 661 epilepsy-related genes from the PHGKB and OMIM databases and classified them into 3 groups: driver genes, passenger genes, and undetermined genes. We discuss the potential roles of some epilepsy driver genes based on GO and KEGG analyses, the circadian rhythmicity of human and animal epilepsies, and the mutual effects between epilepsy and sleep. We review the advantages and challenges of rodents and zebrafish as animal models for epileptic studies. Finally, we posit chronomodulated strategy-based chronotherapy for rhythmic epilepsies, integrating several lines of investigation for unraveling circadian mechanisms underpinning epileptogenesis, chronopharmacokinetic and chronopharmacodynamic examinations of AEDs, as well as mathematical/computational modeling to help develop time-of-day-specific AED dosing schedules for rhythmic epilepsy patients.

Exploring the Effects of EEG-Based Alpha Neurofeedback on Working Memory Capacity in Healthy Participants

Neurofeedback, an operant conditioning neuromodulation technique, uses information from brain activities in real-time via brain-computer interface (BCI) technology. This technique has been utilized to enhance the cognitive abilities, including working memory performance, of human beings. The aims of this study are to investigate how alpha neurofeedback can improve working memory performance in healthy participants and to explore the underlying neural mechanisms in a working memory task before and after neurofeedback. Thirty-six participants divided into the NFT group and the control group participated in this study. This study was not blinded, and both the participants and the researcher were aware of their group assignments. Increasing power in the alpha EEG band was used as a neurofeedback in the eyes-open condition only in the NFT group. The data were collected before and after neurofeedback while they were performing the N-back memory task (N = 1 and N = 2). Both groups showed improvement in their working memory performance. There was an enhancement in the power of their frontal alpha and beta activities with increased working memory load (i.e., 2-back). The experimental group showed improvements in their functional connections between different brain regions at the theta level. This effect was absent in the control group. Furthermore, brain hemispheric lateralization was found during the N-back task, and there were more intra-hemisphere connections than inter-hemisphere connections of the brain. These results suggest that healthy participants can benefit from neurofeedback and from having their brain networks changed after the training.

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy

Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 ± 21.0 min) and starting time before seizure onset (47.6 ± 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient.

Convulsive behaviors of spontaneous recurrent seizures in a mouse model of extended hippocampal kindling

Growing studies indicate that vigilance states and circadian rhythms can influence seizure occurrence in patients with epilepsy and rodent models of epilepsy. Electrical kindling, referred to brief, repeated stimulations of a limbic structure, is a commonly used model of temporal lobe epilepsy. Kindling via the classic protocol lasting a few weeks does not generally induce spontaneous recurrent seizures (SRS), but extended kindling that applies over the course of a few months has shown to induce SRS in several animal species. Kindling-induced SRS in monkeys and cats were observed mainly during resting wakefulness or sleep, but the behavioral activities associated with SRS in rodent models of extended kindling remain unknown. We aimed to add information in this area using a mouse model of extended hippocampal kindling. Middle-aged C57 black mice experienced ≥80 hippocampal stimulations (delivered twice daily) and then underwent continuous 24 h electroencephalography (EEG)-video monitoring for SRS detection. SRS were recognized by EEG discharges and associated motor seizures. The five stages of the modified Racine scale for mice were used to score motor seizure severities. Seizure-preceding behaviors were assessed in a 3 min period prior to seizure onset and categorized as active and inactive. Three main observations emerged from the present analysis. (1) SRS were found to predominantly manifest as generalized (stage 3-5) motor seizures in association with tail erection or Straub tail. (2) SRS occurrences were not significantly altered by the light on/off cycle. (3) Generalized (stage 3-5) motor seizures were mainly preceded by inactive behaviors such as immobility, standing still, or apparent sleep without evident volitional movement. Considering deeper subcortical structures implicated in genesis of tail erection in other seizure models, we postulate that genesis of generalized motor seizures in extended kindled mice may involve deeper subcortical structures. Our present data together with previous findings from post-status epilepticus models support the notion that ambient cage behaviors are strong influencing factors of SRS occurrence in rodent models of temporal lobe epilepsy.

Chronobiology of epilepsy and sudden unexpected death in epilepsy

Epilepsy is a neurological disease characterized by spontaneous, unprovoked seizures. Various insults render the brain hyperexcitable and susceptible to seizure. Despite there being dozens of preventative anti-seizure medications available, these drugs fail to control seizures in nearly 1 in 3 patients with epilepsy. Over the last century, a large body of evidence has demonstrated that internal and external rhythms can modify seizure phenotypes. Physiologically relevant rhythms with shorter periodic rhythms, such as endogenous circadian rhythms and sleep-state, as well as rhythms with longer periodicity, including multidien rhythms and menses, influence the timing of seizures through poorly understood mechanisms. The purpose of this review is to discuss the findings from both human and animal studies that consider the effect of such biologically relevant rhythms on epilepsy and seizure-associated death. Patients with medically refractory epilepsy are at increased risk of sudden unexpected death in epilepsy (SUDEP). The role that some of these rhythms play in the nocturnal susceptibility to SUDEP will also be discussed. While the involvement of some of these rhythms in epilepsy has been known for over a century, applying the rhythmic nature of such phenomenon to epilepsy management, particularly in mitigating the risk of SUDEP, has been underutilized. As our understanding of the physiological influence on such rhythmic phenomenon improves, and as technology for chronic intracranial epileptiform monitoring becomes more widespread, smaller and less invasive, novel seizure-prediction technologies and time-dependent chronotherapeutic seizure management strategies can be realized.

The interaction between circadian rhythm and epilepsy

Evidence about the interaction between circadian rhythms (CR) and epilepsy has been expanded with the application of advanced detection technology. An adequate understanding of how circadian system and epilepsy interact with each other could contribute to more accurate seizure prediction as well as rapid development of potential treatment timed to specific phases of CR. In this review, we present the reciprocal relationship between CR and epileptic activities from aspects of sleep effect, genetic modulation and brain biochemistry. It has been found that sleep-wake patterns, circadian timing systems and multidien rhythms have essential roles in seizure activities and interictal epileptiform discharge (IED). For instance, specific distribution patterns of seizures and IED have been reported, i.e., lighter non-rapid eye movement (NREM) sleep stage (stage 2) induces seizures while deeper NREM sleep stage (stage 3) activates IEDs. Furthermore, the epilepsy type, seizure type and seizure onset zone can significantly affect the rhythms of seizure occurrence. Apart from the common seizure types, several specific epilepsy syndromes also have a close correlation with sleep-wakefulness patterns. Sleep influences the epilepsy rhythm, and conversely, epilepsy alters the sleep rhythm through multiple pathways. Clock genes accompanied by two feedback loops of regulation have an important role in cortical excitability and seizure occurrence, which may be involved in the mTORopathy. The suprachiasmatic nuclei (SCN) has a rhythm of melatonin and cortisol secretion under the circadian pattern, and then these hormones can feed back into a central oscillator to affect the SCN-dependent rhythms, leading to variable but prominent influence on epilepsy. Furthermore, we discuss the precise predictive algorithms and chronotherapy strategies based on different temporal patterns of seizure occurrence for patients with epilepsy, which may offer a valuable indication for non-invasive closed-loop treatment system. Optimization of the time and dose of antiseizure medications, and resynchronization of disturbed CR (by hormone therapy, light exposure, ketogenic diet, novel small molecules) would be beneficial for epileptic patients in the future. Before formal clinical practice, future large-scale studies are urgently needed to assist prediction and treatment of circadian seizure activities and address unsolved restrictions.

Chronic recording of the vagus nerve to analyze modulations by the light-dark cycle

OBJECTIVE

The vagus nerve is considered to play a key role in the circadian rhythm. Chronic continuous analysis of the vagus nerve activity could contribute to a better understanding of the role of the vagus nerve in light-dark modulations. This paper presents a continuous analysis of spontaneous vagus nerve activity performed in four rats.

APPROACH

We analyzed the vagus electroneurogram (VENG) and electroencephalogram (EEG) over a recording period of 28 days. Spike activity and heart rate estimation were derived from the VENG, and slow-wave activity was derived from the EEG. The presence of repetitive patterns was investigated with periodograms, cosinor fitting, autocorrelation, and statistical tests. The light-dark variations derived from the VENG spikes were compared with EEG slow waves, an established metric in circadian studies.

RESULTS

Our results demonstrate that light-dark variations can be detected in long-term vagus nerve activity monitoring. A recording period of about seven days is required to characterize accurately the VENG light-dark variations.

SIGNIFICANCE

As a major outcome of this study, vagus nerve recordings hold the promise to help understand circadian regulation.

Seizures and Sleep: Not such strange bedfellows

It has long been recognised that sleep and deprivation of it have important consequences for cortical excitability, the electroencephalogram and seizure control. However, in the management of people with epilepsy, it is also important to recognise that epilepsy and its treatment may also have significant implications for sleep. Lack of consideration for this bidirectional relationship between sleep and epilepsy may have negative consequences on individuals’ seizure control, quality of life, and other aspects of their health.

DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy

There is growing interest in using adaptive neuromodulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the 'Picostim DyNeuMo Mk-1' (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient's movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

Sleep and epilepsy: A snapshot of knowledge and future research lines

Sleep and epilepsy have a reciprocal relationship, and have been recognized as bedfellows since antiquity. However, research on this topic has made a big step forward only in recent years. In this narrative review we summarize the most stimulating discoveries and insights reached by the "European school." In particular, different aspects concerning the sleep-epilepsy interactions are analysed: (a) the effects of sleep on epilepsy; (b) the effects of epilepsy on sleep structure; (c) the relationship between epilepsy, sleep and epileptogenesis; (d) the impact of epileptic activity during sleep on cognition; (e) the relationship between epilepsy and the circadian rhythm; (f) the history and features of sleep hypermotor epilepsy and its differential diagnosis; (g) the relationship between epilepsy and sleep disorders.

The Effect of Alpha Neurofeedback Training on Cognitive Performance in Healthy Adults

This study investigates the effect of long-term alpha neurofeedback training (NFT) in healthy adults using music stimuli. The optimal protocol for future research is presented in this study. The data from 40 healthy participants, divided into two groups (NFT group and Control group), were analyzed in the current study. We found a significantly enhanced alpha rhythm after training in the NFT group which was not observed in the control group. The immediate subsequent effects were greater in more than 80% of the sessions from the initial recordings. Stroop task and behavioral questionnaires, mini-mental state exam (MMSE), and perceived stress scale (PSS) did not reveal any training-specific changes. Within-training session effects were significant from the baseline and were more pronounced at the beginning of the session as compared to the end of the session. It is also observed that a shorter session length with multiple sessions may be more effective than a long and continuous run of a single session.

Beyond Seizure Control: Treating Comorbidities in Epilepsy via Targeting of the P2X7 Receptor

Epilepsy is one of the most common chronic diseases of the central nervous system (CNS). Treatment of epilepsy remains, however, a clinical challenge with over 30% of patients not responding to current pharmacological interventions. Complicating management of treatment, epilepsy comes with multiple comorbidities, thereby further reducing the quality of life of patients. Increasing evidence suggests purinergic signalling via extracellularly released ATP as shared pathological mechanisms across numerous brain diseases. Once released, ATP activates specific purinergic receptors, including the ionotropic P2X7 receptor (P2X7R). Among brain diseases, the P2X7R has attracted particular attention as a therapeutic target. The P2X7R is an important driver of inflammation, and its activation requires high levels of extracellular ATP to be reached under pathological conditions. Suggesting the therapeutic potential of drugs targeting the P2X7R for epilepsy, P2X7R expression increases following status epilepticus and during epilepsy, and P2X7R antagonism modulates seizure severity and epilepsy development. P2X7R antagonism has, however, also been shown to be effective in treating conditions most commonly associated with epilepsy such as psychiatric disorders and cognitive deficits, which suggests that P2X7R antagonisms may provide benefits beyond seizure control. This review summarizes the evidence suggesting drugs targeting the P2X7R as a novel treatment strategy for epilepsy with a particular focus of its potential impact on epilepsy-associated comorbidities.

Circannual incidence of seizure evacuations from the Canadian Arctic

OBJECTIVES

Emerging evidence suggests that circadian rhythms affect seizure propensity in addition to, and possibly independent of, sleep-wake states. Subject to extreme seasonal changes in light and dark, the northerly Arctic can serve as a "natural experiment" to assess the real-life impact of environmental influences on seizure severity. Therefore, we evaluated the timing of seizure evacuations over 11.25 years in a well-defined region of the Canadian Arctic.

METHODS

Retrospective review of EEG database and patient records at the single "bottleneck" hospital to which all patients from the Kivalliq Region in Nunavut, Canada are evacuated for seizure emergencies. We calculated the mean resultant length (MRL) of circular data for circannual analysis, and conducted Rayleigh's test to assess for a statistical departure from circular uniformity.

RESULTS

Screening 40,392 EEGs, we found 117 medical evacuations from 99 distinct individuals from September 2009 to November 2020. Most evacuations occurred month-wise in May (19%); week-wise within a 7-day period in February (5%), June (5%), or November (5%); and day-wise within a 24-hour period in June (3%) or November (3%). Maximal MRL clustering occurred in April no matter if analyzed by day (0.16333, p = 0.04), week (0.16296, p = 0.04), or month (0.1736, p = 0.03).

CONCLUSIONS

A relative circannual increase in seizure evacuations between the winter and summer solstices may be related to increasing sleep loss when day length grows. Fewer evacuations between the summer and winter solstices may be related to decreased daylight and "catching up" on sleep when night length grows. Additional factors likely also play a role in circannual variation of seizure evacuations in the Arctic, which warrants further research.

Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.

More than sleep and wake disturbances: An actigraphic study showing the sleep-wake pattern dysregulation in epilepsy

OBJECTIVE

This study aimed to assess the sleep-wake pattern in patients with epilepsy compared to controls.

METHODS

Patients with epilepsy and controls underwent a 14-day actigraphic recording to evaluate the rest-activity cycle. A sleep medicine interview was performed to exclude conditions interfering with the sleep-wake cycle in both patients and controls. Patients presenting seizures during the actigraphic recording were excluded. Daytime activity, nocturnal sleep, and non-parametric circadian rhythm activity (NPCRA) were analysed.

RESULTS

Twenty-two patients (mean age 49.5 ± 19.84 years; 50% female) and 17 controls were included. Patients showed lower sleep efficiency and longer sleep latency than controls. NPCRA analysis showed lower inter-daily stability and higher intra-daily variability in patients, who also presented lower daytime activity and a longer central phase measure (CPM) than controls.

CONCLUSIONS

Patients showed a significant alteration of the sleep-wake pattern, featured by lower synchronization and higher fragmentation of the rest-activity rhythm. Moreover, patients showed a delayed CPM compared with controls, corresponding to an evening chronotype tendency. Nocturnal sleep alteration and lower daytime activity were also evident. Therefore, patients with epilepsy present an alteration of the sleep-wake pattern and clinicians should increase their awareness about circadian rhythmicity dysregulation in epilepsy.

Distinct Fastigial Output Channels and Their Impact on Temporal Lobe Seizures

Despite being canonically considered a motor control structure, the cerebellum is increasingly recognized for important roles in processes beyond this traditional framework, including seizure suppression. Excitatory fastigial neurons project to a large number of downstream targets, and it is unclear whether this broad targeting underlies seizure suppression, or whether a specific output may be sufficient. To address this question, we used the intrahippocampal kainic acid mouse model of temporal lobe epilepsy, male and female animals, and a dual-virus approach to selectively label and manipulate fastigial outputs. We examined fastigial neurons projecting to the superior colliculus, medullary reticular formation, and central lateral nucleus of the thalamus, and found that these comprise largely nonoverlapping populations of neurons that send collaterals to unique sets of additional, somewhat overlapping, thalamic and brainstem regions. We found that neither optogenetic stimulation of superior colliculus nor reticular formation output channels attenuated hippocampal seizures. In contrast, on-demand stimulation of fastigial neurons targeting the central lateral nucleus robustly inhibited seizures. Our results indicate that fastigial control of hippocampal seizures does not require simultaneous modulation of many fastigial output channels. Rather, selective modulation of the fastigial output channel to the central lateral thalamus, specifically, is sufficient for seizure control. More broadly, our data highlight the concept of specific cerebellar output channels, whereby discrete cerebellar nucleus neurons project to specific aggregates of downstream targets, with important consequences for therapeutic interventions.SIGNIFICANCE STATEMENT The cerebellum has an emerging relationship with nonmotor systems and may represent a powerful target for therapeutic intervention in temporal lobe epilepsy. We find, as previously reported, that fastigial neurons project to numerous brain regions via largely segregated output channels, and that projection targets cannot be predicted simply by somatic locations within the nucleus. We further find that on-demand optogenetic excitation of fastigial neurons projecting to the central lateral nucleus of the thalamus-but not fastigial neurons projecting to the reticular formation, superior colliculus, or ventral lateral thalamus-is sufficient to attenuate hippocampal seizures.

Sleep Disruption Worsens Seizures: Neuroinflammation as a Potential Mechanistic Link

Sleep disturbances, such as insomnia, obstructive sleep apnea, and daytime sleepiness, are common in people diagnosed with epilepsy. These disturbances can be attributed to nocturnal seizures, psychosocial factors, and/or the use of anti-epileptic drugs with sleep-modifying side effects. Epilepsy patients with poor sleep quality have intensified seizure frequency and disease progression compared to their well-rested counterparts. A better understanding of the complex relationship between sleep and epilepsy is needed, since approximately 20% of seizures and more than 90% of sudden unexpected deaths in epilepsy occur during sleep. Emerging studies suggest that neuroinflammation, (e.g., the CNS immune response characterized by the change in expression of inflammatory mediators and glial activation) may be a potential link between sleep deprivation and seizures. Here, we review the mechanisms by which sleep deprivation induces neuroinflammation and propose that neuroinflammation synergizes with seizure activity to worsen neurodegeneration in the epileptic brain. Additionally, we highlight the relevance of sleep interventions, often overlooked by physicians, to manage seizures, prevent epilepsy-related mortality, and improve quality of life.

The circadian clock regulator Bmal1 affects traumatic brain injury in rats through the p38 MAPK signalling pathway

Traumatic brain injury (TBI) is still one of the main causes of death and disability worldwide. Bmal1 (brain and muscle Arnt-like protein-1) is the most central factor of the circadian rhythms that control life and cells. Studies have shown that Bmal1 is involved in inflammation, oxidative stress, vasodilation, glucose and lipid metabolism. This study explored the effect of Bmal1 on secondary brain injury after TBI in rats and the possible mechanism. We established a rat model of TBI induced by the free fall of a weight in rats. The Western blotting and immunofluorescence results showed that the Bmal1 levels decreased in the cerebral cortex after TBI, especially at 48 h. The effects of Bmal1 levels on rats after TBI were evaluated by brain oedema measurement, adhesive removal tests, behavioural tests, and TUNEL and FJC staining. We found that the recombinant Bmal1 protein increased Bmal1 levels after TBI and reduced brain oedema, neurobehavioural injury, somatosensory disturbances, and nerve cell necrosis and apoptosis. The ELISA results showed that Bmal1 overexpression could reduce the inflammatory factors IL-4 and TNF-α after TBI. In contrast, inhibiting Bmal1 expression had the opposite effect. The changes in Bmal1 levels were closely related to the phosphorylation of p38 MAPK after TBI. In conclusion, a decrease in Bmal1 after TBI may exacerbate pathological symptoms in vivo by activating p38 MAPK phosphorylation.

Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study

Background

Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy).

Methods

We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase.

Findings

Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles.

Interpretation

Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

Funding

This research received funding from the Australian Government National Health and Medical Research Council (investigator grant 1178220), the Australian Government BioMedTech Horizons program, and the Epilepsy Foundation of America's ‘My Seizure Gauge’ grant.

How much can medical management alone improve the outcome of adult drug-resistant epilepsy? An exploratory study on possibilities and limitations of combining multiple therapeutic actions

PURPOSE

Failure to control epileptic seizures with two medications, adequately chosen and dosed, indicates drug-resistant epilepsy (DRE). The chance of pharmacologically controlling seizures is low for patients with DRE and uncontrolled seizures who are not candidates for surgery, who have already undergone surgery, or who already had a vagus nerve stimulator (VNS) placed. Patients experiencing these conditions must instead rely on medical management of their seizures, and there is no breakthrough solution on the horizon. Medical care of DRE might be optimized by systematically considering factors that promote and inhibit breakthrough seizures. For example, seizure control could be enhanced through measures such as increasing the frequency of follow-up visits, tracking treatment plan compliance, treating sleep disorders, rational polypharmacy, adjusting drug administration to achieve higher levels when seizures are more likely and educating patients on seizure triggers. A systematic and simultaneous implementation of all of these measures is likely to yield a sizable, clinically relevant, improvement. This paper presents an exploratory study on the effects of implementing such an approach, specifically evaluating this method's impact on seizure frequency.

METHODS

I performed a retrospective chart review of 659 consecutive adult patients with epilepsy followed up at the University of Utah and at the Salt Lake City VA Medical center using the multimodal approach described above. I identified 27 patients who had DRE and uncontrolled seizures and in whom a medical management optimization protocol was implemented. I measured these patients' seizure frequency at the beginning and the end of the study period and compared the results with those of a matching control group of 48 patients.

RESULTS

The optimization protocol did not increase the number of seizure-free patients with DRE; however, it was effective in minimizing seizure frequency in patients whose seizures remained uncontrolled. Among these patients, the median seizure frequency dropped by 64% in the optimization group but did not change in the control group.

CONCLUSIONS

Despite the high occurrence of DRE, there is no accepted protocol for the related medical management. This paper describes an effective approach that can be implemented in a clinically relevant and readily achievable manner.

The pilocarpine model of mesial temporal lobe epilepsy: Over one decade later, with more rodent species and new investigative approaches

Fundamental work on the mechanisms leading to focal epileptic discharges in mesial temporal lobe epilepsy (MTLE) often rests on the use of rodent models in which an initial status epilepticus (SE) is induced by kainic acid or pilocarpine. In 2008 we reviewed how, following systemic injection of pilocarpine, the main subsequent events are the initial SE, the latent period, and the chronic epileptic state. Up to a decade ago, rats were most often employed and they were frequently analysed only behaviorally. However, the use of transgenic mice has revealed novel information regarding this animal model. Here, we review recent findings showing the existence of specific neuronal events during both latent and chronic states, and how optogenetic activation of specific cell populations modulate spontaneous seizures. We also address neuronal damage induced by pilocarpine treatment, the role of neuroinflammation, and the influence of circadian and estrous cycles. Updating these findings leads us to propose that the rodent pilocarpine model continues to represent a valuable tool for identifying the basic pathophysiology of MTLE.

The cerebellum and epilepsy

Epilepsy is the fourth most common neurological disorder, but current treatment options provide limited efficacy and carry the potential for problematic adverse effects. There is an immense need to develop new therapeutic interventions in epilepsy, and targeting areas outside the seizure focus for neuromodulation has shown therapeutic value. While not traditionally associated with epilepsy, anatomical, clinical, and electrophysiological studies suggest the cerebellum can play a role in seizure networks, and importantly, may be a potential therapeutic target for seizure control. However, previous interventions targeting the cerebellum in both preclinical and clinical studies have produced mixed effects on seizures. These inconsistent results may be due in part to the lack of specificity inherent with open-loop electrical stimulation interventions. More recent studies, using more targeted closed-loop optogenetic approaches, suggest the possibility of robust seizure inhibition via cerebellar modulation for a range of seizure types. Therefore, while the mechanisms of cerebellar inhibition of seizures have yet to be fully elucidated, the cerebellum should be thoroughly revisited as a potential target for therapeutic intervention in epilepsy. This article is part of the Special Issue "NEWroscience 2018.

The chronobiology of migraine: a systematic review

BACKGROUND

The paroxysmal nature of migraine is a hallmark of the disease. Some patients report increased attack frequency at certain seasons or towards the end of the week, while others experience diurnal variations of migraine attack onset. This systematic review investigates the chronobiology of migraine and its relation to the periodicity of attacks in existing literature to further understand the oscillating nature of migraine.

MAIN BODY

PubMed and Embase were systematically searched and screened for eligible articles with outcome measures relating to a circadian, weekly or seasonal distribution of migraine attacks. We found that the majority of studies reported morning hours (6 am-12 pm) as the peak time of onset for migraine attacks. More studies reported Saturday as weekly peak day of attack. There was no clear seasonal variation of migraine due to methodological differences (primarily related to location), however four out of five studies conducted in Norway reported the same yearly peak time indicating a possible seasonal periodicity phenomenon of migraine.

CONCLUSIONS

The findings of the current review suggest a possible role of chronobiologic rhythms to the periodicity of migraine attacks. Future studies are, however, still needed to provide more knowledge of the oscillating nature of migraine.

Cycles in epilepsy

Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans have looked for patterns of temporal organization in seizure occurrence. Observations that seizures are cyclical date back to antiquity, but recent technological advances have, for the first time, enabled cycles of seizure occurrence to be quantitatively characterized with direct brain recordings. Chronic recordings of brain activity in humans and in animals have yielded converging evidence for the existence of cycles of epileptic brain activity that operate over diverse timescales: daily (circadian), multi-day (multidien) and yearly (circannual). Here, we review this evidence, synthesizing data from historical observational studies, modern implanted devices, electronic seizure diaries and laboratory-based animal neurophysiology. We discuss advances in our understanding of the mechanistic underpinnings of these cycles and highlight the knowledge gaps that remain. The potential clinical applications of a knowledge of cycles in epilepsy, including seizure forecasting and chronotherapy, are discussed in the context of the emerging concept of seizure risk. In essence, this Review addresses the broad question of why seizures occur when they occur.

Seizure Cycles in Focal Epilepsy

Importance

Focal epilepsy is characterized by the cyclical recurrence of seizures, but, to our knowledge, the prevalence and patterns of seizure cycles are unknown.

Objective

To establish the prevalence, strength, and temporal patterns of seizure cycles over timescales of hours to years.

Design, Setting, and Participants

This retrospective cohort study analyzed data from continuous intracranial electroencephalography (cEEG) and seizure diaries collected between January 19, 2004, and May 18, 2018, with durations up to 10 years. A total of 222 adults with medically refractory focal epilepsy were selected from 256 total participants in a clinical trial of an implanted responsive neurostimulation device. Selection was based on availability of cEEG and/or self-reports of disabling seizures.

Exposures

Antiseizure medications and responsive neurostimulation, based on clinical indications.

Main Outcomes and Measures

Measures involved (1) self-reported daily seizure counts, (2) cEEG-based hourly counts of electrographic seizures, and (3) detections of interictal epileptiform activity (IEA), which fluctuates in daily (circadian) and multiday (multidien) cycles. Outcomes involved descriptive characteristics of cycles of IEA and seizures: (1) prevalence, defined as the percentage of patients with a given type of seizure cycle; (2) strength, defined as the degree of consistency with which seizures occur at certain phases of an underlying cycle, measured as the phase-locking value (PLV); and (3) seizure chronotypes, defined as patterns in seizure timing evident at the group level.

Results

Of the 222 participants, 112 (50%) were male, and the median age was 35 years (range, 18-66 years). The prevalence of circannual (approximately 1 year) seizure cycles was 12% (24 of 194), the prevalence of multidien (approximately weekly to approximately monthly) seizure cycles was 60% (112 of 186), and the prevalence of circadian (approximately 24 hours) seizure cycles was 89% (76 of 85). Strengths of circadian (mean [SD] PLV, 0.34 [0.18]) and multidien (mean [SD] PLV, 0.34 [0.17]) seizure cycles were comparable, whereas circannual seizure cycles were weaker (mean [SD] PLV, 0.17 [0.10]). Across individuals, circadian seizure cycles showed 5 peaks: morning, mid-afternoon, evening, early night, and late night. Multidien cycles of IEA showed peak periodicities centered around 7, 15, 20, and 30 days. Independent of multidien period length, self-reported and electrographic seizures consistently occurred during the days-long rising phase of multidien cycles of IEA.

Conclusions and Relevance

Findings in this large cohort establish the high prevalence of plural seizure cycles and help explain the natural variability in seizure timing. The results have the potential to inform the scheduling of diagnostic studies, the delivery of time-varying therapies, and the design of clinical trials in epilepsy.

Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study

Background

Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy.

Objective

EEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk.

Methods

A total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King’s College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk.

Results

The EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022.

Conclusions

With the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence.

International Registered Report Identifier (IRRID)

PRR1-10.2196/25309

Diurnal changes in perineuronal nets and parvalbumin neurons in the rat medial prefrontal cortex

Perineuronal nets (PNNs) surrounding fast-spiking, parvalbumin (PV) interneurons provide excitatory:inhibitory balance, which is impaired in several disorders associated with altered diurnal rhythms, yet few studies have examined diurnal rhythms of PNNs or PV cells. We measured the intensity and number of PV cells and PNNs labeled with Wisteria floribunda agglutinin (WFA) and also the oxidative stress marker 8-oxo-deoxyguanosine (8-oxo-dG) in rat prelimbic medial prefrontal cortex (mPFC) at Zeitgeber times (ZT) ZT0 (lights-on, inactive phase), ZT6 (mid-inactive phase), ZT12 (lights-off, active phase), and ZT18 (mid-active phase). Relative to ZT0, the intensities of PNN and PV labeling were increased in the dark (active) phase compared with the light (inactive) phase. The intensity of 8-oxo-dG was decreased from ZT0 at all times (ZT6,12,18). We also measured GAD 65/67 and vGLUT1 puncta apposed to PV cells with and without PNNs. There were more excitatory puncta on PV cells with PNNs at ZT18 vs. ZT6, but no changes in PV cells without PNNs and no changes in inhibitory puncta. Whole-cell slice recordings in fast-spiking (PV) cells with PNNs showed an increased ratio of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor:N-methyl-D-aspartate receptor (AMPA: NMDA) at ZT18 vs. ZT6. The number of PV cells and PV/PNN cells containing orthodenticle homeobox 2 (OTX2), which maintains PNNs, showed a strong trend toward an increase from ZT6 to ZT18. Diurnal fluctuations in PNNs and PV cells are expected to alter cortical excitatory:inhibitory balance and provide new insights into treatments for diseases impacted by disturbances in sleep and circadian rhythms.

Can chronopharmacology improve the therapeutic management of neurological diseases?

The importance of circadian rhythm dysfunctions in the pathophysiology of neurological diseases has been highlighted recently. Chronopharmacology principles imply that tailoring the timing of treatments to the circadian rhythm of individual patients could optimize therapeutic management. According to these principles, chronopharmacology takes into account the individual differences in patients' clocks, the rhythmic changes in the organism sensitivity to therapeutic and side effects of drugs, and the predictable time variations of disease. This review examines the current literature on chronopharmacology of neurological diseases focusing its scope on epilepsy, Alzheimer and Parkinson diseases, and neuropathic pain, even if other neurological diseases could have been analyzed. While the results of the studies discussed in this review point to a potential therapeutic benefit of chronopharmacology in neurological diseases, the field is still in its infancy. Studies including a sufficiently large number of patients and measuring gold standard markers of the circadian rhythmicity are still needed to evaluate the beneficial effect of administration times over the 24-hour day but also of clock modulating drugs.

Untangling a Web: Basic Mechanisms of the Complex Interactions Between Sleep, Circadian Rhythms, and Epilepsy

Seizures have sleep–wake and circadian patterns in various epilepsies and, in turn, disrupt sleep and circadian rhythms. The resultant sleep deprivation (SD) is an exacerbating factor for seizures that sets up a vicious cycle that can potentially lead to disease progression and even to epilepsy-related mortality. A variety of cellular or network electrophysiological changes and changes in expression of clock-controlled genes or other transcription factors underlie sleep–wake and circadian distribution of seizures, as well as the disruptions seen in both. A broad understanding of these mechanisms may help in designing better treatments to prevent SD-induced seizure exacerbation, disrupt the vicious cycle of disease progression, and reduce epilepsy-related mortality.

Temporal patterns in the first unprovoked seizure

OBJECTIVE

Cyclic phenomena in epilepsy are well recognized. We investigated a multicenter cohort of unprovoked first seizure presentations to determine whether seizures have a preponderance to occur in: a particular time of the day, a particular day of the week, a particular month of the year, day time versus night time, and wakefulness versus sleep.

METHODS

We retrospectively studied adults who presented with a first-ever unprovoked seizure to the First Seizure Clinic at two tertiary centers in Australia. Seizure onset time was obtained from the emergency department and ambulance documentations. Electro-clinical and neuroimaging findings were reviewed. We used histograms and Poisson regression modeling to determine whether seizures have a preponderance to occur at a particular time and calculated incidence rate ratios (IRR). We performed further analysis on patients with "first seizure epilepsy" and "first seizure not epilepsy" based on the ILAE criteria for a diagnosis of epilepsy after a single unprovoked seizure, as well as comparing patients that could be categorized as having a generalized-onset seizure versus those with focal-onset seizures.

RESULTS

We analyzed 1724 patients (38% females; age range 14-97 yr, median 39 yr), of whom 18% had epileptiform abnormalities on EEG and potentially epileptogenic lesions were detected on neuroimaging in 28%. Whole cohort analysis shows the incidence rate ratios (IRR) of seizures varied significantly across the 24-hour clock-time of the day (p < 0.001), peaking at hour 12 (IRR 3.18). The first unprovoked seizure was significantly less likely to be reported during the night (IRR 0.61, p < 0.001) and during sleep (IRR 0.29, p < 0.001). Both the "first seizure epilepsy" and "first seizure not epilepsy" subgroups' analysis demonstrated similar patterns. An infraradian pattern was also noted with seizures most likely to occur in May (IRR 1.29, p = 0.02). Both "first seizure epilepsy - generalized" and "first seizure epilepsy - focal" groups had a preponderance for seizures to occur during the day versus night and wakefulness as opposed to sleep, but the association was more robust for generalized seizures.

CONCLUSIONS

Our results suggest that temporal patterns are seen in patients with first-ever unprovoked seizures, including those that meet contemporary criteria for epilepsy. These results raise the possibility that first unprovoked seizures have intrinsic rhythmicity similar to epileptic seizures.