Circadian rhythm and epilepsy

More variable circadian rhythms in epilepsy captured by long-term heart rate recordings from wearable sensors

OBJECTIVE

The circadian rhythm synchronizes physiological and behavioral patterns with the 24-h light-dark cycle. Disruption to the circadian rhythm is linked to various health conditions, although optimal methods to describe these disruptions remain unclear. An emerging approach is to examine the intraindividual variability in measurable properties of the circadian rhythm over extended periods. Epileptic seizures are modulated by circadian rhythms, but the relevance of circadian rhythm disruption in epilepsy remains unexplored. Our study investigates intraindividual circadian variability in epilepsy and its relationship with seizures.

METHODS

We retrospectively analyzed >70 000 h of wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 healthy controls. Circadian oscillations in heart rate time series were extracted, daily estimates of circadian period, acrophase, and amplitude properties were produced, and estimates of the intraindividual variability of these properties over an entire recording were calculated.

RESULTS

PWE exhibited greater intraindividual variability in period (76 vs. 57 min, d = .66, p < .001) and acrophase (64 vs. 48 min, d = .49, p = .004) compared to controls, but not in amplitude (2 beats per minute, d = -.15, p = .49). Variability in circadian properties showed no correlation with seizure frequency nor any differences between weeks with and without seizures.

SIGNIFICANCE

For the first time, we show that heart rate circadian rhythms are more variable in PWE, detectable via consumer wearable devices. However, no association with seizure frequency or occurrence was found, suggesting that this variability might be underpinned by the epilepsy etiology rather than being a seizure-driven effect.

Implantable systems for neurological chronotherapy

Implantable systems for neurological chronotherapy are poised to revolutionize the treatment of central nervous system diseases and disorders. These devices enable precise, time-controlled drug delivery aligned with the body's circadian rhythms, optimizing therapeutic outcomes. By bypassing the blood-brain barrier, they achieve high local drug concentrations while minimizing systemic side effects, offering significant advantages for conditions where traditional therapies often fall short. Platforms like SynchroMed II and CraniUS showcase this innovation, providing programmable delivery for conditions such as epilepsy and glioblastoma, with customizable profiles ranging from continuous infusion to timed bolus administration. Preclinical and clinical studies underscore the efficacy of aligning drug delivery with circadian rhythms, enhancing outcomes in chrono-chemotherapy and anti-epileptic treatments. Despite their promise, challenges remain, including the invasiveness of implantation within the brain, device longevity, synchronization complexities, and cost(s). Accordingly, this review explores the current state of implantable neurological systems that may be leveraged for chronotherapy, their applications, limitations, and potential to transform neurological disease/disorder management.

Causal relationship between sleep traits and risk of Epilepsy: A Mendelian randomization study

BACKGROUND

Observational studies have identified a strong correlation between epilepsy and sleep traits, highlighting their interactive relationship. However, no studies have specifically examined the associations between sleep traits and epilepsy. In this context, we conducted a Mendelian Randomization (MR) investigation to explore the causal nature of these associations.

METHODS

We performed a two-sample Mendelian Randomization (MR) analysis model to genetically predict the causal effects of morning chronotype on epilepsy. Five MR analysis methods were conducted to analyze the final results. The inverse-variance weighted (IVW) method was used as the primary outcome. The other MR analysis methods (MR-Egger; Weighted Mode; and Weighted median (WM)) were conducted as the complement to IVW. In addition, the robustness of the MR analysis results was assessed by leave-one-out analysis.

RESULTS

In forward MR, epilepsy showed causal relationships with sleep duration (IVW beta = 0.008, P = 0.015). Specifically, doubling the odds of inheriting epilepsy may be associated with a 0.0075 standard deviation (SD) (95 % CI: 1.001 to 1.014) increase in sleep duration. In reverse MR, we found statistically significant associations between chronotype (evening preference) (OR = 1.397, p = 0.007) and insomnia (OR = 2.280, p = 0.025) and the risk of epilepsy.

CONCLUSION

Our two-sample Mendelian randomization analyses indicate that individuals with epilepsy frequently experience extended sleep duration. Additionally, we identified insomnia and chronotype (evening preference) as significant risk factors that increase the likelihood of developing epilepsy.

Mechanistic insights into the interaction between epilepsy and sleep

Epidemiological evidence has demonstrated associations between sleep and epilepsy, but we lack a mechanistic understanding of these associations. If sleep affects the pathophysiology of epilepsy and the risk of seizures, as suggested by correlative evidence, then understanding these effects could provide crucial insight into the basic mechanisms that underlie the development of epilepsy and the generation of seizures. In this Review, we provide in-depth discussion of the associations between epilepsy and sleep at the cellular, network and system levels and consider the mechanistic underpinnings of these associations. We also discuss the clinical relevance of these associations, highlighting how they could contribute to improvements in the management of epilepsy. A better understanding of the mechanisms that govern the interactions between epilepsy and sleep could guide further research and the development of novel approaches to the management of epilepsy.

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.

Abnormal Synchronization Between Cortical Delta Power and Ripples in Hippocampal Sclerosis

OBJECTIVE

Discriminating between epileptogenic and physiological ripples in the hippocampus is important for identifying epileptogenic (EP) zones; however, distinguishing these ripples on the basis of their waveforms is difficult. We hypothesized that the nocturnal synchronization of hippocampal ripples and cortical delta power could be used to classify epileptogenic and physiological ripples in the hippocampus.

METHODS

We enrolled 38 patients with electrodes implanted in the hippocampus or parahippocampal gyrus between April 2014 and March 2023 at our institution. We divided 11 patients (11 hippocampi) who were pathologically diagnosed with hippocampal sclerosis into the EP group and five patients (six hippocampi) with no epileptogenicity in the hippocampus into the nonepileptogenic (NE) group. Hippocampal ripples were detected using intracranial electroencephalography with hippocampal or parahippocampal electrodes. Cortical delta power (0.5-4 Hz) was assessed using cortical electrodes. The Pearson correlation coefficient between the ripple rates and cortical delta power (Corr-RD) was calculated on the basis of the intracranial electroencephalographic signals recorded each night.

RESULTS

Although hippocampal ripples were similar among the EP and NE groups based on their waveforms and frequency properties, the Corr-RDs in the EP group (mean [standard deviation]: 0.20 [0.049]) were significantly lower than those in the NE group (0.67 [0.070]). On the basis of the minimum Corr-RDs, the two groups were classified with 94.1% accuracy.

INTERPRETATION

Our results demonstrate that the Corr-RD is a biomarker of hippocampal epileptogenicity.

Targeting the central and peripheral nervous system to regulate bone homeostasis: mechanisms and potential therapies

The skeleton is innervated by different types of nerves and receives signaling from the nervous system to maintain homeostasis and facilitate regeneration or repair. Although the role of peripheral nerves and signals in regulating bone homeostasis has been extensively investigated, the intimate relationship between the central nervous system and bone remains less understood, yet it has emerged as a hot topic in the bone field. In this review, we discussed clinical observations and animal studies that elucidate the connection between the nervous system and bone metabolism, either intact or after injury. First, we explored mechanistic studies linking specific brain nuclei with bone homeostasis, including the ventromedial hypothalamus, arcuate nucleus, paraventricular hypothalamic nucleus, amygdala, and locus coeruleus. We then focused on the characteristics of bone innervation and nerve subtypes, such as sensory, sympathetic, and parasympathetic nerves. Moreover, we summarized the molecular features and regulatory functions of these nerves. Finally, we included available translational approaches that utilize nerve function to improve bone homeostasis and promote bone regeneration. Therefore, considering the nervous system within the context of neuromusculoskeletal interactions can deepen our understanding of skeletal homeostasis and repair process, ultimately benefiting future clinical translation.

Clinical Chronobiology: Circadian Rhythms in Health and Disease

Circadian rhythms (CRs) are entrainable endogenous rhythms that respond to external stimuli and regulate physiological functions. The suprachiasmatic nucleus (SCN) in the hypothalamus is the mammalian master clock that synchronizes all other tissue-specific peripheral clocks, primarily through gamma-aminobutyric acid (GABA) and vasoactive intestinal polypeptide (VIP). The SCN follows Earth's 24-hour cycle by light entrainment through the retinohypothalamic tract. At the cellular level, the core clock genes CLOCK, BMAL1, PER1-PER3, CRY1, and CRY2 regulate CRs in a negative feedback loop. The circadian disruption of the sleep-wake cycle manifests in at least six distinct clinical conditions. These are the circadian rhythm sleep-wake disorders (CRSWDs). Their diagnosis is made by history, sleep diaries, and actigraphy. Treatment involves a combination of timed light exposure, melatonin/melatonin agonists, and behavioral interventions. In addition, CR disturbances and subsequent misalignment can increase the risk of a variety of illnesses. These include infertility and menstrual irregularities as well as diabetes, obesity, fatty liver disease, and other metabolic syndromes. In addition, a disruption in the gut microbiome creates a proinflammatory environment. CR disturbances increase the risk for mood disorders, hence the utility of light-based therapies in depression. People with neurodegenerative disorders demonstrate significant disturbances in their CRs, and in their sleep-wake cycles. Circadian realignment therapies can also help decrease the symptomatic burden of these disorders. Certain epilepsy syndromes, such as juvenile myoclonic epilepsy (JME), have a circadian pattern of seizures. Circadian disturbances in epilepsy can be both the consequence and cause for breakthrough seizures. The immune system has its own CR. Disturbances in these due to shift work, for instance, can increase the risk of infections. CR disturbances can also increase the risk of cancer by impacting DNA repair, apoptosis, immune surveillance, and cell cycle regulation. Moreover, the timing of chemotherapeutic agents has been shown to increase their therapeutic impact in certain cancers.

Tonic-clonic seizures induce hypersomnia and suppress rapid eye movement sleep in mouse models of epilepsy

The reciprocal relationship between sleep and epilepsy has been reported by numerous clinical studies. However, the underlying neural mechanisms are poorly understood. Animal models of epilepsy are powerful tools to tackle this question. A lagging research area is the understudied sleep in epilepsy models. Here, we characterize sleep architecture and its relationship with seizures in a mouse model of sleep-related hypermotor epilepsy, caused by mutation of KCNT1. We demonstrated that nocturnal tonic-clonic seizures induce more non-rapid eye movement (NREM) sleep but suppress rapid eye movement (REM) sleep, resulting in altered sleep architecture in this mouse model. Importantly, the seizure number is quantitatively anticorrelated with the amount of REM sleep. Strikingly, this modulation of NREM and REM sleep states can be repeated in another mouse model of epilepsy with diurnal tonic-clonic seizures. Together, our findings provide evidence from rodent models to substantiate the close interplay between sleep and epilepsy, which lays the ground for mechanistic studies.

Diurnal variation in brain injury after cardiac arrest and cardiopulmonary resuscitation

Background

Although the circadian rhythm is known to influence several neurological diseases and response to treatments, its potential impact on brain injury following cardiac arrest and cardiopulmonary resuscitation (CA/CPR) remains unclear.

Methods

We performed a retrospective observational study on out-of-hospital cardiac arrest (OHCA) cases that presented to the emergency department of our hospital between September 2022 and August 2024. Based on the CA/CPR onset time, all patients were divided into two cohorts: daytime and nighttime groups. The gray-to white-matter signal intensity ratio (GWR) was analyzed using brain computed tomography (CT) images. We used the Cerebral Performance Category (CPC) to estimate the neurological outcomes. C-reactive protein (CRP), white blood cell (WBC) count, and monocyte (MONO) count levels in the plasma were also analyzed.

Results

Our study included 138 patients, of whom 68 were subjected to CA/CPR during daytime (8:00 to 20:00) and the remaining 70 were subjected to CA/CPR during nighttime (20:00 to 8:00). The imaging data showed that GWR values were significantly lower among patients subjected to CA/CPR during nighttime compared to those who were subjected to CA/CPR during daytime. Consistently, lower survival rates were observed among nighttime CA/CPR survivors. The CPC results indicated that a greater number of patients who underwent CA/CPR during daytime were rated as class 1-2 on day 3, day 5, and day 7 after achieving return of spontaneous circulation (ROSC). In contrast, a larger proportion of CA/CPR survivors in the nighttime group were rated as class 5 at the same time points. Elevated levels of C-reactive protein, white blood cell count, and monocyte count were observed in the plasma of survivors who underwent nighttime CA/CPR.

Conclusion

We found that patients subjected to CA/CPR during nighttime (20:00-8:00) had worse neurological outcomes compared to those treated during daytime (8:00-20:00).

The Circadian Brain and Cognition

Circadian rhythms are inherent to living organisms from single cells to humans and operate on a genetically determined cycle of approximately 24 hours. These endogenous rhythms are aligned with the external light/dark cycle of the Earth's rotation and offer the advantage of anticipating environmental changes. Circadian rhythms act directly on human cognition and indirectly through their fundamental influence on sleep/wake cycles. The strength of the circadian regulation of performance depends on the accumulated sleep debt and the cognitive domain, and it has been suggested to involve the activation of ascending arousal systems and their interaction with attention and other cognitive processes. In addition, attention-related cortical responses show extensive circadian rhythms, the phases of which vary across brain regions. This review discusses the impact of the circadian system on sleep/wake regulation and cognitive performance. It further addresses the health implications of circadian disruption, particularly in relation to mental and neurological disorders.

Optimizing epilepsy treatment: the impact of circadian rhythms and medication timing on conversion rates and survival

BACKGROUND

The progression from isolated seizures to status epilepticus (SE) is a critical clinical issue. This study explores the influence of circadian rhythms on this transition and assesses the impact of medication timing on SE conversion rates and patient survival.

AIM

To determine the circadian patterns in the transition from isolated seizures to SE and to evaluate the efficacy of medication timing in reducing this conversion and improving survival outcomes.

DESIGN AND METHODS

Utilizing the eICU Collaborative Research Database v2.0, a retrospective analysis was performed on patients at risk of SE conversion. The study analyzed the correlation between SE conversion timing and AEDs administration in relation to circadian rhythms, using a Logit model to evaluate the impact of medication timing on SE conversion and survival.

RESULTS

The transition from isolated seizures to SE showed distinct circadian patterns, with a delayed acrophase. Early night-time AEDs administration significantly reduced conversion rates. Medication timing also influenced survival rates, with higher survival during specific periods.

CONCLUSION

Circadian rhythms significantly affect the transition from isolated seizures to SE. Timely AEDs administration is crucial for reducing conversions and improving survival. A chronotherapeutic approach aligning AEDs administration with individual circadian vulnerabilities could advance epilepsy management in ICU settings. Future research should focus on personalized medication strategies that utilize circadian rhythms to optimize treatment effects.

Chronobiological Patterns and Risk of Acute Aortic Dissection: A Clinical Retrospective and Two-Sample Mendelian Randomisation Study

AIM

Acute aortic dissection (AAD) represents a cardiovascular ailment characterised by a notable mortality rate. Chronobiological patterns can offer a predictive framework for anticipating the onset of AAD.

METHOD

Data were gathered from 1,151 patients diagnosed with AAD at Changhai Hospital in Shanghai, China, spanning 2000-2023. The χ2 test was used to assess whether specific periods exhibited significantly different seasonal/weekly distributions compared with others. Fourier models were utilised for the analysis of rhythmicity in monthly/circadian distribution. Publicly available genome-wide association studies datasets were used to establish the causal relationship between chronotype and AAD. Two sets of genetics instruments were used for analysis, derived from publicly available genetic summary data: 75 single-nucleotide polymorphisms (SNPs) significantly associated with chronotype; and SNPs associated with AAD in the FinnGen consortium.

RESULTS

The mean age was 51.5±13.8 years, with 665 patients (57.8%) aged <55 years. Among the 1,151 patients, 80.9% were male. The distribution of DeBakey types was 73.2% (843) for DeBakey I, 21% (242) for DeBakey II, and 5.7% (66) for DeBakey III. Comorbidities included hypertension in 58.5% (673 cases) and diabetes in 7.8% (90 cases). A peak occurred during colder periods (winter/December), and a trough was noted in warmer periods (summer/June). Weekly distribution exhibited no significant variation. Fourier analysis revealed a statistically significant circadian variation (p<0.0001) with a trough between 23:00 and 00:00, a prominent peak from 07:00 to 08:00, and a minor peak between 20:00 and 21:00. Subgroup analyses identified circadian rhythmicity in all subgroups, except for the DeBakey III group and the female group. Using the 75 chronotype-related SNPs, evidence was found of a potential causal effect of chronotype on the risk of AAD, as the inverse-variance weighting analysis showed that self-report chronotype of morningness was associated with a decreased risk of AAD.

CONCLUSION

The findings substantiate that the initiation of AAD displays noteworthy seasonal, monthly, and circadian patterns. The Mendelian randomisation analysis also indicated that the onset of acute aortic dissection is related to circadian rhythm. These findings offer a fresh perspective, facilitating the identification of triggering factors for AAD and bolstering preventive measures for this catastrophic event.

Circadian rhythm and epilepsy: a nationally representative cross-sectional study based on actigraphy data

Objective

The study aims to assess the relationship between epilepsy and circadian rhythms.

Method

This study included a cohort of 7,410 participants sourced from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) database. The investigation focused on the comparative analysis of seven nonparametric indices associated with circadian rhythms (Interdaily Stability (IS), Intradaily Variability (IV), Relative Amplitude (RA), L5, M10, L5 start time, and M10 start time) between the overall population and patients with epilepsy. Logistic regression analysis was utilized to assess the potential correlation between the rest-activity circadian rhythm patterns and the presence of epilepsy within the cohort.

Results

Compared to the general population, individuals with epilepsy exhibited lower values of IS and M10. Multivariable logistic regression analysis, when IS, RA, and M10 were categorized into four groups based on quartiles, revealed that the odds ratio (IS: OR = 0.36, 95% CI: 0.13, 0.89; RA: OR = 0.25, 95% CI: 0.06, 0.77; M10: OR = 0.24, 95% CI: 0.06, 0.73) for the highest quartile was lower than that for the lowest quartile. Furthermore, after adjustment for confounding factors, participants in the highest quartile compared to those in the lowest quartile of IV and M10 start time demonstrated a higher prevalence of epilepsy.

Conclusion

Individuals with epilepsy demonstrate significant alterations in circadian rhythms.

The Oxidative Stress in Epilepsy-Focus on Melatonin

Oxidative stress develops when there is an excess of oxidants leading to molecular and cellular damage. Seizure activity leads to oxidative stress and the resulting increased lipid peroxidation. Generally, antiseizure medications reduce oxidative stress, although the data on levetiracetam are ambiguous. Exogenous antioxidants (vitamin E, resveratrol, hesperidin, and curcumin) have been documented to exert an anticonvulsant effect in animal models of seizures and some recent clinical data point to curcumin as an affective adjuvant for the therapy of pediatric intractable epilepsy. Melatonin is an antioxidant with an ability to attenuate seizure activity induced by various convulsants in rodents. Its clinical effectiveness has been also confirmed in a number of clinical studies. Experimental studies point to a possibility that endogenous melatonin may possess proconvulsive activity. Moreover, some scarce clinical data seem to express this view; however, a limited number of patients were included. The anticonvulsant activity of exogenous melatonin may involve GABA-mediated inhibition, while endogenous melatonin may act as a proconvulsant due to a decrease in the brain dopaminergic transmission. Antioxidants, including melatonin, may be considered as adjuvants in the therapy of epilepsy and melatonin, in addition, in patients with epilepsy suffering from sleep disorders.

Timing Mechanisms for Circadian Seizures

For centuries, epileptic seizures have been noticed to recur with temporal regularity, suggesting that an underlying biological rhythm may play a crucial role in their timing. In this review, we propose to adopt the framework of chronobiology to study the circadian timing of seizures. We first review observations made on seizure timing in patients with epilepsy and animal models of the disorder. We then present the existing chronobiology paradigm to disentangle intertwined circadian and sleep-wake timing mechanisms. In the light of this framework, we review the existing evidence for specific timing mechanisms in specific epilepsy syndromes and highlight that current knowledge is far from sufficient. We propose that individual seizure chronotypes may result from an interplay between independent timing mechanisms. We conclude with a research agenda to help solve the urgency of ticking seizures.

Chronobiological Spatial Clusters of Cortical Regions in the Human Brain

PURPOSE

We demonstrate that different regions of the cerebral cortex have different diurnal rhythms of spontaneously occurring high-frequency oscillations (HFOs).

METHODS

High-frequency oscillations were assessed with standard-of-care stereotactic electroencephalography in patients with drug-resistant epilepsy. To ensure generalizability of our findings beyond patients with drug-resistant epilepsy, we excluded stereotactic electroencephalography electrode contacts lying within seizure-onset zones, epileptogenic lesions, having frequent epileptiform activity, and excessive artifact. For each patient, we evaluated twenty-four 5-minute stereotactic electroencephalography epochs, sampled hourly throughout the day, and obtained the HFO rate (number of HFOs/minute) in every stereotactic electroencephalography channel. We analyzed diurnal rhythms of the HFO rates with the cosinor model and clustered neuroanatomic parcels in a standard brain space based on similarity of their cosinor parameters. Finally, we compared overlap among resting-state networks, described in the neuroimaging literature, and chronobiological spatial clusters discovered by us.

RESULTS

We found five clusters that localized predominantly or exclusively to the left perisylvian, left perirolandic and left temporal, right perisylvian and right parietal, right frontal, and right insular-opercular cortices, respectively. These clusters were characterized by similarity of the HFO rates according to the time of the day. Also, these chronobiological spatial clusters preferentially overlapped with specific resting-state networks, particularly default mode network (clusters 1 and 3), frontoparietal network (cluster 1), visual network (cluster 1), and mesial temporal network (cluster 2).

CONCLUSIONS

This is probably the first human study to report clusters of cortical regions with similar diurnal rhythms of electrographic activity. Overlap with resting-state networks attests to their functional significance and has implications for understanding cognitive functions and epilepsy-related mortality.

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.

BMAL1/PGC1α4-FNDC5/irisin axis impacts distinct outcomes of time-of-day resistance exercise

BACKGROUND

Resistance exercise leads to improved muscle function and metabolic homeostasis. Yet how circadian rhythm impacts exercise outcomes and its molecular transduction remains elusive.

METHODS

Human volunteers were subjected to 4 weeks of resistance training protocols at different times of day to assess training outcomes and their associations with myokine irisin. Based on rhythmicity of Fibronectin type III domain containing 5 (FNDC5/irisin), we trained wild type and FNDC5 knockout mice at late active phase (high FNDC5/irisin level) or late rest phase (low FNDC5/irisin level) to analyze exercise benefits on muscle function and metabolic homeostasis. Molecular analysis was performed to understand the regulatory mechanisms of FNDC5 rhythmicity and downstream signaling transduction in skeletal muscle.

RESULTS

In this study, we showed that regular resistance exercises performed at different times of day resulted in distinct training outcomes in humans, including exercise benefits and altered plasma metabolomics. We found that muscle FNDC5/irisin levels exhibit rhythmicity. Consistent with human data, compared to late rest phase (low irisin level), mice trained chronically at late active phase (high irisin level) gained more muscle capacity along with improved metabolic fitness and metabolomics/lipidomics profiles under a high-fat diet, whereas these differences were lost in FNDC5 knockout mice. Mechanistically, Basic helix-loop-helix ARNT like 1 (BMAL1) and Peroxisome proliferative activated receptor, gamma, coactivator 1 alpha 4 (PGC1α4) induce FNDC5/irisin transcription and rhythmicity, and the signaling is transduced via αV integrin in muscle.

CONCLUSION

Together, our results offered novel insights that exercise performed at distinct times of day determines training outcomes and metabolic benefits through the rhythmic regulation of the BMAL1/PGC1α4-FNDC5/irisin axis.

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.

Classifier Combination Supported by the Sleep-Wake Cycle Improves EEG Seizure Prediction Performance

OBJECTIVE

Seizure prediction is a promising solution to improve the quality of life for drug-resistant patients, which concerns nearly 30% of patients with epilepsy. The present study aimed to ascertain the impact of incorporating sleep-wake information in seizure prediction.

METHODS

We developed five patient-specific prediction approaches that use vigilance state information differently: i) using it as an input feature, ii) building a pool of two classifiers, each with different weights to sleep/wake training samples, iii) building a pool of two classifiers, each with only sleep/wake samples, iv) changing the alarm-threshold concerning each sleep/wake state, and v) adjusting the alarm-threshold after a sleep-wake transition. We compared these approaches with a control method that did not integrate sleep-wake information. Our models were tested with data (43 seizures and 482 hours) acquired during presurgical monitoring of 17 patients from the EPILEPSIAE database. As EPILEPSIAE does not contain vigilance state annotations, we developed a sleep-wake classifier using 33 patients diagnosed with nocturnal frontal lobe epilepsy from the CAP Sleep database.

RESULTS

Although different patients may require different strategies, our best approach, the pool of weighted predictors, obtained 65% of patients performing above chance level with a surrogate analysis (against 41% in the control method).

CONCLUSION

The inclusion of vigilance state information improves seizure prediction. Higher results and testing with long-term recordings from daily-life conditions are necessary to ensure clinical acceptance.

SIGNIFICANCE

As automated sleep-wake detection is possible, it would be feasible to incorporate these algorithms into future devices for seizure prediction.

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.

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%.

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.

Heterogeneity in susceptibility to polycystic ovary syndrome among women with epilepsy

BACKGROUND

Epilepsy comorbidities adversely affect the quality of life of patients. Women with epilepsy are at a high risk of comorbid endocrine disorders. Among them, the polycystic ovary syndrome (PCOS) has a threefold higher prevalence in women with epilepsy than in healthy women and is the main cause of infertility among the patients. Clinically, women with epilepsy show heterogeneity in the susceptibility to PCOS. This heterogeneity may be associated with genetic factor.

METHODS

To test this, we retrospectively collected clinical data from 45 female patients with epilepsy and divided them into three groups according to their susceptibility to PCOS. Groups A and B represented a high susceptibility to PCOS. Patients in Group A were diagnosed with PCOS before their first seizure, while patients in Group B were diagnosed with PCOS after a short period of monotherapy with a low dose of antiseizure medication (ASM) following the diagnosis of epilepsy. Patients in Group C did not develop PCOS despite a prolonged treatment with high-dose ASM. We compared the clinical data and genetic profiles among the three groups.

RESULTS

We found a clear trend of impaired metabolism in Group B patients and this may be associated with high-frequency mutations in MYO10 and ADGRL3.

CONCLUSIONS

Our study suggests that women with epilepsy are heterogeneous in the susceptibility to PCOS and this is associated with mutations in specific genes. Therefore, genetic screening should be conducted to screen for women with epilepsy who are more likely to have comorbid PCOS, so that they can receive targeted interventions at an early stage to reduce the risk.

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.