The Need for Antiepileptic Drug Chronotherapy to Treat Selected Childhood Epilepsy Syndromes and Avert the Harmful Consequences of Drug Resistance

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.

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.

The Interaction Between Sleep and Epilepsy

PURPOSE OF REVIEW

To review the mutual interactions between sleep and epilepsy, including mechanisms of epileptogenesis, the relationship between sleep apnea and epilepsy, and potential strategies to treat seizures.

RECENT FINDINGS

Recent studies have highlighted the role of functional network systems underlying epileptiform activation in sleep in several epilepsy syndromes, including absence epilepsy, benign focal childhood epilepsy, and epileptic encephalopathy with spike-wave activation in sleep. Sleep disorders are common in epilepsy, and early recognition and treatment can improve seizure frequency and potentially reduce SUDEP risk. Additionally, epilepsy is associated with cyclical patterns, which has led to new treatment approaches including chronotherapy, seizure monitoring devices, and seizure forecasting. Adenosine kinase and orexin receptor antagonists are also promising new potential drug targets that could be used to treat seizures. Sleep and epilepsy have a bidirectional relationship that intersects with many aspects of clinical management. In this article, we identify new areas of research involving future therapeutic opportunities in the field of epilepsy.

Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions

Epilepsy is recognised as a dynamic disease, where both seizure susceptibility and seizure characteristics themselves change over time. Specifically, we recently quantified the variable electrographic spatio-temporal seizure evolutions that exist within individual patients. This variability appears to follow subject-specific circadian, or longer, timescale modulations. It is therefore important to know whether continuously recorded interictaliEEG features can capture signatures of these modulations over different timescales. In this study, we analyse continuous intracranial electroencephalographic (iEEG) recordings from video-telemetry units and find fluctuations in iEEG band power over timescales ranging from minutes up to 12 days. As expected and in agreement with previous studies, we find that all subjects show a circadian fluctuation in their iEEG band power. We additionally detect other fluctuations of similar magnitude on subject-specific timescales. Importantly, we find that a combination of these fluctuations on different timescales can explain changes in seizure evolutions in most subjects above chance level. These results suggest that subject-specific fluctuations in iEEG band power over timescales of minutes to days may serve as markers of seizure modulating processes. We hope that future study can link these detected fluctuations to their biological driver(s). There is a critical need to better understand seizure modulating processes, as this will enable the development of novel treatment strategies that could minimise the seizure spread, duration or severity and therefore the clinical impact of seizures.

Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

STUDY OBJECTIVES

Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.

METHODS

Using a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm's inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH-PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24-72 h) scalp EEG recordings from 112 patients (scalpEEG dataset).

RESULTS

The algorithm achieved a Cohen's kappa of 0.74 on the MGH-PSG holdout testing set and cross-validated Cohen's kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen's kappa ~ 0.75 ± 0.11). The algorithm's performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities.

CONCLUSION

We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

The Molecular Genetic Interaction Between Circadian Rhythms and Susceptibility to Seizures and Epilepsy

Seizure patterns observed in patients with epilepsy suggest that circadian rhythms and sleep/wake mechanisms play some role in the disease. This review addresses key topics in the relationship between circadian rhythms and seizures in epilepsy. We present basic information on circadian biology, but focus on research studying the influence of both the time of day and the sleep/wake cycle as independent but related factors on the expression of seizures in epilepsy. We review studies investigating how seizures and epilepsy disrupt expression of core clock genes, and how disruption of clock mechanisms impacts seizures and the development of epilepsy. We focus on the overlap between mechanisms of circadian-associated changes in SCN neuronal excitability and mechanisms of epileptogenesis as a means of identifying key pathways and molecules that could represent new targets or strategies for epilepsy therapy. Finally, we review the concept of chronotherapy and provide a perspective regarding its application to patients with epilepsy based on their individual characteristics (i.e., being a “morning person” or a “night owl”). We conclude that better understanding of the relationship between circadian rhythms, neuronal excitability, and seizures will allow both the identification of new therapeutic targets for treating epilepsy as well as more effective treatment regimens using currently available pharmacological and non-pharmacological strategies.

Chronobiology of limbic seizures: Potential mechanisms and prospects of chronotherapy for mesial temporal lobe epilepsy

Mesial Temporal Lobe Epilepsy (mTLE) characterized by progressive development of complex partial seizures originating from the hippocampus is the most prevalent and refractory type of epilepsy. One of the remarkable features of mTLE is the rhythmic pattern of occurrence of spontaneous seizures, implying a dependence on the endogenous clock system for seizure threshold. Conversely, circadian rhythms are affected by epilepsy too. Comprehending how the circadian system and seizures interact with each other is essential for understanding the pathophysiology of epilepsy as well as for developing innovative therapies that are efficacious for better seizure control. In this review, we confer how the temporal dysregulation of the circadian clock in the hippocampus combined with multiple uncoupled oscillators could lead to periodic seizure occurrences and comorbidities. Unraveling these associations with additional research would help in developing chronotherapy for mTLE, based on the chronobiology of spontaneous seizures. Notably, differential dosing of antiepileptic drugs over the circadian period and/or strategies that resynchronize biological rhythms may substantially improve the management of seizures in mTLE patients.

Generating randomness: making the most out of disordering a false order into a real one

Randomness is far from a disturbing disorder in nature. Rather, it underlies many processes and functions. Randomness can be used to improve the efficacy of development and of systems under certain conditions. Moreover, valid unpredictable random-number generators are needed for secure communication, rendering predictable pseudorandom strings unsuitable. This paper reviews methods of generating randomness in various fields. The potential use of these methods is also discussed. It is suggested that by disordering a "false order," an effective disorder can be generated to improve the function of systems.

Gauging seizure risk

The current paradigm for treatment of epilepsy begins with trials of antiepileptic drugs, followed by evaluation for resective brain surgery in drug-resistant patients. If surgery is not possible or fails to control seizures, some patients benefit from implanted neurostimulation devices. In addition to their therapeutic benefit, some of these devices have diagnostic capability enabling recordings of brain activity with unprecedented chronicity. Two recent studies using different devices for chronic EEG (i.e., over months to years) yielded convergent findings of daily and multiday cycles of brain activity that help explain seizure timing. Knowledge of these patient-specific cycles can be leveraged to gauge and forecast seizure risk, empowering patients to adopt risk-stratified treatment strategies and behavioral modifications. We review evidence that epilepsy is a cyclical disorder, and we argue that implanted monitoring devices should be offered earlier in the treatment paradigm. Chronic EEG would allow pharmacologic treatments tailored to days of high seizure risk-here termed chronotherapy-and would help characterize long timescale seizure dynamics to improve subsequent surgical planning. Coupled with neuromodulation, the proposed approach could improve quality of life for patients and decrease the number ultimately requiring resective surgery. We outline challenges for chronic monitoring and seizure forecasting that demand close collaboration among engineers, neurosurgeons, and neurologists.