Sleep and Epilepsy, Clinical Spectrum and Updated Review

Exploring shared triggers and potential etiopathogenesis between migraine and idiopathic/genetic epilepsy: Insights from a multicenter tertiary-based study

INTRODUCTION

Migraine and epilepsy are two episodic disorders that share common pathophysiological mechanisms. The aim of our research was to assess the possible shared etiopathogenesis by analyzing the relations of headache, and seizure triggers, based on information obtained from a national cohort surveying the headache characteristics of 809 patients who had been diagnosed with idiopathic/genetic epilepsy.

MATERIAL AND METHODS

Our study utilized data from a multi-center, nationwide investigation of headaches in 809 patients with idiopathic/genetic epilepsy. Out of these, 508 patients reported complaints related to any type of headache (333 Migraines, 175 Headaches of other types). In the initial phase of the study encompassing the entire sample of 809 epilepsy patients, differences in seizure triggers were assessed between the migraine group (n = 333) and the non-migraine group (n = 476). Additionally, the subsequent part of the study pertains to a subgroup of the entire patient group, namely those affected by all types of headaches (n = 508), and differences in headache triggers were assessed among migraine patients (n = 333) and those with other types of headaches (n = 175). Similar differences were observed between epilepsy patients with and without a family history of epilepsy.

RESULTS

The most frequently reported seizure triggers in all I/GE group (n = 809) were stress (23%), sleep deprivation (22%) and fatigue (18%), respectively. The most frequently reported headache triggers in migraine patients were stress (31%), sleep deprivation (28%), and noise (26%). The occurrence of menstruation-triggered seizures in individuals with migraine and I/GE was found to be considerably higher than those without migraine. The most common triggers for seizure and headache among the individuals with a positive family history of epilepsy were determined to be light stimuli and sleep deprivation.

CONCLUSION

In conclusion, our study provides valuable insights into the overlapping triggers including sleep patterns, stress levels, and menstrual cycles, etc. and potential shared etiology of migraine and I/GE. Recognizing these connections may facilitate the development of more precise therapeutic strategies and underscore the significance of adopting a holistic, multidisciplinary approach to the management of these intricate neurological conditions. Further research is essential to explore in greater depth the shared mechanisms underpinning these associations and their implications for clinical practice.

Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning

Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.

Effects of anti-seizure therapies on sleep in patients with epilepsy: A literature review

Sleep disorder is common in epilepsy. With a recent rapid development in sleep medicine, it has been increasingly recognized that anti-seizure therapies, either anti-seizure medications (ASMs) or non-pharmaceutical approaches, can take direct or indirect influence on sleep in patients with epilepsy. Here, we systematically review the effect of anti-seizure treatments on sleep. ASMs targeting at different sites exerted various effects on both sleep structure and sleep quality. Non-pharmaceutical treatments including resective surgery, ketogenic diet, and transcranial magnetic stimulation appear to have a positive effect on sleep, while vagus nerve stimulation, deep brain stimulation, and brain-responsive neurostimulation are likely to interrupt sleep and exacerbate sleep-disordered breathing. The potential mechanisms underlying how non-pharmacological approaches affect sleep are also discussed. The limitation of most studies is that they were largely based on small cohorts by short-term observations. Further well-designed and large-scale investigations in this field are warranted. Understanding the effect of anti-seizure therapies on sleep can guide clinicians to optimize epilepsy treatment in the future.

Symptom rates and profile clustering in tuberous sclerosis complex-associated neuropsychiatric disorders (TAND)

BACKGROUND

Tuberous Sclerosis Complex (TSC) is associated with a range of neuropsychiatric difficulties, appropriately termed TSC-Associated Neuropsychiatric Disorders (TAND). The objectives of the study were to analyze the rates of TAND symptoms in a cohort of patients seen at the TSC Center of Excellence at Cincinnati Children's Hospital and to identify clinically meaningful profiles based on TAND symptoms.

METHODS

Data from the TAND Checklist was obtained from participants seen at the TSC Center of Excellence at Cincinnati Children's Hospital Medical Center from June 2015 to August 2018. Cluster and factor analyses for each TAND symptom were performed. Factor scores were then calculated for participants, and a K-means cluster analysis of these scores was used to empirically identify distinct overall TAND symptom profiles occurring in TSC.

RESULTS

A total of 1545 checklists was completed for 668 participants (37% adults and 63% children). Approximately 90% of participants reported at least one TAND symptom with an average of 12 symptoms (out of 29). Symptom rates ranged between 5 and 60%. The most common symptoms were neuropsychologic symptoms. A seven-cluster and seven-factor solution were found to be optimal. K-means cluster analysis resulted in a seven-profile solution, ranging from low to high symptom burden.

CONCLUSION

This study is the first to identify natural phenotypic profiles of TAND symptoms. Study of specific TAND subpopulations with shared profiles may facilitate better understanding of the underlying biology of TAND and better assessment of more targeted treatments.

Sleep fragmentation and decreased REM sleep in a primate model of diurnal cortical seizures

Many people with epilepsy suffer from comorbid sleep disorders and sleep fragmentation. While the disruptive nature of seizures on sleep is well documented, it is unclear how diurnal seizures impact sleep quality and for how long these changes persist during the following nights. To better understand this relationship, the sleep architecture of two rhesus macaques were studied before and several nights after penicillin-induced diurnal seizures. These focal seizures stopped naturally, and none occurred at night. We scored sleep-stage during the nights immediately following the seizures, as well as several nights after seizure induction. We noted a significant increase in movement along with a decrease in sleep efficiency, both limited to the night of seizure induction. For both animals, we observed a significant decrease in the number of REM periods that manifested as a decrease in total REM sleep duration, and this phenomenon persisted up to 2 nights after the seizures. We also found a significant increase in the probability to transition from stage N2 to stage N1 on the night of the seizures. This study shows for the first time that the NHP model of penicillin-induced cortical seizures exhibits significant changes in sleep architecture, including an increase in nocturnal movement, change in sleep architecture and a prolonged decrease in REM activity. The prolonged decrease in REM periods compared to the temporary enhanced movement and reduction of sleep efficiency suggest that these seizures may affect two neural circuits, one controlling REM sleep entry and the other controlling nocturnal wakefulness.

Measuring Sleep, Wakefulness, and Circadian Functions in Neurologic Disorders

Neurologic disorders impact the ability of the brain to regulate sleep, wake, and circadian functions, including state generation, components of state (such as rapid eye movement sleep muscle atonia, state transitions) and electroencephalographic microarchitecture. At its most extreme, extensive brain damage may even prevent differentiation of sleep stages from wakefulness (eg, status dissociatus). Given that comorbid sleep-wake-circadian disorders are common and can adversely impact the occurrence, evolution, and management of underlying neurologic conditions, new technologies for long-term monitoring of neurologic patients may potentially usher in new diagnostic strategies and optimization of clinical management.