Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy (MORE)

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.

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.

The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy

OBJECTIVE

Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics.

METHODS

We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included.

RESULTS

The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51).

INTERPRETATION

In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.

From phenomenological to biophysical models of seizures

Epilepsy is a complex disease that requires various approaches for its study. In this short review, we discuss the contribution of theoretical and computational models. The review presents theoretical frameworks that underlie the understanding of certain seizure properties and their classification based on their dynamical properties at the onset and offset of seizures. Dynamical system tools are valuable resources in the study of seizures. By analyzing the complex, dynamic behavior of seizures, these tools can provide insights into seizure mechanisms and offer a framework for their classification. Additionally, computational models have high potential for clinical applications, as they can be used to develop more accurate diagnostic and personalized medicine tools. We discuss various modeling approaches that span different scales and levels, while also questioning the neurocentric view, and emphasize the importance of considering glial cells. Finally, we explore the epistemic value provided by this type of approach.

Common multi-day rhythms in smartphone behavior

The idea that abnormal human activities follow multi-day rhythms is found in ancient beliefs on the moon to modern clinical observations in epilepsy and mood disorders. To explore multi-day rhythms in healthy human behavior our analysis includes over 300 million smartphone touchscreen interactions logging up to 2 years of day-to-day activities (N401 subjects). At the level of each individual, we find a complex expression of multi-day rhythms where the rhythms occur scattered across diverse smartphone behaviors. With non-negative matrix factorization, we extract the scattered rhythms to reveal periods ranging from 7 to 52 days - cutting across age and gender. The rhythms are likely free-running - instead of being ubiquitously driven by the moon - as they did not show broad population-level synchronization even though the sampled population lived in northern Europe. We propose that multi-day rhythms are a common trait, but their consequences are uniquely experienced in day-to-day behavior.

Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research

Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.

Sleep Disruption Worsens Seizures: Neuroinflammation as a Potential Mechanistic Link

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

Cycles in epilepsy

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

The Kainic Acid Models of Temporal Lobe Epilepsy

Experimental models of epilepsy are useful to identify potential mechanisms of epileptogenesis, seizure genesis, comorbidities, and treatment efficacy. The kainic acid (KA) model is one of the most commonly used. Several modes of administration of KA exist, each producing different effects in a strain-, species-, gender-, and age-dependent manner. In this review, we discuss the advantages and limitations of the various forms of KA administration (systemic, intrahippocampal, and intranasal), as well as the histologic, electrophysiological, and behavioral outcomes in different strains and species. We attempt a personal perspective and discuss areas where work is needed. The diversity of KA models and their outcomes offers researchers a rich palette of phenotypes, which may be relevant to specific traits found in patients with temporal lobe epilepsy.