Epilepsy affects about 70 million people in the world. Every year, approximately 2.4 million people are diagnosed with epilepsy, two-thirds of them will not know the etiology of their disease, and 1% of these individuals will decease as a consequence of it. Due to the inherent complexity of predicting and explaining it, the mathematical model Epileptor was recently developed to reproduce seizure-like events, also providing insights to improve the understanding of the neural dynamics in the interictal and ictal periods, although the physics behind each parameter and variable of the model is not fully established in the literature. This paper introduces an approach to design a feedback-based controller for suppressing epileptic seizures described by Epileptor. Our work establishes how the nonlinear dynamics of this disorder can be written in terms of a combination of linear sub-models employing an exact solution. Additionally, we show how a feedback control gain can be computed to suppress seizures, as well as how specific shapes applied as input stimuli for this purpose can be obtained. The practical application of the approach is discussed and the results show that the proposed technique is promising for developing controllers in this field.
The types of epileptiform activity occurring in the sclerotic hippocampus with highest incidence are interictal-like events (II) and periodic ictal spiking (PIS). These activities are classified according to their event rates, but it is still unclear if these rate differences are consequences of underlying physiological mechanisms. Identifying new and more specific information related to these two activities may bring insights to a better understanding about the epileptogenic process and new diagnosis. We applied Poincaré map analysis and Recurrence Quantification Analysis (RQA) onto 35 in vitro electrophysiological signals recorded from slices of 12 hippocampal tissues surgically resected from patients with pharmacoresistant temporal lobe epilepsy. These analyzes showed that the II activity is related to chaotic dynamics, whereas the PIS activity is related to deterministic periodic dynamics. Additionally, it indicates that their different rates are consequence of different endogenous dynamics. Finally, by using two computational models we were able to simulate the transition between II and PIS activities. The RQA was applied to different periods of these simulations to compare the recurrences between artificial and real signals, showing that different ranges of regularity-chaoticity can be directly associated with the generation of PIS and II activities.
Epilepsy is one of the most common brain disorders worldwide, affecting millions of people every year. Although significant effort has been put into better understanding it and mitigating its effects, the conventional treatments are not fully effective. Advances in computational neuroscience, using mathematical dynamic models that represent brain activities at different scales, have enabled addressing epilepsy from a more theoretical standpoint. In particular, the recently proposed Epileptor model stands out among these models, because it represents well the main features of seizures, and the results from its simulations have been consistent with experimental observations. In addition, there has been an increasing interest in designing control techniques for Epileptor that might lead to possible realistic feedback controllers in the future. However, such approaches rely on knowing all of the states of the model, which is not the case in practice. The work explored in this letter aims to develop a state observer to estimate Epileptor's unmeasurable variables, as well as reconstruct the respective so-called bursters. Furthermore, an alternative modeling is presented for enhancing the convergence speed of an observer. The results show that the proposed approach is efficient under two main conditions: when the brain is undergoing a seizure and when a transition from the healthy to the epileptiform activity occurs.
Modulation of brain activity is one of the main mechanisms capable of demonstrating the synchronization dynamics of neural oscillations. in epilepsy, modulation is a key concept since seizures essentially result from neural hypersynchronization and hyperexcitability. in this study, we have introduced a time-dependent index based on the Kullback-Leibler divergence to quantify the effects of phase and frequency modulations of neural oscillations in neonatal mice exhibiting epileptiform activity induced by Zika virus (ZiKV) infection. through this index, we demonstrate that fast oscillations (gamma and beta 2) are the more susceptible modulated rhythms in terms of phase, during seizures, whereas slow waves (delta and theta) mainly undergo changes in frequency. the index also allowed detection of specific patterns associated with the interdependent modulation of phase and frequency in neural activity. furthermore, by comparing ZiKV modulations with the general computational model Epileptors, we verify different signatures related to the brain rhythms modulation in phase and frequency. These findings instigate new studies on the effects of ZIKV infection on neuronal networks from electrophysiological activities, and how different mechanisms can trigger epilepsy.Zika virus (ZIKV) is an arbovirus from the Flaviviridae family, which was first reported in 1947 in Uganda 1 . It is mainly transmitted by the Aedes aegypti mosquitoes, but can also be transmitted sexually and by blood transfusion from an infected donor 2,3 . Zika has been considered as an emergent health threat, given its epidemic history, transmission in tropical areas 4 , neurological, congenital diseases 5,6 , as well as given the fact that it is associated with brain abnormalities in newborns Rasmussen et al. 8 ). Recent reports show that epileptic seizures are among the main neurological outcomes of congenital Zika syndrome (CZS) 9-13 ; additionally, reports of the incidence of epileptic seizures in infants exposed to the ZIKV and who had not developed microcephaly 14 , represent new challenges due to changes in the neurodevelopmental stages and even their long-term consequences.Epilepsy is one of the most common neurological disorders worldwide 15 , clinically characterized by the occurrence of at least two unprovoked seizures in less than 24 hours, high unprovoked seizure recurrence risk, or even by the diagnosis of epilepsy syndrome 16 . Despite important advances in the understanding of the involved pathophysiology, multiple mechanisms behind the hyperexcitability and hypersynchronization of neurons during epileptic seizures, still need to be understood better. For instance, the role of neuronal discharge modulation during seizures and their relationship with epileptogenesis is not yet completely elucidated 17 . A physical approach
Background: Clinical and pre-clinical studies indicate a reduction in seizure frequency as well as a decrease in susceptibility to subsequently evoked seizures after physical exercise programs. In contrast to the influence of exercise after epilepsy previously established, various studies have been conducted attempting to investigate whether physical activity reduces brain susceptibility to seizures or prevents epilepsy. We report a systematic review and meta-analysis of different animal models that addressed the impact of previous physical exercise programs to reduce seizure susceptibility.Methods: We included animal model (rats and mice) studies before brain insult that reported physical exercise programs compared with other interventions (sham, control, or naïve). We excluded studies that investigated animal models after brain insult, associated with supplement nutrition or drugs, that did not address epilepsy or seizure susceptibility, ex vivo studies, in vitro studies, studies in humans, or in silico studies. Electronic searches were performed in the MEDLINE (PubMed), Web of Science (WOS), Scopus, PsycINFO, Scientific Electronic Library Online (SciELO) databases, and gray literature, without restrictions to the year or language of publication. We used SYRCLE's risk of bias tool and CAMARADES checklist for study quality. We performed a synthesis of results for different types of exercise and susceptibility to seizures by random-effects meta-analysis.Results: Fifteen studies were included in the final analysis (543 animals), 13 of them used male animals, and Wistar rats were the most commonly studied species used in the studies (355 animals). The chemoconvulsants used in the selected studies were pentylenetetrazol, penicillin, kainic acid, pilocarpine, and homocysteine. We assessed the impact of study design characteristics and the reporting of mitigations to reduce the risk of bias. We calculated a standardized mean difference effect size for each comparison and performed a random-effects meta-analysis. The meta-analysis included behavioral analysis (latency to seizure onset, n = 6 and intensity of motor signals, n = 3) and electrophysiological analysis (spikes/min, n = 4, and amplitude, n = 6). The overall effect size observed in physical exercise compared to controls for latency to seizure onset was −130.98 [95% CI: −203.47, −58.49] (seconds) and the intensity of motor signals was −0.40 [95% CI: −1.19, 0.40] (on a scale from 0 to 5). The largest effects were observed in electrophysiological analysis for spikes/min with −26.96 [95% CI: −39.56, −14.36], and for spike amplitude (μV) with −282.64 [95% CI: −466.81, −98.47].Discussion:Limitations of evidence. A higher number of animal models should be employed for analyzing the influence of exerciseon seizure susceptibility. The high heterogeneity in our meta-analysis is attributable to various factors, including the number of animals used in each study and the limited number of similar studies. Interpretation. Studies selected in this systematic review and meta-analysis suggest that previous physical exercise programs can reduce some of the main features related to seizure susceptibility [latency seizure onset, spikes/min, and spike amplitude (μV)] induced by the administration of different chemoconvulsants.Systematic Review Registration: PROSPERO, identifier CRD42021251949; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=251949.
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