Dynamic Changes in Spectral and Spatial Signatures of High Frequency Oscillations in Rat Hippocampi during Epileptogenesis in Acute and Chronic Stages

MiR-23b-3p Improves Brain Damage after Status Epilepticus by Reducing the Formation of Pathological High-Frequency Oscillations via Inhibition of cx43 in Rat Hippocampus

In order to investigate the effectiveness and safety of miR-23b-3p in anti-seizure activity and to elucidate the regulatory relationship between miR-23b-3p and Cx43 in the nervous system, we have established a lithium chloride-pilocarpine (PILO) status epilepticus (SE) model. Rats were randomly divided into the following groups: seizure control (PILO), valproate sodium (VPA+PILO), recombinant miR-23b-3p overexpression (miR+PILO), miR-23b-3p sponges (Sponges+PILO), and scramble sequence negative control (Scramble+PILO) (n = 6/group). After experiments, we got the following results. In the acute phase, the time required for rats to reach stage IV after PILO injection was significantly longer in VPA+PILO and miR+PILO. In the chronic phase after SE, the frequency of spontaneous recurrent seizures (SRSs) in VPA+PILO and miR+PILO was significantly reduced. At 10 min before seizure cessation, the average energy expression of fast ripples (FRs) in VPA+PILO and miR+PILO was significantly lower than in PILO. After 28 days of seizure, Cx43 expression in PILO was significantly increased, and Beclin1expression in all groups was significantly increased. After 28 days of SE,the number of synapses in the CA1 region of the hippocampus was significantly higher in the VPA+PILO and miR+PILO groups compared to that in the PILO group. After 28 days of SE ,hippocampal necrotic cells in the CA3 region were significantly lower in the VPA+PILO and miR+PILO groups compared to those in the PILO group. There were no significant differences in biochemical indicators among the experimental group rats 28 days after SE compared to the seizure control group. Based on the previous facts, we can reach the conclusion that MiR-23b-3p targets and blocks the expression of hippocampal Cx43 which can reduce the formation of pathological FRs, thereby alleviating the severity of seizures, improving seizure-induced brain damage.

The prospective study of 54 children with electrical status epilepticus during sleep: How to simplify the electroencephalogram diagnosis and guide the treatment

OBJECTIVE

To simplify the electroencephalogram (EEG) diagnosis and guide the treatment of electrical status epilepticus during sleep (ESES).

METHODS

We recruited 54 children with ESES from December 2019 to December 2020 and compared various spike-wave index (SWI) calculation methods. Time-frequency analysis assessed the correlation between high-frequency oscillations energy and the SWI. We divided 42 children into responder and non-responder treatment groups based on the observations made during a 12-month follow-up period and evaluate different treatment and the independent risk factors of refractory ESES.

RESULTS

The SWI of 5 min before the second sleep cycle of non-rapid eye movement (NREM; long method II) and that of all NREM sleep (total method) were not significantly different (p = .06). The average energy of γ (r = .288, p = .002) and ripple (r = .203, p = .04) oscillations were correlated with the SWI. Multivariable logistic regression analysis showed that encephalomalacia was an independent risk factor for refractory ESES (OR: 10.48, 95% CI: 1.62-67.63). The clinical seizure improvement rates of anti-seizure medications (ASMs), ASMs with benzodiazepines, and ASMs with benzodiazepines and steroids after 12 months were 9.3%, 42.8%, and 53.8%, EEG improvement rate were 5.5%, 30.9% and 37%, respectively. The intelligence of the children in the responder treatment group has improved during the 1-year follow-up.

SIGNIFICANCE

These findings demonstrate EEG and clinical features of ESES and may provide basis for simplifying diagnosis and guiding the treatment of children with ESES.

Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages

Introduction

The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky.

Methods

In this paper, five popular complexity indicators of EEG signal, including approximate entropy, sample entropy, permutation entropy, fuzzy entropy and Kolmogorov complexity, are computed from rat hippocampi to characterize the normal, acute, and chronic phases during epileptogenesis. Results of one-way ANOVA and principal component analysis both show that utilizing complexity features, we are able to easily identify differences between normal, acute, and chronic phases. We also propose an innovative framework for epilepsy detection based on graph convolutional neural network (GCNN) using multi-channel EEG complexity as input.

Results

Combining information of five complexity measures at eight channels, our GCNN model demonstrate superior ability in recognizing the normal, acute, and chronic phases. Experiments results show that our GCNN model reached the high prediction accuracy above 98% and F1 score above 97% among these three phases for each individual rat.

Discussion

Our research practice based on real data shows that EEG complexity characteristics are of great significance for recognizing different stages of epilepsy.

Dynamic effects of miR-20a-5p on hippocampal ripple energy after status epilepticus in rats

To determine the dynamic effects of miR-20a-5p on hippocampal ripple energy in rats after status epilepticus (SE). A lithium pilocarpine (LiCl-PILO)-induced rat model of status epilepticus (SE) was established, and the rats were divided into the normal control (Control, CTL), epileptic control (PILO), valproic acid (VPA + PILO), miR-20a-5p overexpression lentivirus vector (miR + PILO), sponges blocking lentivirus vector (Sponges + PILO), and scramble sequence negative control (Scramble + PILO) groups (n = 6). Electroencephalograms (EEGs) were used to analyze changes in hippocampal ripple energy before and after SE. Quantitative polymerase chain reaction (q-PCR) analysis showed that miR-20a-5p levels gradually increased after miR-20a-5p overexpression lentivirus vector injection into the lateral ventricle, and the miR-20a-5p levels were significantly higher than that in CTL group on days 7 and 36 (P < 0.001). The miR-20a-5p levels decreased significantly on days 7 and 36 after blocking by sponges lentivirus vector injected into the lateral ventricle (P < 0.001). After injection of PILO, the average ripple energy expression in each group gradually increased, and reached the peak before chloral hydrate injection (compared with 1 day before SE, P < 0.05). The ripple energy in the VPA + PILO and Sponges + PILO groups was significantly lower than that in the PILO group at 60 min and 70 min after PILO injection and before chloral hydrate injection (P < 0.05), and maintained lower until 2 h after chloral hydrate injection in VPA + PILO (P < 0.05). Compared with the VPA + PILO group, the mean ripple energy of the Sponges + PILO group had no difference at all time points (P ≥ 0.05). After SE, ripple distribution of space and energy is closely related to the occurrence of epilepsy. Inhibition of miR20a-5p expression can downregulate ripple oscillation energy during seizure.

High-Frequency Oscillations and Epileptogenic Network

Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.

Anticonvulsant Effect of Carbenoxolone on Chronic Epileptic Rats and Its Mechanism Related to Connexin and High-Frequency Oscillations

Objective

This study was designed to investigate the influence and mechanism of gap junction carbenoxolone (CBX) on dynamic changes in the spectral power of ripples and fast ripples (FRs) in the hippocampus of chronic epileptic rats.

Methods

The lithium-pilocarpine (PILO) status epilepticus (SE) model (PILO group) and the CBX pretreatment model (CBX + PILO group) were established to analyze dynamic changes in the spectral power of ripples and FRs, and the dynamic expression of connexin (CX)26, CX32, CX36, and CX43 in the hippocampus of chronic epileptic rats.

Results

Within 28 days after SE, the number of spontaneous recurrent seizures (SRSs) in the PILO group was significantly higher than that in the CBX + PILO group. The average spectral power of FRs in the PILO group was significantly higher than the baseline level at 1 and 7 days after SE. The average spectral power of FRs in the PILO group was significantly higher than that in the CBX + PILO group at 1, 7, and 14 days after SE. Seizures induced an increase in CX43 expression at 1 and 7 days after SE, but had no significant effect on CX26, CX36, or CX32. CBX pretreatment did not affect the expression of CXs in the hippocampus of normal rats, but it inhibited the expression of CX43 in epileptic rats. The number of SRSs at 2 and 4 weeks after SE had the highest correlation with the average spectral power of FRs; the average spectral power of FRs was moderately correlated with the expression of CX43.

Conclusion

The results of this study indicate that the energy of FRs may be regulated by its interference with the expression of CX43, and thus, affect seizures. Blocking the expression of CX43 thereby reduces the formation of pathological high-frequency oscillations (HFOs), making it a promising strategy for the treatment of chronic epilepsy.

Effect of gap junction blockers on hippocampal ripple energy expression in rats with status epilepticus

OBJECTIVES

To study the effect of gap junction blockers, quinine (QUIN) and carbenoxolone (CBX), on hippocampal ripple energy expression in rats with status epilepticus (SE).

METHODS

A total of 24 rats were randomly divided into four groups: model, QUIN, valproic acid (VPA), and CBX (n=6 each). A rat model of SE induced by lithium-pilocarpine (PILO) was prepared. The QUIN, VPA, and CBX groups were given intraperitoneal injection of QUIN (50 mg/kg), VPA by gavage (200 mg/kg), and intraperitoneal injection of CBX (50 mg/kg) respectively, at 3 days before PILO injection. Electroencephalography was used to analyze the change in hippocampal ripple energy before and after modeling, as well as before and after chloral hydrate injection to control seizures.

RESULTS

Ripple expression was observed in the hippocampal CA1, CA3, and dentate gyrus regions of normal rats. After 10 minutes of PILO injection, all groups had a gradual increase in mean ripple energy expression compared with 1 day before modeling, with the highest expression level before chloral hydrate injection in the model, VPA and CBX groups (P<0.05). The QUIN group had the highest expression level of mean ripple energy 60 minutes after PILO injection. The mean ripple energy returned to normal levels in the three intervention groups immediately after chloral hydrate injection, while in the model group, the mean ripple energy returned to normal levels 1 hour after chloral hydrate injection. The mean ripple energy remained normal till to day 3 after SE in the four groups. The changing trend of maximum ripple energy was similar to that of mean ripple energy.

CONCLUSIONS

The change in ripple energy can be used as a quantitative indicator for early warning of seizures, while it cannot predict seizures in the interictal period. Gap junction blockers can reduce ripple energy during seizures.

Application of High-Frequency Oscillations on Scalp EEG in Infant Spasm: A Prospective Controlled Study

Objective

We quantitatively analyzed high-frequency oscillations (HFOs) using scalp electroencephalography (EEG) in patients with infantile spasms (IS).

Methods

We enrolled 60 children with IS hospitalized from January 2019 to August 2020. Sixty healthy age-matched children comprised the control group. Time-frequency analysis was used to quantify γ, ripple, and fast ripple (FR) oscillation energy changes.

Results

γ, ripple, and FR oscillations dominated in the temporal and frontal lobes. The average HFO energy of the sleep stage is lower than that of the wake stage in the same frequency bands in both the normal control (NC) and IS groups (P < 0.05). The average HFO energy of the IS group was significantly higher than that of the NC group in γ band during sleep stage (P < 0.01). The average HFO energy of S and Post-S stage were higher than that of sleep stage in γ band (P < 0.05). In the ripple band, the average HFO energy of Pre-S, S, and Post-S stage was higher than that of sleep stage (P < 0.05). Before treatment, there was no significant difference in BASED score between the effective and ineffective groups. The interaction of curative efficacy × frequency and the interaction of curative efficacy × state are statistically significant. The average HFO energy of the effective group was lower than that of the ineffective group in the sleep stage (P < 0.05). For the 16 children deemed "effective" in the IS group, the average HFO energy of three frequency bands was not significantly different before compared with after treatment.

Significance

Scalp EEG can record HFOs. The energy of HFOs can distinguish physiological HFOs from pathological ones more accurately than frequency. On scalp EEG, γ oscillations can better detect susceptibility to epilepsy than ripple and FR oscillations. HFOs can trigger spasms. The analysis of average HFO energy can be used as a predictor of the effectiveness of epilepsy treatment.

Detecting High Frequency Oscillations for Stereoelectroencephalography in Epilepsy via Hypergraph Learning

Successful epilepsy surgeries depend highly on pre-operative localization of epileptogenic zones. Stereoelectroencephalography (SEEG) records interictal and ictal activities of the epilepsy in order to precisely find and localize epileptogenic zones in clinical practice. While it is difficult to find distinct ictal onset patterns generated the seizure onset zone from SEEG recordings in a confined region, high frequency oscillations are commonly considered as putative biomarkers for the identification of epileptogenic zones. Therefore, automatic and accurate detection of high frequency oscillations in SEEG signals is crucial for timely clinical evaluation. This work formulates the detection of high frequency oscillations as a signal segment classification problem and develops a hypergraph-based detector to automatically detect high frequency oscillations such that human experts can visually review SEEG signals. We evaluated our method on 4,000 signal segments from clinical SEEG recordings that contain both ictal and interictal data obtained from 19 patients who suffer from refractory focal epilepsy. The experimental results demonstrate the effectiveness of the proposed detector that can successfully localize interictal high frequency oscillations and outperforms multiple peer machine learning methods. In particular, the proposed detector achieved 90.7% in accuracy, 80.9% in sensitivity, and 96.9% in specificity.

Catalpol Exerts an Anti-Epilepticus Effect, Possibly by Regulating the Nrf2-Keap1-ARE Signaling Pathway

Background

Status epilepticus (SE) is a refractory neurological disease with high mortality and morbidity rates. SE can be induced by numerous factors, including oxidative stress. Catalpol has several biological activities, including regulating the oxidative stress response. However, the role of catapol in SE has not been fully elucidated.

Material/Methods

Thirty Wistar rats were randomly and equally divided into 3 groups: a control group, an SE group established by LiCl-pilocarpine intraperitoneal injection, and an SE+catalpol group established administering catalpol to SE rats. Epileptic seizure level and after-discharge duration (ADD) were analyzed. Cognitive function was assessed by Morris water maze. Myeloperoxidase (MPO) and superoxide dismutase (SOD) activities were tested. Keap1 and ARE mRNA expressions were detected by real-time PCR. Nrf2 protein expression was determined by Western blot.

Results

Catalpol significantly decreased epileptic seizure level, extended ADD, and improved cognitive function compared with the SE group (P<0.05). MPO was increased, SOD was reduced, Keap1 mRNA was upregulated, and Nrf2 protein and ARE mRNA were reduced in the SE group compared with the control group (P<0.05). Catalpol markedly decreased MPO, enhanced SOD activity, decreased Keap1 mRNA level, and elevated Nrf2 protein and ARE mRNA expressions compared with the SE group (P<0.05).

Conclusions

Catalpol plays an anti-epileptic role and improves cognitive function by regulating the Nrf2-Keap1-ARE signaling pathway to inhibit oxidative stress response.

A new comorbidity model and the common pathological mechanisms of migraine and epilepsy

A bi-directional relationship between epilepsy and migraine has been widely reported in epidemiological and clinical studies, but the mechanisms of interaction between these disorders have not been fully examined using animal models. The aim of the present study was to develop a new comorbidity model of migraine and epilepsy. Nociception was induced by applying an inflammatory soup to the dura mater; this procedure resulted in nociception similar to that expressed in inflammatory disorders such as migraine. We showed that nociceptive behaviors (head rubbing, including with the forepaws and hindpaws) were significantly enhanced in comorbidity rats compared to sham-operation (Sham) rats, and these nociceptive behaviors were correlated with epilepsy-like behaviors. The plasma levels of calcitonin gene-related peptide (CGRP) significantly increased in the migraine group compared with the sham group, but CGRP did not further increase in comorbidity rats compared with migraine rats. C-Fos immunoreactive area values were higher in the comorbidity group than in the migraine group in the trigeminal nucleus caudalis (Sp5C) area (P<0.05). There was no significant difference in HSP70 expression between the comorbidity group and the epilepsy group. The expression of brain-derived neurotrophic factor (BDNF) in the CA3 area of the hippocampus in the comorbidity group was significantly higher than that in the epilepsy group according to the immunohistochemical test (P<0.05). In conclusion, our findings indicate that pilocarpine-induced epilepsy exacerbates inflammatory nociception, which can aggravate seizure severity. Moreover, we established a new comorbidity model for migraine and epilepsy.