Involvement of the contralateral hippocampus in ictal-like but not interictal epileptic activities in the kainate mouse model of temporal lobe epilepsy.

OBJECTIVE: Animal and human studies have shown that the seizure-generating region is vastly dependent on distant neuronal hubs that can decrease duration and propagation of ongoing seizures. However, we still lack a comprehensive understanding of the impact of distant brain areas on specific interictal and ictal epileptic activities (e.g., isolated spikes, spike trains, seizures). Such knowledge is critically needed, because all kinds of epileptic activities are not equivalent in terms of clinical expression and impact on the progression of the disease. METHODS: We used surface high-density electroencephalography and multisite intracortical recordings, combined with pharmacological silencing of specific brain regions in the well-known kainate mouse model of temporal lobe epilepsy. We tested the impact of selective regional silencing on the generation of epileptic activities within a continuum ranging from very transient to more sustained and long-lasting discharges reminiscent of seizures. RESULTS: Silencing the contralateral hippocampus completely suppresses sustained ictal activities in the focus, as efficiently as silencing the focus itself, but whereas focus silencing abolishes all focus activities, contralateral silencing fails to control transient spikes. In parallel, we observed that sustained focus epileptiform discharges in the focus are preceded by contralateral firing and more strongly phase-locked to bihippocampal delta/theta oscillations than transient spiking activities, reinforcing the presumed dominant role of the contralateral hippocampus in promoting long-lasting, but not transient, epileptic activities. SIGNIFICANCE: Altogether, our work provides suggestive evidence that the contralateral hippocampus is necessary for the interictal to ictal state transition and proposes that crosstalk between contralateral neuronal activity and ipsilateral delta/theta oscillation could be a candidate mechanism underlying the progression from short- to long-lasting epileptic activities.

Thalidomide Attenuates Epileptogenesis and Seizures by Decreasing Brain Inflammation in Lithium Pilocarpine Rat Model.

Thalidomide (TAL) has shown potential therapeutic effects in neurological diseases like epilepsy. Both clinical and preclinical studies show that TAL may act as an antiepileptic drug and as a possible treatment against disease development. However, the evidence for these effects is limited. Therefore, the antiepileptogenic and anti-inflammatory effects of TAL were evaluated herein. Sprague Dawley male rats were randomly allocated to one of five groups (n = 18 per group): control (C); status epilepticus (SE); SE-TAL (25 mg/kg); SE-TAL (50 mg/kg); and SE-topiramate (TOP; 60mg/kg). The lithium-pilocarpine model was used, and one day after SE induction the rats received pharmacological treatment for one week. The brain was obtained, and the hippocampus was micro-dissected 8, 18, and 28 days after SE. TNF-α, IL-6, and IL-1ß concentrations were quantified. TOP and TAL (50 mg/kg) increased the latency to the first of many spontaneous recurrent seizures (SRS) and decreased SRS frequency, as well as decreasing TNF-α and IL-1ß concentrations in the hippocampus. In conclusion, the results showed that both TAL (50 mg/kg) and TOP have anti-ictogenic and antiepileptogenic effects, possibly by decreasing neuroinflammation.

Ictogenesis of viral pneumonia: A comparison between SARS-CoV-2 and H1N1/H3N2.

Several studies reported acute symptomatic seizures as a possible neurological complication of COVID-19 pneumonia. Apart from metabolic imbalances, hypoxia, and fever, other ictogenic mechanisms are likely related to an immune-mediated damage. The same mechanisms are shared by other respiratory viruses. Since neurotropic properties of SARS-CoV-2 have been questioned, we investigated whether SARS-CoV-2 has a similar ictogenic potential to other respiratory non-neurotropic viruses. We conducted a retrospective study identifying 1141 patients with SARS-CoV-2 pneumonia and 146 patients with H1N1/H3N2 pneumonia. We found a similar prevalence of seizures in the two viral pneumonia (1.05% with SARS-CoV-2 vs 2.05% with influenza; p = 0.26). We detailed clinical, electroencephalographic, and neuroradiological features of each patient, together with the hypothesized pathogenesis of seizures. Previous epilepsy or pre-existing predisposing conditions (i.e., Alzheimer's disease, stroke, cerebral neoplasia) were found in one-third of patients that experienced seizures, while two-thirds of patients had seizures without known risk factors other than pneumonia in both groups. The prevalence of pre-existing predisposing conditions and disease severity indexes was similar in SARS-CoV-2 and H1N1/H3N2 pneumonia, thus excluding they could act as potential confounders. Considering all the patients with viral pneumonia together, previous epilepsy (p < 0.001) and the need for ventilatory support (p < 0.001), but not the presence of pre-existing predisposing conditions (p = 0.290), were associated with seizure risk. Our study showed that SARS-CoV-2 and influenza viruses share a similar ictogenic potential. In both these infections, seizures are rare but serious events, and can manifest without pre-existing predisposing conditions, in particular when pneumonia is severe, thus suggesting an interplay between disease severity and host response as a major mechanism of ictogenesis, rather than a virus-specific mechanism.

The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures.

Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.

A Model-Based Assessment of the Seizure Onset Zone Predictive Power to Inform the Epileptogenic Zone.

Epilepsy surgery is a clinical procedure that aims to remove the brain tissue responsible for the emergence of seizures, the epileptogenic zone (EZ). It is preceded by an evaluation to determine the brain tissue that must be resected. The identification of the seizure onset zone (SOZ) from intracranial EEG recordings stands as one of the key proxies for the EZ. In this study we used computational models of epilepsy to assess to what extent the SOZ may or may not represent the EZ. We considered a set of different synthetic networks (e.g., regular, small-world, random, and scale-free networks) to represent large-scale brain networks and a phenomenological network model of seizure generation. In the model, the SOZ was inferred from the seizure likelihood (SL), a measure of the propensity of single nodes to produce epileptiform dynamics, whilst a surgery corresponded to the removal of nodes and connections from the network. We used the concept of node ictogenicity (NI) to quantify the effectiveness of each node removal on reducing the network's propensity to generate seizures. This framework enabled us to systematically compare the SOZ and the seizure control achieved by each considered surgery. Specifically, we compared the distributions of SL and NI across different networks. We found that SL and NI were concordant when all nodes were similarly ictogenic, whereas when there was a small fraction of nodes with high NI, the SL was not specific at identifying these nodes. We further considered networks with heterogeneous node excitabilities, i.e., nodes with different susceptibilities of being engaged in seizure activity, to understand how such heterogeneity may affect the relationship between SL and NI. We found that while SL and NI are concordant when there is a small fraction of hyper-excitable nodes in a network that is otherwise homogeneous, they do diverge if the network is heterogeneous, such as in scale-free networks. We observe that SL is highly dependent on node excitabilities, whilst the effect of surgical resections as revealed by NI is mostly determined by network structure. Together our results suggest that the SOZ is not always a good marker of the EZ.

Elevated Ictal Brain Network Ictogenicity Enables Prediction of Optimal Seizure Control.

Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.