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

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.

Distinct profiles of cerebral oxygenation in focal vs. secondarily generalized EEG seizures in children undergoing cardiac surgery

Objectives

Seizures are common in children undergoing cardiopulmonary bypass (CPB). Cerebral oxygen saturation (ScO2) by near-infrared spectroscopy is routinely monitored in many centers, but the relations between the levels and changes of ScO2 and brain injuries remain incompletely understood. We aimed to analyze the postoperative profiles of ScO2 and cerebral blood flow velocity in different types of EEG seizures in relation to brain injuries on MRI.

Methods

We monitored continuous EEG and ScO2 in 337 children during the first 48 h after CPB, which were analyzed in 3 h periods. Cerebral blood flow peak systolic velocity (PSV) in the middle cerebral artery was measured daily by transcranial Doppler. Postoperative cerebral MRI was performed before hospital discharge.

Results

Based on the occurrence and spreading types of seizures, patients were divided into three groups as patients without seizures (Group N; n = 309), those with focal seizures (Group F; n = 13), or with secondarily generalized seizures (Group G; n = 15). There were no significant differences in the onset time and duration of seizures and incidence of status epilepticus between the two seizures groups (Ps ≥ 0.27). ScO2 increased significantly faster across Group N, Group G, and Group F during the 48 h (p < 0.0001) but its overall levels were not significantly different among the three groups (p = 0.30). PSV was significantly lower (p = 0.003) but increased significantly faster (p = 0.0003) across Group N, Group G, and Group F. Group F had the most severe brain injuries and the highest incidence of white matter injuries on MRI among the three groups (Ps ≤ 0.002).

Conclusion

Postoperative cerebral oxygenation showed distinct profiles in secondarily generalized and particularly focal types of EEG seizures in children after CPB. A state of 'overshooting' ScO2 with persistently low PSV was more frequently seen in those with focal seizures and more severe brain injury. Information from this study may have important clinical implications in detecting brain injuries when monitoring cerebral oxygenation in this vulnerable group of children after CPB.

Virus-Induced Epilepsy vs. Epilepsy Patients Acquiring Viral Infection: Unravelling the Complex Relationship for Precision Treatment

The intricate relationship between viruses and epilepsy involves a bidirectional interaction. Certain viruses can induce epilepsy by infecting the brain, leading to inflammation, damage, or abnormal electrical activity. Conversely, epilepsy patients may be more susceptible to viral infections due to factors, such as compromised immune systems, anticonvulsant drugs, or surgical interventions. Neuroinflammation, a common factor in both scenarios, exhibits onset, duration, intensity, and consequence variations. It can modulate epileptogenesis, increase seizure susceptibility, and impact anticonvulsant drug pharmacokinetics, immune system function, and brain physiology. Viral infections significantly impact the clinical management of epilepsy patients, necessitating a multidisciplinary approach encompassing diagnosis, prevention, and treatment of both conditions. We delved into the dual dynamics of viruses inducing epilepsy and epilepsy patients acquiring viruses, examining the unique features of each case. For virus-induced epilepsy, we specify virus types, elucidate mechanisms of epilepsy induction, emphasize neuroinflammation's impact, and analyze its effects on anticonvulsant drug pharmacokinetics. Conversely, in epilepsy patients acquiring viruses, we detail the acquired virus, its interaction with existing epilepsy, neuroinflammation effects, and changes in anticonvulsant drug pharmacokinetics. Understanding this interplay advances precision therapies for epilepsy during viral infections, providing mechanistic insights, identifying biomarkers and therapeutic targets, and supporting optimized dosing regimens. However, further studies are crucial to validate tools, discover new biomarkers and therapeutic targets, and evaluate targeted therapy safety and efficacy in diverse epilepsy and viral infection scenarios.

Influence and influenceability: global directionality in directed complex networks

Knowing which nodes are influential in a complex network and whether the network can be influenced by a small subset of nodes is a key part of network analysis. However, many traditional measures of importance focus on node level information without considering the global network architecture. We use the method of trophic analysis to study directed networks and show that both 'influence' and 'influenceability' in directed networks depend on the hierarchical structure and the global directionality, as measured by the trophic levels and trophic coherence, respectively. We show that in directed networks trophic hierarchy can explain: the nodes that can reach the most others; where the eigenvector centrality localizes; which nodes shape the behaviour in opinion or oscillator dynamics; and which strategies will be successful in generalized rock-paper-scissors games. We show, moreover, that these phenomena are mediated by the global directionality. We also highlight other structural properties of real networks related to influenceability, such as the pseudospectra, which depend on trophic coherence. These results apply to any directed network and the principles highlighted-that node hierarchy is essential for understanding network influence, mediated by global directionality-are applicable to many real-world dynamics.

Neuronal network activity and connectivity are impaired in a conditional knockout mouse model with PCDH19 mosaic expression

Mutations in PCDH19 gene, which encodes protocadherin-19 (PCDH19), cause Developmental and Epileptic Encephalopathy 9 (DEE9). Heterogeneous loss of PCDH19 expression in neurons is considered a key determinant of the disorder; however, how PCDH19 mosaic expression affects neuronal network activity and circuits is largely unclear. Here, we show that the hippocampus of Pcdh19 mosaic mice is characterized by structural and functional synaptic defects and by the presence of PCDH19-negative hyperexcitable neurons. Furthermore, global reduction of network firing rate and increased neuronal synchronization have been observed in different limbic system areas. Finally, network activity analysis in freely behaving mice revealed a decrease in excitatory/inhibitory ratio and functional hyperconnectivity within the limbic system of Pcdh19 mosaic mice. Altogether, these results indicate that altered PCDH19 expression profoundly affects circuit wiring and functioning, and provide new key to interpret DEE9 pathogenesis.

The distribution and heterogeneity of excitability in focal epileptic network potentially contribute to the seizure propagation

Introduction

Existing dynamical models can explain the transmigration mechanisms involved in seizures but are limited to a single modality. Combining models with networks can reproduce scaled epileptic dynamics. And the structure and coupling interactions of the network, as well as the heterogeneity of both the node and network activities, may influence the final state of the network model.

Methods

We built a fully connected network with focal nodes prominently interacting and established a timescale separated epileptic network model. The factors affecting epileptic network seizure were explored by varying the connectivity patterns of focal network nodes and modulating the distribution of network excitability.

Results

The whole brain network topology as the brain activity foundation affects the consistent delayed clustering seizure propagation. In addition, the network size and distribution heterogeneity of the focal excitatory nodes can influence seizure frequency. With the increasing of the network size and averaged excitability level of focal network, the seizure period decreases. In contrast, the larger heterogeneity of excitability for focal network nodes can lower the functional activity level (average degree) of focal network. There are also subtle effects of focal network topologies (connection patterns of excitatory nodes) that cannot be ignored along with non-focal nodes.

Discussion

Unraveling the role of excitatory factors in seizure onset and propagation can be used to understand the dynamic mechanisms and neuromodulation of epilepsy, with profound implications for the treatment of epilepsy and even for the understanding of the brain.

Prediction and control of focal seizure spread: Random walk with restart on heterogeneous brain networks

Whole-brain models offer a promising method of predicting seizure spread, which is critical for successful surgical treatment of focal epilepsy. Existing methods are largely based on structural connectome, which ignores the effects of heterogeneity within the regional excitability of brains. In this study we used a whole-brain model to show that heterogeneity in nodal excitability had a significant impact on seizure propagation in the networks and compromised the prediction accuracy with structural connections. We then addressed this problem with an algorithm based on random walk with restart on graphs. We demonstrated that by establishing a relationship between the restarting probability and the excitability for each node, this algorithm could significantly improve the seizure spread prediction accuracy in heterogeneous networks and was more robust against the extent of heterogeneity. We also strategized surgical seizure control as a process to identify and remove the key nodes (connections) responsible for the early spread of seizures from the focal region. Compared to strategies based on structural connections, virtual surgery with a strategy based on a modified random walk with extended restart generated outcomes with a high success rate while maintaining low damage to the brain by removing fewer anatomical connections. These findings may have potential applications in developing personalized surgery strategies for epilepsy.

Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease

People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.

People with Alzheimer’s disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to ‘focal’ seizures which only involve certain areas) than the general population with epilepsy.

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

Epilepsy surgery: Evaluating robustness using dynamic network models

Epilepsy is one of the most common neurological conditions affecting over 65 million people worldwide. Over one third of people with epilepsy are considered refractory: they do not respond to drug treatments. For this significant cohort of people, surgery is a potentially transformative treatment. However, only a small minority of people with refractory epilepsy are considered suitable for surgery, and long-term seizure freedom is only achieved in half the cases. Recently, several computational approaches have been proposed to support presurgical planning. Typically, these approaches use a dynamic network model to explore the potential impact of surgical resection in silico. The network component of the model is informed by clinical imaging data and is considered static thereafter. This assumption critically overlooks the plasticity of the brain and, therefore, how continued evolution of the brain network post-surgery may impact upon the success of a resection in the longer term. In this work, we use a simplified dynamic network model, which describes transitions to seizures, to systematically explore how the network structure influences seizure propensity, both before and after virtual resections. We illustrate key results in small networks, before extending our findings to larger networks. We demonstrate how the evolution of brain networks post resection can result in a return to increased seizure propensity. Our results effectively determine the robustness of a given resection to possible network reconfigurations and so provide a potential strategy for optimizing long-term seizure freedom.