Adaptation and Inhibition Control Pathological Synchronization in a Model of Focal Epileptic Seizure

Regulating epileptiform discharges by heterogeneous interneurons in thalamocortical model

Inhibitory interneurons in the cortex are abundant and have diverse roles, classified as parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal polypeptide (VIP) according to chemically defined categories. Currently, their involvement with seizures has been partially uncovered in physiological terms. Here, we propose a corticothalamic model containing heterogeneous interneurons to study the effects of various interneurons on absence seizure dynamics by means of optogenetic stimulation. First, the important role of feedforward inhibition caused by SRN→PV→PN projections on seizures is verified. Then, we demonstrate that light activation targeting either PV or SOM INs can control seizures. Finally, with different inhibition contributions from PV INs and SOM INs, the possible disinhibitory effect of blue light acting on VIP INs is mainly discussed. The results suggest that depending on the inhibition degree of both types, the disinhibition brought about by the VIP INs will trigger seizures, will control seizures, and will not work or cause the PNs to tend toward a high saturation state with high excitability. The circuit mechanism and the related bifurcation characteristics in various cases are emphatically revealed. In the model presented, in addition to Hopf and saddle-node bifurcations, the system may also undergo period-doubling and torus bifurcations under stimulus action, with more complex dynamics. Our work may provide a theoretical basis for understanding and further exploring the role of heterogeneous interneurons, in particular, the VIP INs, a novel target, in absence seizures.

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.

Modeling seizures: From single neurons to networks

Dynamical system tools offer a complementary approach to detailed biophysical seizure modeling, with a high potential for clinical applications. This review describes the theoretical framework that provides a basis for theorizing certain properties of seizures and for their classification according to their dynamical properties at onset and offset. We describe various modeling approaches spanning different scales, from single neurons to large-scale networks. This narrative review provides an accessible overview of this field, including non-exhaustive examples of key recent works.

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.

Missense variants in the N-terminal domain of the A isoform of FHF2/FGF13 cause an X-linked developmental and epileptic encephalopathy

Fibroblast growth factor homologous factors (FHFs) are intracellular proteins which regulate voltage-gated sodium (Nav) channels in the brain and other tissues. FHF dysfunction has been linked to neurological disorders including epilepsy. Here, we describe two sibling pairs and three unrelated males who presented in infancy with intractable focal seizures and severe developmental delay. Whole-exome sequencing identified hemi- and heterozygous variants in the N-terminal domain of the A isoform of FHF2 (FHF2A). The X-linked FHF2 gene (also known as FGF13) has alternative first exons which produce multiple protein isoforms that differ in their N-terminal sequence. The variants were located at highly conserved residues in the FHF2A inactivation particle that competes with the intrinsic fast inactivation mechanism of Nav channels. Functional characterization of mutant FHF2A co-expressed with wild-type Nav1.6 (SCN8A) revealed that mutant FHF2A proteins lost the ability to induce rapid-onset, long-term blockade of the channel while retaining pro-excitatory properties. These gain-of-function effects are likely to increase neuronal excitability consistent with the epileptic potential of FHF2 variants. Our findings demonstrate that FHF2 variants are a cause of infantile-onset developmental and epileptic encephalopathy and underline the critical role of the FHF2A isoform in regulating Nav channel function.

Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions

In this work, we introduce new phenomenological neuronal models (eLIF and mAdExp) that account for energy supply and demand in the cell as well as the inactivation of spike generation how these interact with subthreshold and spiking dynamics. Including these constraints, the new models reproduce a broad range of biologically-relevant behaviors that are identified to be crucial in many neurological disorders, but were not captured by commonly used phenomenological models. Because of their low dimensionality eLIF and mAdExp open the possibility of future large-scale simulations for more realistic studies of brain circuits involved in neuronal disorders. The new models enable both more accurate modeling and the possibility to study energy-associated disorders over the whole time-course of disease progression instead of only comparing the initially healthy status with the final diseased state. These models, therefore, provide new theoretical and computational methods to assess the opportunities of early diagnostics and the potential of energy-centered approaches to improve therapies.

Neurons, even “at rest”, require a constant supply of energy to function. They cannot sustain high-frequency activity over long periods because of regulatory mechanisms, such as adaptation or sodium channels inactivation, and metabolic limitations. These limitations are especially severe in many neuronal disorders, where energy can become insufficient and make the neuronal response change drastically, leading to increased burstiness, network oscillations, or seizures. Capturing such behaviors and impact of energy constraints on them is an essential prerequisite to study disorders such as Parkinson’s disease and epilepsy. However, energy and spiking constraints are not present in any of the standard neuronal models used in computational neuroscience. Here we introduce models that provide a simple and scalable way to account for these features, enabling large-scale theoretical and computational studies of neurological disorders and activity patterns that could not be captured by previously used models. These models provide a way to study energy-associated disorders over the whole time-course of disease progression, and they enable a better assessment of energy-centered approaches to improve therapies.

Identification of global and local states during seizures using quantitative functional connectivity and recurrence plot analysis

INTRODUCTION

As a dynamical system, the brain constantly modulates its state and epileptic seizures have been hypothesized to be low dimensional periodic states of the brain. With this assumption, seizures have previously been investigated to identify patterns of these recurrent states; however, these attempts have generated conflicting results. These discrepant observations led us to reconsider the dynamic of state transitions during seizures.

METHODS

Using intracerebral recordings of 17 refractory epilepsy patients assessed prior to surgery, we studied ictal states with several state-of-the-art methods in order to investigate their dynamics. Global states were identified based on distinct functional connectivity measures in the time domain, frequency domain, and phase-space. We further investigated the state transitions in different brain regions locally using a univariate measure based on dynamical system analysis named the Recurrence Plot (RP).

RESULTS

For the ictal period, we detected lower global state transition rates compared to pre- and post-ictal periods (p < 0.05 for seizure-free (SF) and p > 0.05 for non-seizure-free (NSF) groups post-surgery); however, the structure of RPs pointed towards higher state transition rates in some regions like the seizure-onset-zone (p < 0.001 for SF and p > 0.05 for NSF group). Moreover, a direct comparison of state transition dynamics between SF and NSF patients revealed different patterns for local state transitions between SF and NSF patients (p < 0.05 for seizure-onset-zone while p > 0.05 for other regions) and no significant difference in global state transition rates (p > 0.05).

CONCLUSION

Our findings pointed to distinct dynamics for state transitions at different spatial scales. While the pattern of global state transitions led to the conclusion that the brain changes state less frequently during ictal activity, locally, it experienced a higher rate of state transition. Furthermore, our results for different patterns of state transitions in the seizure-onset-zone between SF and NSF patients could have a practical application in predicting surgical outcome.