Open loop optogenetic control of simulated cortical epileptiform activity

Dynamical Mechanism Analysis of Three Neuroregulatory Strategies on the Modulation of Seizures

This paper attempts to explore and compare the regulatory mechanisms of optogenetic stimulation (OS), deep brain stimulation (DBS) and electromagnetic induction on epilepsy. Based on the Wilson-Cowan model, we first demonstrate that the external input received by excitatory and inhibitory neural populations can induce rich dynamic bifurcation behaviors such as Hopf bifurcation, and make the system exhibit epileptic and normal states. Then, both OS and DBS are shown to be effective in controlling the epileptic state to a normal low-level state, and the stimulus parameters have a broad effective range. However, electromagnetic induction cannot directly control epilepsy to this desired state, even if it can significantly reduce the oscillation frequency of neural populations. One main difference worth noting is that the high spatiotemporal specificity of OS allows it to target inhibitory neuronal populations, whereas DBS and electromagnetic induction can only stimulate excitatory as well as inhibitory neuronal populations together. Next, the propagation behavior of epilepsy is explored under a typical three-node feedback loop structure. An increase in coupling strength accelerates and exacerbates epileptic activity in other brain regions. Finally, OS and DBS applied to the epileptic focus play similar positive roles in controlling the behavior of the area of seizure propagation, while electromagnetic induction still only achieves unsatisfactory effects. It is hoped that these dynamical results can provide insights into the treatment of epilepsy as well as other neurological disorders.

Epilepsy control using a fixed time integral super twisting sliding mode control for Pinsky-Rinzel pyramidal model through ion channels with optogenetic method

BACKGROUND AND OBJECTIVE

Epilepsy is a dynamic disease of neuronal networks and epileptic activity in the brain should be suppressed quickly in the shortest possible time with minimum control signal. Thus, a closed-loop feedback control by using the fixed-time integral super-twisting sliding-mode controller via an optogenetic method is employed for suppressing seizures in the Pinsky-Rinzel (PR) model as a dynamic model of the hippocampus CA3 region where epileptic seizures occur. The control signal is applied to the PR model through the ChR2 channel model in the form of light photons using the optogenetic method. The present study aimed to determine the controller robustness against parameter changes and disturbances in order to reduce the control time, approach the zero tracking error of the normal desired state in a fixed time, and finally, converge the epileptic state to the normal desired state.

METHOD

In order to apply the control signal to the Pinsky-Rinzel model in the optogenetic method, the dynamic model of the ion current generated by channelrhodopsin 2 (ChR2) as a light-sensitive protein model in the optogenetic method was first applied to the PR model. Then, a fixed-time integral super-twisting sliding-mode controller was designed for the system, which is the combination of PR and ChR2 models.

RESULTS

After applying the proposed controller, the simulation results indicated that the control signal was -0.7 mV, the tracking error of the normal desired state could reach zero within 1.5 milliseconds, and the problems of singularity and chattering were solved.

CONCLUSIONS

A reduction occurred in the control signal reduced regarding the objectives of the study and comparing the proposed controller with the classical sliding-mode controller. Thus, this method can produce a safe control input for brain. In addition, both types of sliding mode controllers are robust against the parameters variations and external disturbances. Thus, they are superior to non-robust and simple controllers. Finally, based on the results, the validity of the fixed-time integral super-twisting sliding mode controller is confirmed for epilepsy control.

Innovations in the Neurosurgical Management of Epilepsy

Technical limitations and clinical challenges have historically limited the diagnostic tools and treatment methods available for surgical approaches to the management of epilepsy. By contrast, recent technological innovations in several areas hold significant promise in improving outcomes and decreasing morbidity. We review innovations in the neurosurgical management of epilepsy in several areas, including wireless recording and stimulation systems (particularly responsive neurostimulation [NeuroPace]), conformal electrodes for high-resolution electrocorticography, robot-assisted stereotactic surgery, optogenetics and optical imaging methods, novel positron emission tomography ligands, and new applications of focused ultrasonography. Investigation into genetic causes of and susceptibilities to epilepsy has introduced a new era of precision medicine, enabling the understanding of cell signaling mechanisms underlying epileptic activity as well as patient-specific molecularly targeted treatment options. We discuss the emerging path to individualized treatment plans, predicted outcomes, and improved selection of effective interventions, on the basis of these developments.

Suppression of epileptic seizures via Anderson localization

Here we show that brain seizures can be effectively suppressed through random modulation of the brain medium. We use an established mesoscale cortical model in the form of a system of coupled stochastic partial differential equations. We show that by temporal and spatial randomization of parameters governing the firing rates of the excitatory and inhibitory neuron populations, seizure waves can be significantly suppressed. We find that the attenuation is the most effective when applied to the mean threshold potential. The proposed technique can serve as a non-invasive paradigm to mitigate epileptic seizures without knowing the location of the epileptic foci.

Seizing Control: From Current Treatments to Optogenetic Interventions in Epilepsy

The unpredictability and severity of seizures contribute to the debilitating nature of epilepsy. These factors also render the condition particularly challenging to treat, as an ideal treatment would need to detect and halt the pathological bursts of hyperactivity without disrupting normal brain activity. Optogenetic techniques offer promising tools to study and perhaps eventually treat this episodic disorder by controlling specific brain circuits in epileptic animals with great temporal precision. Here, we briefly review the current treatment options for patients with epilepsy. We then describe the many ways optogenetics has allowed us to untangle the microcircuits involved in seizure activity, and how it has, in some cases, changed our perception of previous theories of seizure generation. Control of seizures with light is no longer a dream, and has been achieved in numerous different animal models of epilepsy. Beyond its application as a seizure suppressor, we highlight another facet of optogenetics in epilepsy, namely the ability to create "on-demand" seizures, as a tool to systematically probe the dynamics of networks during seizure initiation and propagation. Finally, we look into the future to discuss the possibilities and challenges of translating optogenetic techniques to clinical use.

Closed-loop feedback control and bifurcation analysis of epileptiform activity via optogenetic stimulation in a mathematical model of human cortex

Optogenetics provides a method of neuron stimulation that has high spatial, temporal, and cell-type specificity. Here we present a model of optogenetic feedback control that targets the inhibitory population, which expresses light-sensitive channelrhodopsin-2 channels, in a mean-field model of undifferentiated cortex that is driven to seizures. The inhibitory population is illuminated with an intensity that is a function of electrode measurements obtained via the cortical model. We test the efficacy of this control method on seizurelike activity observed in two parameter spaces of the cortical model that most closely correspond to seizures observed in patients. We also compare the effect of closed-loop and open-loop control on seizurelike activity using a less-complicated ordinary differential equation model of the undifferentiated cortex in parameter space. Seizurelike activity is successfully suppressed in both parameter planes using optimal illumination intensities less likely to have adverse effects on cortical tissue.

Optogenetic tools for modulating and probing the epileptic network

Epilepsy affects roughly 1% of the population worldwide. Although effective treatments with antiepileptic drugs are available, more than 20% of patients have seizures that are refractory to medical therapy and many patients experience adverse effects. Hence, there is a continued need for novel therapies for those patients. A new technique called "optogenetics" may offer a new hope for these refractory patients. Optogenetics is a technology based on the combination of optics and genetics, which can control or record neural activity with light. Following delivery of light-sensitive opsin genes such as channelrhodopsin-2 (ChR2), halorhodopsin (NpHR), and others into brain, excitation or inhibition of specific neurons in precise brain areas can be controlled by illumination at different wavelengths with very high temporal and spatial resolution. Neuromodulation with the optogenetics toolbox have already been shown to be effective at treating seizures in animal models of epilepsy. This review will outline the most recent advances in epilepsy research with optogenetic techniques and discuss how this technology can contribute to our understanding and treatment of epilepsy in the future.

Computational modeling of neurostimulation in brain diseases

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.

Optimal control based seizure abatement using patient derived connectivity

Epilepsy is a neurological disorder in which patients have recurrent seizures. Seizures occur in conjunction with abnormal electrical brain activity which can be recorded by the electroencephalogram (EEG). Often, this abnormal brain activity consists of high amplitude regular spike-wave oscillations as opposed to low amplitude irregular oscillations in the non-seizure state. Active brain stimulation has been proposed as a method to terminate seizures prematurely, however, a general and widely-applicable approach to optimal stimulation protocols is still lacking. In this study we use a computational model of epileptic spike-wave dynamics to evaluate the effectiveness of a pseudospectral method to simulated seizure abatement. We incorporate brain connectivity derived from magnetic resonance imaging of a subject with idiopathic generalized epilepsy. We find that the pseudospectral method can successfully generate time-varying stimuli that abate simulated seizures, even when including heterogeneous patient specific brain connectivity. The strength of the stimulus required varies in different brain areas. Our results suggest that seizure abatement, modeled as an optimal control problem and solved with the pseudospectral method, offers an attractive approach to treatment for in vivo stimulation techniques. Further, if optimal brain stimulation protocols are to be experimentally successful, then the heterogeneity of cortical connectivity should be accounted for in the development of those protocols and thus more spatially localized solutions may be preferable.

Future of seizure prediction and intervention: closing the loop

The ultimate goal of epilepsy therapies is to provide seizure control for all patients while eliminating side effects. Improved specificity of intervention through on-demand approaches may overcome many of the limitations of current intervention strategies. This article reviews the progress in seizure prediction and detection, potential new therapies to provide improved specificity, and devices to achieve these ends. Specifically, we discuss (1) potential signal modalities and algorithms for seizure detection and prediction, (2) closed-loop intervention approaches, and (3) hardware for implementing these algorithms and interventions. Seizure prediction and therapies maximize efficacy, whereas minimizing side effects through improved specificity may represent the future of epilepsy treatments.

Optogenetic induced epileptiform activity in a model human cortex

Background

Cortical stimulation plays an important role in the study of epileptic seizures. We present a numerical simulation of stimulation using optogenetic channels expressed by excitatory cells in a mean field model of the human cortex.

Findings

Depolarising excitatory cells in a patch of model cortex using Channelrhodpsin-2 (ChR2) ion channels, we are able to hyper-excite a normally functioning cortex and mimic seizure activity. The temporal characteristics of optogenetic channels, and the ability to control the frequency of synchronous activity using these properties are also demonstrated.

Conclusions

Optogenetics is a powerful stimulation technique with high spatial, temporal and cell-type specificity, and would be invaluable in studying seizures and other brain disorders and functions.

Beyond the hammer and the scalpel: selective circuit control for the epilepsies

Current treatment options for epilepsy are inadequate, as too many patients suffer from uncontrolled seizures and from negative side effects of treatment. In addition to these clinical challenges, our scientific understanding of epilepsy is incomplete. Optogenetic and designer receptor technologies provide unprecedented and much needed specificity, allowing for spatial, temporal and cell type-selective modulation of neuronal circuits. Using such tools, it is now possible to begin to address some of the fundamental unanswered questions in epilepsy, to dissect epileptic neuronal circuits and to develop new intervention strategies. Such specificity of intervention also has the potential for direct therapeutic benefits, allowing healthy tissue and network functions to continue unaffected. In this Perspective, we discuss promising uses of these technologies for the study of seizures and epilepsy, as well as potential use of these strategies for clinical therapies.