Prediction of the Seizure Suppression Effect by Electrical Stimulation via a Computational Modeling Approach

Abstract

In this paper, we identified factors that can affect seizure suppression via electrical stimulation by an integrative study based on experimental and computational approach. Preferentially, we analyzed the characteristics of seizure-like events (SLEs) using our previous in vitro experimental data. The results were analyzed in two groups classified according to the size of the effective region, in which the SLE was able to be completely suppressed by local stimulation. However, no significant differences were found between these two groups in terms of signal features or propagation characteristics (i.e., propagation delays, frequency spectrum, and phase synchrony). Thus, we further investigated important factors using a computational model that was capable of evaluating specific influences on effective region size. In the proposed model, signal transmission between neurons was based on two different mechanisms: synaptic transmission and the electrical field effect. We were able to induce SLEs having similar characteristics with differentially weighted adjustments for the two transmission methods in various noise environments. Although the SLEs had similar characteristics, their suppression effects differed. First of all, the suppression effect occurred only locally where directly received the stimulation effect in the high noise environment, but it occurred in the entire network in the low noise environment. Interestingly, in the same noise environment, the suppression effect was different depending on SLE propagation mechanism; only a local suppression effect was observed when the influence of the electrical field transmission was very weak, whereas a global effect was observed with a stronger electrical field effect. These results indicate that neuronal activities synchronized by a strong electrical field effect respond more sensitively to partial changes in the entire network. In addition, the proposed model was able to predict that stimulation of a seizure focus region is more effective for suppression. In conclusion, we confirmed the possibility of a computational model as a simulation tool to analyze the efficacy of deep brain stimulation (DBS) and investigated the key factors that determine the size of an effective region in seizure suppression via electrical stimulation.

Three-dimensional, multifunctional neural interfaces for cortical spheroids and engineered assembloids

“…The finding shows that focal neural modulation of the brain in vitro is possible through the 3D MMF, with high spatial resolution. Study opportunities include electrically modulated seizures or epilepsy suppression ( 32 ) using brain organoids.…”

Contributions of Imaging to Neuromodulatory Treatment of Drug-Refractory Epilepsy

“…The exact mode of action for RNS remains yet to be ascertained and is most likely multifactorial. GABA-mediated hyperpolarization or neuronal depolarization blockade by accumulation of extracellular potassium ions might account for stimulation effects on seizure generation and propagation [ 85 ]. Additionally, an observed decrease in seizure frequency over time suggests that stimulation might alter gene expression patterns or modulate brain network architecture and connectivity, an appealing hypothesis that needs to be tested [ 86 ].…”

Materials Chemistry of Neural Interface Technologies and Recent Advances in Three-Dimensional Systems

“…These devices can provide essential information for investigating and diagnosing degenerative conditions such as Parkinson's 4−8 and Alzheimer's diseases 9−11 and also various neurological disorders such as Zika virus induced microcephaly 12−15 and epilepsy. 16,17 The resulting signals can also be used as brain−machine interfaces to control prosthetics and other external devices. 18,19 As sources of electrical stimulation, the electrodes can activate or inactivate targeted neural networks to eliminate seizures and tremors.…”

“…For clinical practice in the context of the brain, such technologies typically involve arrays of a few tens of electrodes that interface with the skin of the scalp or the surface of the brain to capture spatiotemporal variations in electrical potential, through techniques known as electroencephalography (EEG) and electrocorticography (ECoG), respectively (Figure a). These devices can provide essential information for investigating and diagnosing degenerative conditions such as Parkinson’s − and Alzheimer’s diseases − and also various neurological disorders such as Zika virus induced microcephaly − and epilepsy. , The resulting signals can also be used as brain–machine interfaces to control prosthetics and other external devices. , As sources of electrical stimulation, the electrodes can activate or inactivate targeted neural networks to eliminate seizures and tremors. , In rehabilitation, related types of stimulation can modulate cortical excitability in stroke patients to enhance the effects of training for various tasks associated with daily life. , In the context of research, neural interfaces can be applied in wide-ranging animal models for fundamental studies. For in vitro experiments that use human induced pluripotent stem cells (hiPSCs), these technologies can be used to explore drugs, , genetic diseases, , and cancers. , Here, electrophysiological measurements − occur with multielectrode arrays (MEAs) , and patch clamps , for thin films of cells (Figure b).…”