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

Electric field temporal interference stimulation of neurons in vitro

Electrical stimulation (ES) techniques, such as deep brain and transcranial electrical stimulation, have shown promise in alleviating the symptoms of depression and other neurological disorders in vivo. A new noninvasive ES method called temporal interference stimulation (TIS), possesses great potential as it can be used to steer the stimulation and possibly selectively modulate different brain regions. To study TIS in a controlled environment, we successfully established an in vitro 'TIS on a chip' setup using rat cortical neurons on microelectrode arrays (MEAs) in combination with a current stimulator. We validated the developed TIS system and demonstrated the spatial steerability of the stimulation by direct electric field measurements in the chip setup. We stimulated cultures of rat cortical neurons at 28 days in vitro (DIV) by two-channel stimulation delivering 1) TIS at 653 Hz and 643 Hz, resulting in a 10 Hz frequency envelope, 2) low-frequency stimulation (LFS) at 10 Hz and 3) high-frequency stimulation (HFS) at 653 Hz. Unstimulated cultures were used as control/sham. We observed the differences in the electric field strengths during TIS, HFS, and LFS. Moreover, HFS and LFS had the smallest effects on neuronal activity. Instead, TIS elicited neuronal electrophysiological responses, especially 24 hours after stimulation. Our 'TIS on a chip' approach eludicates the applicability of TIS as a method to modulate neuronal electrophysiological activity. The TIS on a chip approach provides spatially steerable stimuli while mitigating the effects of high stimulus fields near the stimulation electrodes. Thus, the approach opens new avenues for stimulation on a chip applications, allowing the study of neuronal responses to gain insights into the potential clinical applications of TIS in treating various brain disorders.

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.

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

Advances in materials chemistry and engineering serve as the basis for multifunctional neural interfaces that span length scales from individual neurons to neural networks, neural tissues, and complete neural systems. Such technologies exploit electrical, electrochemical, optical, and/or pharmacological modalities in sensing and neuromodulation for fundamental studies in neuroscience research, with additional potential to serve as routes for monitoring and treating neurodegenerative diseases and for rehabilitating patients. This review summarizes the essential role of chemistry in this field of research, with an emphasis on recently published results and developing trends. The focus is on enabling materials in diverse device constructs, including their latest utilization in 3D bioelectronic frameworks formed by 3D printing, self-folding, and mechanically guided assembly. A concluding section highlights key challenges and future directions.

Control of epileptic seizures by electrical stimulation: a model-based study

High frequency electrical stimulation of brain is commonly used in research experiments and clinical trials as a modern tool for control of epileptic seizures. However, the mechanistic basis by which periodic external stimuli alter the brain state is not well understood. This study provides a computational insight into the mechanism of seizure suppression by high frequency stimulation (HFS). In particular, a modified version of the Jansen-Rit neural mass model is employed, in which EEG signals can be considered as the input. The proposed model reproduces seizure-like activity in the output during the ictal period of the input signal. By applying a control signal to the model, a wide range of stimulation amplitudes and frequencies are systematically explored. Simulation results reveal that HFS can effectively suppress the seizure-like activity. Our results suggest that HFS has the ability of shifting the operating state of neural populations away from a critical condition. Furthermore, a closed-loop control strategy is proposed in this paper. The main objective has been to considerably reduce the control effort needed for blocking abnormal activity of the brain. Such an energy reduction could be of practical importance, to reduce possible side effects and increase battery life for implanted neurostimulators.

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

Three-dimensional (3D), submillimeter-scale constructs of neural cells, known as cortical spheroids, are of rapidly growing importance in biological research because these systems reproduce complex features of the brain in vitro. Despite their great potential for studies of neurodevelopment and neurological disease modeling, 3D living objects cannot be studied easily using conventional approaches to neuromodulation, sensing, and manipulation. Here, we introduce classes of microfabricated 3D frameworks as compliant, multifunctional neural interfaces to spheroids and to assembloids. Electrical, optical, chemical, and thermal interfaces to cortical spheroids demonstrate some of the capabilities. Complex architectures and high-resolution features highlight the design versatility. Detailed studies of the spreading of coordinated bursting events across the surface of an isolated cortical spheroid and of the cascade of processes associated with formation and regrowth of bridging tissues across a pair of such spheroids represent two of the many opportunities in basic neuroscience research enabled by these platforms.

Contributions of Imaging to Neuromodulatory Treatment of Drug-Refractory Epilepsy

Epilepsy affects about 1% of the world's population, and up to 30% of all patients will ultimately not achieve freedom from seizures with anticonvulsive medication alone. While surgical resection of a magnetic resonance imaging (MRI) -identifiable lesion remains the first-line treatment option for drug-refractory epilepsy, surgery cannot be offered to all. Neuromodulatory therapy targeting "seizures" instead of "epilepsy" has emerged as a valuable treatment option for these patients, including invasive procedures such as deep brain stimulation (DBS), responsive neurostimulation (RNS) and peripheral approaches such as vagus nerve stimulation (VNS). The purpose of this review is to provide in-depth information on current concepts and evidence on network-level aspects of drug-refractory epilepsy. We reviewed the current evidence gained from studies utilizing advanced imaging methodology, with a specific focus on their contributions to neuromodulatory therapy.

Artificial Intelligence and Computational Approaches for Epilepsy

Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient's data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.