Multimodal, Multiscale Insights into Hippocampal Seizures Enabled by Transparent, Graphene-Based Microelectrode Arrays

Mulcahey, Patrick J.; Chen, Yuzhang; Driscoll, Nicolette; Murphy, Brendan B.; Dickens, Olivia O.; Johnson, A. T. Charlie; Vitale, Flavia; Takano, Hajime

doi:10.1523/eneuro.0386-21.2022

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…To fully leverage the advantages of a three-compartment platform, suitable analysis tools for the obtained electrophysiological data must be designed. Analyzing network synchrony is crucial for obtaining a deeper understanding of epileptic seizure-like activity in vitro since it is suggested that neuronal network synchrony is a key factor in the onset and spread of seizures ( Jiruska et al, 2013 ; Ren et al, 2021 ; Mulcahey et al, 2022 ). In the context of the tripartite network circuitry model, by analyzing the synchronous patterns of firing within and between the interconnected local networks in vitro, one can identify interrelations of induced hyperactivity that may contribute to understanding the initiation and spread of epileptic seizures in vivo.…”

Section: Discussionmentioning

confidence: 99%

Exploring Kainic Acid-Induced Alterations in Circular Tripartite Networks with Advanced Analysis Tools

Vinogradov,

Kapucu,

Narkilahti

2024

eNeuro

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Brain activity implies the orchestrated functioning of interconnected brain regions. Typical in vitro models aim to mimic the brain using single human pluripotent stem cell-derived neuronal networks. However, the field is constantly evolving to model brain functions more accurately through the use of new paradigms, e.g., brain-on-a-chip models with compartmentalized structures and integrated sensors. These methods create novel data requiring more complex analysis approaches. The previously introduced circular tripartite network concept models the connectivity between spatially diverse neuronal structures. The model consists of a microfluidic device allowing axonal connectivity between separated neuronal networks with an embedded microelectrode array to record both local and global electrophysiological activity patterns in the closed circuitry. The existing tools are suboptimal for the analysis of the data produced with this model. Here, we introduce advanced tools for synchronization and functional connectivity assessment. We used our custom-designed analysis to assess the interrelations between the kainic acid (KA)-exposed proximal compartment and its nonexposed distal neighbors before and after KA. Novel multilevel circuitry bursting patterns were detected and analyzed in parallel with the inter- and intracompartmental functional connectivity. The effect of KA on the proximal compartment was captured, and the spread of this effect to the nonexposed distal compartments was revealed. KA induced divergent changes in bursting behaviors, which may be explained by distinct baseline activity and varied intra- and intercompartmental connectivity strengths. The circular tripartite network concept combined with our developed analysis advances importantly both face and construct validity in modeling human epilepsy in vitro.

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Section: Discussionmentioning

confidence: 99%

Exploring Kainic Acid-Induced Alterations in Circular Tripartite Networks with Advanced Analysis Tools

Vinogradov,

Kapucu,

Narkilahti

2024

eNeuro

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show abstract

“…Driscoll et al (2021) used transparent graphene microelectrode arrays and successfully recorded simultaneous high-bandwidth electrophysiology and calcium imaging signals of brain network activity induced by epileptic seizures in vivo. Moreover, these cellular-scale methods enable the recording of electrophysiology and calcium fluorescence in CA1 pyramidal neurons in hippocampus seizures (Mulcahey et al, 2022). Moreover, inspired by the accomplishments of brain studies, integrating a transparent graphene sensor onto a designed abdominal window detected changes in the activity of the enteric nervous system through simultaneous optical and electrical recording in a live mouse (Rakhilin et al, 2016).…”

Section: The Optical Property Of Graphene In Neural Imaging and Photo...mentioning

confidence: 99%

The mechanical, optical, and thermal properties of graphene influencing its pre-clinical use in treating neurological diseases

Yang

Bai

et al. 2023

Front. Neurosci.

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Rapid progress in nanotechnology has advanced fundamental neuroscience and innovative treatment using combined diagnostic and therapeutic applications. The atomic scale tunability of nanomaterials, which can interact with biological systems, has attracted interest in emerging multidisciplinary fields. Graphene, a two-dimensional nanocarbon, has gained increasing attention in neuroscience due to its unique honeycomb structure and functional properties. Hydrophobic planar sheets of graphene can be effectively loaded with aromatic molecules to produce a defect-free and stable dispersion. The optical and thermal properties of graphene make it suitable for biosensing and bioimaging applications. In addition, graphene and its derivatives functionalized with tailored bioactive molecules can cross the blood–brain barrier for drug delivery, substantially improving their biological property. Therefore, graphene-based materials have promising potential for possible application in neuroscience. Herein, we aimed to summarize the important properties of graphene materials required for their application in neuroscience, the interaction between graphene-based materials and various cells in the central and peripheral nervous systems, and their potential clinical applications in recording electrodes, drug delivery, treatment, and as nerve scaffolds for neurological diseases. Finally, we offer insights into the prospects and limitations to aid graphene development in neuroscience research and nanotherapeutics that can be used clinically.

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Transparent MXene Microelectrode Arrays for Multimodal Mapping of Neural Dynamics

Shankar,

Chen,

Averbeck

et al. 2024

Adv Healthcare Materials

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Transparent microelectrode arrays have proven useful in neural sensing, offering a clear interface for monitoring brain activity without compromising high spatial and temporal resolution. The current landscape of transparent electrode technology faces challenges in developing durable, highly transparent electrodes while maintaining low interface impedance and prioritizing scalable processing and fabrication methods. To address these limitations, we introduce artifact‐resistant transparent MXene microelectrode arrays optimized for high spatiotemporal resolution recording of neural activity. With 60% transmittance at 550 nm, these arrays enable simultaneous imaging and electrophysiology for multimodal neural mapping. Electrochemical characterization shows low impedance of 563 ± 99 kΩ at 1 kHz and a charge storage capacity of 58 mC cm⁻² without chemical doping. In vivo experiments in rodent models demonstrate the transparent arrays' functionality and performance. In a rodent model of chemically‐induced epileptiform activity, we tracked ictal wavefronts via calcium imaging while simultaneously recording seizure onset. In the rat barrel cortex, we recorded multi‐unit activity across cortical depths, showing the feasibility of recording high‐frequency electrophysiological activity. The transparency and optical absorption properties of Ti₃C₂Tx MXene microelectrodes enable high‐quality recordings and simultaneous light‐based stimulation and imaging without contamination from light‐induced artifacts.

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Multimodal, Multiscale Insights into Hippocampal Seizures Enabled by Transparent, Graphene-Based Microelectrode Arrays

Abstract: Visual Abstract

Cited by 7 publications

References 40 publications

Exploring Kainic Acid-Induced Alterations in Circular Tripartite Networks with Advanced Analysis Tools

Exploring Kainic Acid-Induced Alterations in Circular Tripartite Networks with Advanced Analysis Tools

The mechanical, optical, and thermal properties of graphene influencing its pre-clinical use in treating neurological diseases

Transparent MXene Microelectrode Arrays for Multimodal Mapping of Neural Dynamics

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