Multimodal in vivo recording using transparent graphene microelectrodes illuminates spatiotemporal seizure dynamics at the microscale

Challenges and Opportunities: Nanomaterials in Epilepsy Diagnosis

Epilepsy is a common neurological disorder characterized by a significant rate of disability. Accurate early diagnosis and precise localization of the epileptogenic zone are essential for timely intervention, seizure prevention, and personalized treatment. However, over 30% of patients with epilepsy exhibit negative results on electroencephalography and magnetic resonance imaging (MRI), which can lead to misdiagnosis and subsequent delays in treatment. Consequently, enhancing diagnostic methodologies is imperative for effective epilepsy management. The integration of nanomaterials with biomedicine has led to the development of diagnostic tools for epilepsy. Key advancements include nanomaterial-enhanced neural electrodes, contrast agents, and biochemical sensors. Nanomaterials improve the quality of electrophysiological signals and broaden the detection range of electrodes. In imaging, functionalized magnetic nanoparticles enhance MRI sensitivity, facilitating localization of the epileptogenic zone. NIR-II nanoprobes enable tracking of seizure-related biomarkers with deep tissue penetration. Furthermore, nanomaterials enhance the sensitivity of biochemical sensors for detecting epilepsy biomarkers, which is crucial for early detection. These advancements significantly increase diagnostic sensitivity and specificity. However, challenges remain, particularly regarding biosafety, quality control, and the scalability of fabrication processes. Overcoming these obstacles is essential for successful clinical translation. Artificial-intelligence-based big data analytics can facilitate the development of diagnostic tools by screening nanomaterials with specific properties. This approach may help to address current limitations and improve both effectiveness and safety. This review explores the application of nanomaterials in the diagnosis and detection of epilepsy, with the objective of inspiring innovative ideas and strategies to enhance diagnostic effectiveness.

Flexible 3D Kirigami Probes for In Vitro and In Vivo Neural Applications

3D microelectrode arrays (MEAs) are gaining popularity as brain-machine interfaces and platforms for studying electrophysiological activity. Interactions with neural tissue depend on the electrochemical, mechanical, and spatial features of the recording platform. While planar or protruding 2D MEAs are limited in their ability to capture neural activity across layers, existing 3D platforms still require advancements in manufacturing scalability, spatial resolution, and tissue integration. In this work, a customizable, scalable, and straightforward approach to fabricate flexible 3D kirigami MEAs containing both surface and penetrating electrodes, designed to interact with the 3D space of neural tissue, is presented. These novel probes feature up to 512 electrodes distributed across 128 shanks in a single flexible device, with shank heights reaching up to 1 mm. The 3D kirigami MEAs are successfully deployed in several neural applications, both in vitro and in vivo, and identified spatially dependent electrophysiological activity patterns. Flexible 3D kirigami MEAs are therefore a powerful tool for large-scale electrical sampling of complex neural tissues while improving tissue integration and offering enhanced capabilities for analyzing neural disorders and disease models where high spatial resolution is required.

Bio-inspired electronics: Soft, biohybrid, and "living" neural interfaces

Neural interface technologies are increasingly evolving towards bio-inspired approaches to enhance integration and long-term functionality. Recent strategies merge soft materials with tissue engineering to realize biologically-active and/or cell-containing living layers at the tissue-device interface that enable seamless biointegration and novel cell-mediated therapeutic opportunities. This review maps the field of bio-inspired electronics and discusses key recent developments in tissue-like and regenerative bioelectronics, from soft biomaterials and surface-functionalized bioactive coatings to cell-containing 'biohybrid' and 'all-living' interfaces. We define and contextualize key terminology in this emerging field and highlight how biological and living components can bridge the gap to clinical translation.

Transparent MXene Microelectrode Arrays for Multimodal Mapping of Neural Dynamics

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.

MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.

Epilepsy insights revealed by intravital functional optical imaging

Despite an abundance of pharmacologic and surgical epilepsy treatments, there remain millions of patients suffering from poorly controlled seizures. One approach to closing this treatment gap may be found through a deeper mechanistic understanding of the network alterations that underly this aberrant activity. Functional optical imaging in vertebrate models provides powerful advantages to this end, enabling the spatiotemporal acquisition of individual neuron activity patterns across multiple seizures. This coupled with the advent of genetically encoded indicators, be them for specific ions, neurotransmitters or voltage, grants researchers unparalleled access to the intact nervous system. Here, we will review how in vivo functional optical imaging in various vertebrate seizure models has advanced our knowledge of seizure dynamics, principally seizure initiation, propagation and termination.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

High-density transparent graphene arrays for predicting cellular calcium activity at depth from surface potential recordings

Optically transparent neural microelectrodes have facilitated simultaneous electrophysiological recordings from the brain surface with the optical imaging and stimulation of neural activity. A remaining challenge is to scale down the electrode dimensions to the single-cell size and increase the density to record neural activity with high spatial resolution across large areas to capture nonlinear neural dynamics. Here we developed transparent graphene microelectrodes with ultrasmall openings and a large, transparent recording area without any gold extensions in the field of view with high-density microelectrode arrays up to 256 channels. We used platinum nanoparticles to overcome the quantum capacitance limit of graphene and to scale down the microelectrode diameter to 20 µm. An interlayer-doped double-layer graphene was introduced to prevent open-circuit failures. We conducted multimodal experiments, combining the recordings of cortical potentials of microelectrode arrays with two-photon calcium imaging of the mouse visual cortex. Our results revealed that visually evoked responses are spatially localized for high-frequency bands, particularly for the multiunit activity band. The multiunit activity power was found to be correlated with cellular calcium activity. Leveraging this, we employed dimensionality reduction techniques and neural networks to demonstrate that single-cell and average calcium activities can be decoded from surface potentials recorded by high-density transparent graphene arrays.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Understanding the nervous system: lessons from Frontiers in Neurophotonics

The Frontiers in Neurophotonics Symposium is a biennial event that brings together neurobiologists and physicists/engineers who share interest in the development of leading-edge photonics-based approaches to understand and manipulate the nervous system, from its individual molecular components to complex networks in the intact brain. In this Community paper, we highlight several topics that have been featured at the symposium that took place in October 2022 in Québec City, Canada.

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.

Photoacoustic Chemical Imaging Sodium Nano-Sensor Utilizing a Solvatochromic Dye Transducer for In Vivo Application

Sodium has many vital and diverse roles in the human body, including maintaining the cellular pH, generating action potential, and regulating osmotic pressure. In cancer, sodium dysregulation has been correlated with tumor growth, metastasis, and immune cell inhibition. However, most in vivo sodium measurements are performed via Na23 NMR, which is handicapped by slow acquisition times, a low spatial resolution (in mm), and low signal-to-noise ratios. We present here a plasticizer-free, ionophore-based sodium-sensing nanoparticle that utilizes a solvatochromic dye transducer to circumvent the pH cross-sensitivity of most previously reported sodium nano-sensors. We demonstrate that this nano-sensor is non-toxic, boasts a 200 μM detection limit, and is over 1000 times more selective for sodium than potassium. Further, the in vitro photoacoustic calibration curve presented demonstrates the potential of this nano-sensor for performing the in vivo chemical imaging of sodium over the entire physiologically relevant concentration range.

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

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.

Stability of Ti3C2Tx MXene Films and Devices under Clinical Sterilization Processes

MXenes are being heavily investigated in biomedical research, with applications ranging from regenerative medicine to bioelectronics. To enable the adoption and integration of MXenes into therapeutic platforms and devices, however, their stability under standard sterilization procedures must be established. Here, we present a comprehensive investigation of the electrical, chemical, structural, and mechanical effects of common thermal (autoclave) and chemical (ethylene oxide (EtO) and H2O2 gas plasma) sterilization protocols on both thin-film Ti3C2Tx MXene microelectrodes and mesoscale arrays made from Ti3C2Tx-infused cellulose-elastomer composites. We also evaluate the effectiveness of the sterilization processes in eliminating all pathogens from the Ti3C2Tx films and composites. Post-sterilization analysis revealed that autoclave and EtO did not alter the DC conductivity, electrochemical impedance, surface morphology, or crystallographic structure of Ti3C2Tx and were both effective at eliminating E. coli from both types of Ti3C2Tx-based devices. On the other end, exposure to H2O2 gas plasma sterilization for 45 min induced severe degradation of the structure and properties of Ti3C2Tx films and composites. The stability of the Ti3C2Tx after EtO and autoclave sterilization and the complete removal of pathogens establish the viability of both sterilization processes for Ti3C2Tx-based technologies.

Sensing and Stimulation Applications of Carbon Nanomaterials in Implantable Brain-Computer Interface

Implantable brain–computer interfaces (BCIs) are crucial tools for translating basic neuroscience concepts into clinical disease diagnosis and therapy. Among the various components of the technological chain that increases the sensing and stimulation functions of implanted BCI, the interface materials play a critical role. Carbon nanomaterials, with their superior electrical, structural, chemical, and biological capabilities, have become increasingly popular in this field. They have contributed significantly to advancing BCIs by improving the sensor signal quality of electrical and chemical signals, enhancing the impedance and stability of stimulating electrodes, and precisely modulating neural function or inhibiting inflammatory responses through drug release. This comprehensive review provides an overview of carbon nanomaterials’ contributions to the field of BCI and discusses their potential applications. The topic is broadened to include the use of such materials in the field of bioelectronic interfaces, as well as the potential challenges that may arise in future implantable BCI research and development. By exploring these issues, this review aims to provide insight into the exciting developments and opportunities that lie ahead in this rapidly evolving field.

Shaping the future of European epilepsy research: Final meeting report from EPICLUSTER

Collaboration is essential to the conduct of basic, applied and clinical research and its translation into the technologies and treatments urgently needed to improve the lives of people living with brain diseases and the health professionals who care for them. EPICLUSTER was formed in 2019 by the European Brain Research Area (EBRA) to support the coordination of epilepsy research in Europe. A key objective was to provide a platform to discuss shared research priorities by bringing together scientists and clinicians with multiple stakeholders including patient organisations and industry and the networks and infrastructures that provide healthcare and support research. Additional objectives were to facilitate access and sharing of data and biosamples, working together to ensure epilepsy is a priority for research funding, and embedding a culture of public and patient involvement (PPI) among epilepsy researchers. In this meeting report, we summarise the shared research priorities discussed by the leadership of EPICLUSTER at the recent final meeting. We also briefly review the discussion on patient and industry priorities, guidance on starting PPI for epilepsy researchers, and the sustainability of funding and infrastructures needed to ensure a comprehensive stakeholder-embedded community for epilepsy research.

Transparent neural interfaces: challenges and solutions of microengineered multimodal implants designed to measure intact neuronal populations using high-resolution electrophysiology and microscopy simultaneously

The aim of this review is to present a comprehensive overview of the feasibility of using transparent neural interfaces in multimodal in vivo experiments on the central nervous system. Multimodal electrophysiological and neuroimaging approaches hold great potential for revealing the anatomical and functional connectivity of neuronal ensembles in the intact brain. Multimodal approaches are less time-consuming and require fewer experimental animals as researchers obtain denser, complex data during the combined experiments. Creating devices that provide high-resolution, artifact-free neural recordings while facilitating the interrogation or stimulation of underlying anatomical features is currently one of the greatest challenges in the field of neuroengineering. There are numerous articles highlighting the trade-offs between the design and development of transparent neural interfaces; however, a comprehensive overview of the efforts in material science and technology has not been reported. Our present work fills this gap in knowledge by introducing the latest micro- and nanoengineered solutions for fabricating substrate and conductive components. Here, the limitations and improvements in electrical, optical, and mechanical properties, the stability and longevity of the integrated features, and biocompatibility during in vivo use are discussed.

Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

Multimodal platforms combining electrical neural recording and stimulation, optogenetics, optical imaging, and magnetic resonance (MRI) imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific research. Electrically conductive, optically transparent, and MRI-compatible electrodes can optimally combine all modalities. Graphene as a suitable electrode candidate material can be grown via chemical vapor deposition (CVD) processes and sandwiched between transparent biocompatible polymers. However, due to the high graphene growth temperature (≥ 900 °C) and the presence of polymers, fabrication is commonly based on a manual transfer process of pre-grown graphene sheets, which causes reliability issues. In this paper, we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural interfaces. Our fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and geometry. They also exhibit the highest charge storage capacity (CSC) reported to date among all previously fabricated CVD graphene electrodes. Our graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse illumination. Additionally, we show here, for the first time, that CVD graphene electrodes do not cause any image artifact in a 3T MRI scanner. These results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal electrodes. Our fabricated graphene electrodes enable multimodal neural recording, electrical and optogenetic stimulation, while allowing for optical imaging, as well as, artifact-free MRI studies.

Combining Cortical Voltage Imaging and Hippocampal Electrophysiology for Investigating Global, Multi-Timescale Activity Interactions in the Brain

A new generation of optogenetic tools for analyzing neural activity has been contributing to the elucidation of classical open questions in neuroscience. Specifically, voltage imaging technologies using enhanced genetically encoded voltage indicators have been increasingly used to observe the dynamics of large circuits at the mesoscale. Here, we describe how to combine cortical wide-field voltage imaging with hippocampal electrophysiology in awake, behaving mice. Furthermore, we highlight how this method can be useful for different possible investigations, using the characterization of hippocampal-neocortical interactions as a case study.

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

Hippocampal seizures are a defining feature of mesial temporal lobe epilepsy (MTLE). Area CA1 of the hippocampus is commonly implicated in the generation of seizures, which may occur because of the activity of endogenous cell populations or of inputs from other regions within the hippocampal formation. Simultaneously observing activity at the cellular and network scales in vivo remains challenging. Here, we present a novel technology for simultaneous electrophysiology and multicellular calcium imaging of CA1 pyramidal cells (PCs) in mice enabled by a transparent graphene-based microelectrode array (Gr MEA). We examine PC firing at seizure onset, oscillatory coupling, and the dynamics of the seizure traveling wave as seizures evolve. Finally, we couple features derived from both modalities to predict the speed of the traveling wave using bootstrap aggregated regression trees. Analysis of the most important features in the regression trees suggests a transition among states in the evolution of hippocampal seizures.

Neurophotonic tools for microscopic measurements and manipulation: status report

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Emerging approaches for sensing and modulating neural activity enabled by nanocarbons and carbides

Devices that can record or modulate neural activity are essential tools in clinical diagnostics and monitoring, basic research, and consumer electronics. Realizing stable functional interfaces between manmade electronics and biological tissues is a longstanding challenge that requires device and material innovations to meet stringent safety and longevity requirements and to improve functionality. Compared to conventional materials, nanocarbons and carbides offer a number of specific advantages for neuroelectronics that can enable advances in functionality and performance. Here, we review the latest emerging trends in neuroelectronic interfaces based on nanocarbons and carbides, with a specific emphasis on technologies developed for use in vivo. We highlight specific applications where the ability to tune fundamental material properties at the nanoscale enables interfaces that can safely and precisely interact with neural circuits at unprecedented spatial and temporal scales, ranging from single synapses to the whole human body.

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.