Academic literature on the topic 'Graphene neurons'

We demonstrate the formation of a micro-roll for neuron encapsulation with a self-folding graphene/parylene-C bilayer film, and show the importance of using pores on the micro-roll to allow the encapsulated neurons to interact with the surroundings.

In this study, we explore the use of electrically active graphene foam as a scaffold for the culture of human-derived neurons. Human embryonic stem cell (hESC)-derived cortical neurons fated as either glutamatergic or GABAergic neuronal phenotypes were cultured on graphene foam. We show that graphene foam is biocompatible for the culture of human neurons, capable of supporting cell viability and differentiation of hESC-derived cortical neurons. Based on the findings, we propose that graphene foam represents a suitable scaffold for engineering neuronal tissue and warrants further investigation as a model for understanding neuronal maturation, function and circuit formation.

AbstractA synapse is a junction between two biological neurons, and the strength, or weight of the synapse, determines the communication strength between the neurons. Building a neuromorphic (i.e. neuron isomorphic) computing architecture, inspired by a biological network or brain, requires many engineered synapses. Furthermore, recent investigation in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. We propose a graphene-based synapse model as a core element to enable large-scale photonic neural networks based on on-chip multiwavelength techniques. This device consists of an electro-absorption modulator embedded in a microring resonator. We also introduce an encoding protocol that allows for the representation of synaptic weights on our photonic device with 15.7 bits of resolution using current control hardware. Recent work has suggested that graphene-based modulators could operate in excess of 100 GHz. Combined with our work, such a graphene-based synapse could enable applications for ultrafast and online learning.

Object Graphene possesses unique electrical, physical, and chemical properties that may offer significant potential as a bioscaffold for neuronal regeneration after spinal cord injury. The purpose of this investigation was to establish the in vitro biocompatibility of pristine graphene for interface with primary rat cortical neurons. Methods Graphene films were prepared by chemical vapor deposition on a copper foil catalytic substrate and subsequent apposition on bare Permanox plastic polymer dishes. Rat neuronal cell culture was grown on graphene-coated surfaces, and cell growth and attachment were compared with those on uncoated and poly-d-lysine (PDL)-coated controls; the latter surface is highly favorable for neuronal attachment and growth. Live/dead cell analysis was conducted with flow cytometry using ethidium homodimer-1 and calcein AM dyes. Lactate dehydrogenase (LDH) levels—indicative of cytotoxicity—were measured as markers of cell death. Phase contrast microscopy of active cell culture was conducted to assess neuronal attachment and morphology. Results Statistically significant differences in the percentage of live or dead neurons were noted between graphene and PDL surfaces, as well as between the PDL-coated and bare surfaces, but there was little difference in cell viability between graphene-coated and bare surfaces. There were significantly lower LDH levels in the graphene-coated samples compared with the uncoated ones, indicating that graphene was not more cytotoxic than the bare control surface. According to phase contrast microscopy, neurons attached to the graphene-coated surface and were able to elaborate long, neuritic processes suggestive of normal neuronal metabolism and morphology. Conclusions Further use of graphene as a bioscaffold will require surface modification that enhances hydrophilicity to increase cellular attachment and growth. Graphene is a nanomaterial that is biocompatible with neurons and may have significant biomedical applications.

Aim: To differentiate mesenchymal stem cells into functional dopaminergic neurons using an electrospun polycaprolactone (PCL) and graphene (G) nanocomposite. Methods: A one-step approach was used to electrospin the PCL nanocomposite, with varying G concentrations, followed by evaluating their biocompatibility and neuronal differentiation. Results: PCL with exiguous graphene demonstrated an ideal nanotopography with an unprecedented combination of guidance stimuli and substrate cues, aiding the enhanced differentiation of mesenchymal stem cells into dopaminergic neurons. These newly differentiated neurons were seen to exhibit unique neuronal arborization, enhanced intracellular Ca2+ influx and dopamine secretion. Conclusion: Having cost-effective fabrication and room-temperature storage, the PCL-G nanocomposites could pave the way for enhanced neuronal differentiation, thereby opening a new horizon for an array of applications in neural regenerative medicine.

The implantation of stem cells in vivo is the ideal approach for the restoration of normal life functions, such as replenishing the decreasing levels of affected dopaminergic (DA) neurons during neurodegenerative disease conditions. However, combining stem cells with biomaterial scaffolds provides a promising strategy for engineering tissues or cellular delivery for directed stem cell differentiation as a means of replacing diseased/damaged tissues. In this study, mouse mesenchymal stem cells (MSCs) were differentiated into DA neurons using sonic hedgehog, fibroblast growth factor, basic fibroblast growth factor, and brain-derived neurotrophic factor, while they were cultured within collagen-coated 3D graphene foams (GF). The differentiation into DA neurons within the collagen-coated GF and controls (collagen gels, plastic) was confirmed using β-III tubulin, tyrosine hydroxylase (TH), and NeuN positive immunostaining. Enhanced expression of β-III tubulin, TH, and NeuN and an increase in the average neurite extension length were observed when cells were differentiated within collagen-coated GF in comparison with collagen gels. Furthermore, these graphene-based scaffolds were not cytotoxic as MSC seemed to retain viability and proliferated substantially during in vitro culture. In summary, these results suggest the utility of 3D graphene foams towards the differentiation of DA neurons from MSC, which is an important step for neural tissue engineering applications.

Printed electronic devices have demonstrated their applicability in complex electronic circuits. There is recent progress in the realization of neuromorphic computing systems (NCSs) to implement basic synaptic functions using solution-processed materials. However, a fully printed neuron is yet to be realised. We demonstrate a fully printed artificial neuromorphic circuit on flexible polyimide (PI) substrate. Characteristic features of individual components of the printed system were guided by the software training of the NCS. The printing process employs graphene ink for passive structures and In2O3 as active material to print a two-input artificial neuron on PI. To ensure a small area footprint, the thickness of graphene film is tuned to target a resistance and to obtain conductors or resistors. The sheet resistance of the graphene film annealed at 300 °C can be adjusted between 200 Ω and 500 kΩ depending on the number of printed layers. The fully printed devices withstand a minimum of 2% tensile strain for at least 200 cycles of applied stress without any crack formation. The area usage of the printed two-input neuron is 16.25 mm2, with a power consumption of 37.7 mW, a propagation delay of 1 s, and a voltage supply of 2 V, which renders the device a promising candidate for future applications in smart wearable sensors.

Graphene displays properties that make it appealing for neuroregenerative medicine, yet the potential of large-scale highly-crystalline graphene as a conductive peripheral neural interface has been scarcely investigated. In particular, pristine graphene offers enhanced electrical properties that can be advantageous for nervous system regeneration applications. In this work, we investigate graphene potential as peripheral nerve interface. First, we perform an unprecedented analysis aimed at revealing how the typical polymeric coatings for neural cultures distribute on graphene at the nanometric scale. Second, we examine the impact of graphene on the culture of two established cellular models for peripheral nervous system: PC12 cell line and primary embryonic rat dorsal root ganglion (DRG) neurons, showing a better and faster axonal elongation using graphene. We then observe that the axon elongation in the first days of culture correlates to an altered nerve growth factor (NGF) axonal transport, with a reduced number of retrogradely moving NGF vesicles in favor of stalled vesicles. We thus hypothesize that the axon elongation observed in the first days of culture could be mediated by this pool of NGF vesicles locally retained in the medial/distal parts of axons. Furthermore, we investigate electrophysiological properties and cytoskeletal structure of peripheral neurons. We observe a reduced neural excitability and altered membrane potential together with a reduced inter-microtubular distance on graphene and correlate these electrophysiological and structural reorganizations of axon physiology to the observed vesicle stalling. Finally, the potential of another 2D material as neural interface, tungsten disulfide, is explored.

Dans la lignée des progrès technologiques récents en électronique, ces dernières décennies ont vu l’émergence d’une variété de systèmes permettant l’interface bioélectronique, allant de la mesure de l’activité électrique émise par l’ensemble du cerveau jusqu’à la mesure du signal émis par un neurone unique. Bien que des interfaces électroniques avec les neurones ont montré leur utilité pour des applications cliniques et sont communément utilisés par les neurosciences fondamentales, leurs performances sont encore très limitées, notamment en raison de l’incompatibilité relative entre les systèmes à l’état solide et le vivant. Dans ce travail de thèse, nous avons étudié des techniques et des matériaux nouveaux permettant une approche alternative et qui pourraient améliorer le suivi de l’activité de réseaux de neurones cultivés in situ et à terme la performance des neuroprothèses in vivo. Dans ce travail, des réseaux de nanofils de silicium et des microélectrodes en diamant sont élaborés pour respectivement améliorer la résolution spatiale et la stabilité des électrodes dans un environnement biologique. Un point important de cette thèse est également l’évaluation des performances de transistors à effet de champ en graphène pour la bio électronique. En raison des performances remarquables et combinées sur les aspects électrique, mécanique et chimique du graphène, ce matériau apparaît comme un candidat très prometteur pour la réalisation d’une électronique permettant une interface stable et sensible avec un réseau de neurones. Nous montrons dans ce travail l’affinité exceptionnelle des neurones avec une surface de graphène brut et la réalisation d’une électronique de détection rapide et sensible à base de transistor en graphène
In line with the technological progress of last decades a variety of adapted bioelectrical interfaces was developed to record electrical activity from the nervous system reaching from whole brain activity to single neuron signaling. Although neural interfaces have reached clinical utility and are commonly used in fundamental neuroscience, their performance is still limited. In this work we investigated alternative materials and techniques, which could improve the monitoring of neuronal activity of cultured networks, and the long-term performance of prospective neuroprosthetics. While silicon nanowire transistor arrays and diamond based microelectrodes are proposed for improving the spatial resolution and the electrode stability in biological environment respectively, the main focus of this thesis is set on the evaluation of graphene based field effect transistor arrays for bioelectronics. Due to its outstanding electrical, mechanical and chemical properties graphene appears as a promising candidate for the realization of chemically stable flexible electronics required for long-term neural interfacing. Here we demonstrate the outstanding neural affinity of pristine graphene and the realization of highly sensitive fast graphene transistors for neural interfaces

2011/2012
Nanotechnology underwent a very rapid development in the last decades, thanks to the invention of different techniques that allow reaching the nanoscale. The great interest in this area arises from the variety of possible applications in different fields, such as electronics, where the miniaturization of components is a key factor, but also medicine. The creation of smart systems able to carry out a specific task in the body in a controlled way, either in diagnosis or therapy or tissue engineering, is the ultimate goal of a newborn area of research, called nanomedicine. In fact, to reach such an outstanding objective, a nanometer‐sized material is needed and carbon nanotubes (CNTs) are among the most promising candidates. The aim of this thesis was to study this opportunity and, in particular, the possible application of carbon nanotubes for spinal network repairing. After a review of the main features of neuronal network systems and the most common techniques to study their functionality, possible applications of nanotechnology for nanomedicine purposes are considered, focusing the attention on CNTs as neuronal interface in nerve tissue engineering. The work can be divided into two big parts. In the first part the impact of carbon nanotubes on various neuronal systems was studied. Different form of carbonaceous materials (carbon nanotubes, nanohorns and graphene) were deposited in a homogeneous way on a glass surface playing with organic functionalization and different deposition techniques. Hippocampal neuronal cells were grown on their surface to better understand how morphology and conductivity of the material could influence the activity of the neuronal network evidencing how both these characteristics could affect the electrophysiological properties of neurons. Then, also spinal neurons were grown on carbon nanotubes network deposited on a glass substrate to evaluate, for the first time, the impact of carbon nanotubes on this kind of cells. The tight interaction between these two materials appeared to cause a faster maturation of the spinal neurons with respect II to the control grown on a glass substrate. The long-term impact on a complex tissue (spinal cord slice) grown on carbon nanotubes carpet was also studied. The intimate interaction between the two materials observed by TEM and SEM analysis caused an increase in dimensions and number of neuronal fibers that comes out from the body of a spinal cord slice. An increase in electrophysiological activity of all neuronal network of the slice was also reported. In the second part of the work different conductive biocompatible nanocomposite materials based on carbon nanotubes and “artificial” polymers (such as Nafion, PVA, PET, PEI, PDMS and PANI) were investigated. The idea is to test these materials as neuronal prosthesis to repair spinal cord damage. All the prepared scaffolds showed CNTs on the surface favoring CNTs-neurons interaction. To address this aim different techniques and different organic functionalizations of CNTs were utilized to control supramolecular interactions between the nanomaterial and polymers orienting the deposition of the CNTs and preventing their aggregation. After that, an innovative method to study the possible ability of this nanocomposite materials to transmit a neuronal signal between two portions of spinal cord was designed. Functionalization of gold surfaces with thiolated carbon nanotubes have been conducted in order to develop suitable devices for neuronal stimulation and consequent spinal cord lesions repairing. In particular thiol groups were introduced on the graphitic surface of carbon nanotubes by means of covalent functionalization. First of all, the interaction of CNTs with gold nanoparticles has been evaluated, then a gold surface has been coated by means of contact printing technique with a homogeneous film of CNTs. This hybrid material could be useful to produce innovative electrodes for neuronal stimulation
XXV Ciclo
1985

Le graphène, une couche monoatomique de carbone, est étudié comme matériau pourconstruire ou encapsuler des biocapteurs afin d’adresser les problèmes de durabilitérencontrés avec les implants intra-corticaux. Ces derniers sont des outils essentiels pour lesprojets médicaux de neuro-réhabilitation afin d’enregistrer les signaux de motoneuronesuniques dans le cerveau. Les implants actuels sont invasifs et leur efficacité est limitée dans letemps par la réaction de rejet des tissus. En combinant une synthèse de graphène optimiséeà cet usage (monocouche continue sur plusieurs cm²) et son intégration dans des capteursélectroniques ultra-sensibles, protégés par des polymères bioactifs, cette thèse propose unenouvelle approche pluridisciplinaire pour construire des implants offrant une meilleurebioacceptance. Au moyen de méthodes d’intégration innovantes et d’études ducomportement du graphène in-vivo et in-vitro, nous évaluons expérimentalement lafaisabilité d’intégration du graphène dans les futures interfaces cerveau machines pour desprojets médicaux au long terme
Graphene, an atomically thin layer of carbon, is investigated as a biosensing andcoating material in order to address the long term durability issues of invasive intracorticalimplants. These devices are essential tools to record specific single motor neurons activity formedical applications aiming at healing neural injuries. Today’s implants suffer from their highinvasiveness. It is responsible for local inflammation that leads to the failure in unique neuronsactivity recordings in the motor cortex on a long term basis. By combining a monolayergraphene growth and transfer with an ultra-sensitive electronic integration and a biochemicalfunctionalization, this thesis proposes a new multidisciplinary approach to build intracorticalimplants with an improved bioacceptance. By using innovative methods of grapheneintegration in implants, and in-vitro and in-vivo studies to assess the reactions of living tissuesto graphene, we provide an overview of graphene’s potential contribution to future brainmachine interfaces for long term medical projects

La tecnologia d’implants neuronals en medicina té com a objectiu restaurar la funcionalitat del sistema nerviós en casos de degeneració o dany greu registrant o estimulant l’activitat elèctrica del teixit nerviós. Els implants neuronals disponibles actualment ofereixen una eficàcia clínica modesta, en part a causa de les limitacions que tenen els metalls utilitzats en la interfície elèctrica amb el teixit. Aquests materials comprometen la resolució de la interfície i, per tant, la restauració funcional amb el rendiment i l’estabilitat. En aquest treball presento uns implants neuronals flexibles basats en una pel·lícula prima de grafè porós nanoestructurat i biocompatible que proporciona una interfície neural bidireccional estable i d’alt rendiment. En comparació amb els dispositius de microelectrodos de platí estàndard, elèctrodes de 25 μm de diàmetre basats en grafè ofereixen una impedància significativament menor i poden injectar de manera segura 200 vegades més càrrega durant més de 100 milions de polsos. N’evaluo les seves capacitats in vivo registrant activitat epicortical amb alta fidelitat i alta resolució, estimulant subconjunts d’axons dins del nervi ciàtic amb llindars de corrent baixos i alta selectivitat i modulant l’activitat de la retina amb alta precisió. La tecnologia de pel·lícula fina de grafè aquí descrita té el potencial de convertir-se en el nou punt de referència per la pròxima generació de tecnologia d’implants neuronals.
La tecnología de implantes neuronales en medicina tiene como objetivo restaurar la funcionalidad del sistema nervioso en casos de degeneración o daño grave registrando o estimulando la actividad eléctrica del tejido nervioso. Los implantes neurales disponibles actualmente ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantean los metales utilizados en la interfaz eléctrica con el tejido. Dichos materiales comprometen la resolución de la interfaz y, por lo tanto, la restauración funcional con el rendimiento y la estabilidad. En este trabajo presento unos implantes neuronales flexibles basados en una película delgada de grafeno poroso nanoestructurado y biocompatible que proporciona una interfaz neural bidireccional estable y de alto rendimiento. En comparación con los dispositivos de microelectrodos de platino estándar, electrodos de 25 μm de diámetro basados en grafeno ofrecen una impedancia significativamente menor y pueden inyectar de forma segura 200 veces más carga durante más de 100 millones de pulsos. Aquí evaluo sus capacidades in vivo registrando actividad epicortical con alta fidelidad y alta resolución, estimulando subconjuntos de axones dentro del nervio ciático con umbrales de corriente bajos y alta selectividad y modulando la actividad de la retina con alta precisión. La tecnología de película fina de grafeno aquí descrita tiene el potencial de convertirse en el nuevo punto de referencia para la próxima generación de tecnología de implantes neuronales.
Neural implants technology in medicine aims to restore nervous system functionality in cases of severe degeneration or damage by recording or stimulating the electrical activity of the nervous tissue. Currently available neural implants offer a modest clinical efficacy partly due to the limitations posed by the metals used at the electrical interface with the tissue. Such materials compromise interfacing resolution, and therefore functional restoration, with performance and stability. In this work, I present flexible neural implants based on a biocompatible nanostructured porous graphene thin film that provides a stable and high performance bidirectional neural interface. Compared to standard platinum microelectrode devices, the graphene-based electrodes of 25 μm diameter offer significantly lower impedance and can safely inject 200 times more charge for more than 100 million pulses. I assessed their performance in vivo by recording high fidelity and high resolution epicortical activity, by stimulating subsets of axons within the sciatic nerve with low thresholds and high selectivity and by modulating the retinal activity with high precision. The graphene thin film technology I describe here has the potential to become the new performance benchmark for the next generation of neural implant technology.
Universitat Autònoma de Barcelona. Programa de Doctorat en Enginyeria Electrònica i de Telecomunicació

En els últims anys s’han produït desenvolupaments tecnològics innovadors en el camp dels implants neuronals per a aplicacions mèdiques. La comprensió de el cervell humà es considera com un dels majors reptes científics del nostre temps; com a conseqüència, estem sent testimonis d’una intensificació de la investigació en el desenvolupament de les interfícies cervell-màquina (IMC) per llegir i estimular l’activitat cerebral. No obstant això, els implants neuronals actualment disponibles ofereixen una eficàcia clínica modesta, en part a causa de les limitacions que plantegen la invasivitat dels materials. Aquests materials comprometen la resolució de la interfície, el rendiment i l’estabilitat a llarg termini dels implants. El desenvolupament d’una electrònica flexible que utilitzi materials biocompatibles és clau per al desenvolupament d’implants neuronals mínimament invasius, que puguin implantar-se de forma crònica. Un camp d’investigació molt prometedor, és l’ús de materials bidimensionals, com el grafè, per a aplicacions bioelectròniques. El transistor d’efecte de camp en solució de grafè (gSGFET) és una de d’aquestes noves tecnologies neuronals emergents. Aquests dispositius poden superar les limitacions esmentades anteriorment gràcies a les extraordinàries propietats del grafè, com ara la seva alta flexibilitat mecànica, estabilitat electroquímica, biocompatibilitat i alta sensibilitat. En aquesta tesi doctoral, s’han fabricat matrius de gSGFET i s’han optimitzat iterativament en termes de sensibilitat i relació senyal / soroll, adoptant mètodes de microfabricació a escala d’oblia. S’ha caracteritzat el soroll 1 / f en els gSGFETs i s’ha optimitzat amb un tractament UVO de la interfície metall / grafè i desacoblant el grafè del substrat utilitzant diferents nanomaterials com ara l’encapsulació del grafè amb nitrur de bor hexagonal (hBN), monocapes autoacoblades i grafè bicapa. A més, s’han fabricat amb èxit sondes neuronals epicorticals i intracorticals flexibles, que contenien matrius de gSGFET, i s’han fet enregistraments de microelectrocorticografia in vivo en rosegadors. S’han inserit dispositius intracorticals flexibles en el cervell utilitzant un protocol de reforç de la capa posterior del dispositiu amb proteïna de fibroïna de seda biorresistent. Els resultats presentats en aquesta tesi demostren la superior resolució espai-temporal dels gSGFET en comparació amb la tecnologia estàndard de microelèctrodes; en particular, la capacitat de mapejar amb alta fidelitat, l’activitat de molt baixa freqüència (ISA, <0,1 Hz) juntament amb els senyals en el típic ample de banda dels LFP. Avui dia se sap que l’activitat cerebral de molt baixa freqüència, contribueix a la fisiopatologia de diversos trastorns neurològics com el vessament cerebral, la lesió cerebral traumàtica, la migranya i l’epilèpsia. No obstant això, aquesta activitat rares vegades es registra a causa de les limitacions tècniques intrínseques dels elèctrodes convencionals acoblats a la CA. S’han obtingut mesures neuronals amb sondes de profunditat flexibles i multicanal de grafè (gDNP) en models animals desperts amb convulsions i epilèpsia. S’ha detectat i cartografiat l’AIS a través de diferents capes corticals i regions subcorticals, registrant simultàniament l’activitat epilèptica en bandes de freqüència més convencionals (1-600Hz). A més, com a part d’aquesta tesi s’ha demostrat també l’estabilitat i funcionalitat de registres a llarg termini, així com la biocompatibilitat dels gDNPs. La tecnologia bioelectrònica basada en grafè aquí descrita té el potencial d’esdevenir una eina de referència per a l’electrofisiologia d’ample de banda complet. Es preveu que aquesta tecnologia tingui un gran impacte en una comunitat àmplia i multidisciplinària que inclogui investigadors en neurotecnologia, enginyers biomèdics, neurocientífics que estudien la dinàmica cortical de banda ampla associada amb el comportament espontani i /o els estats cerebrals, així com investigadors clínics interessats en el paper de l’activitat de molt baixa freqüència en epilèpsia, els accidents cerebrovasculars i la migranya.
En los últimos años se han producido nuevos desarrollos tecnológicos en el campo de los implantes neuronales para aplicaciones médicas. La comprensión del cerebro humano se considera uno de los mayores desafíos científicos de nuestro tiempo; como consecuencia, estamos siendo testigos de una intensificación de la investigación en el desarrollo de las interfaces cerebro-máquina (IMC) para leer y estimular la actividad cerebral. No obstante, los implantes neuronales actualmente disponibles ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantea la invasividad de los materiales. Esos materiales comprometen la resolución de la interfaz, el rendimiento y la estabilidad a largo plazo de los implantes neurales. El desarrollo de una electrónica flexible que utilice materiales biocompatibles es clave para la realización de implantes neuronales mínimamente invasivos que puedan implantarse de forma crónica. Un campo de investigación muy prometedor es el uso de materiales bidimensionales, como el grafeno, para aplicaciones bioelectrónicas. El transistor de efecto de campo en solución de grafeno (gSGFET) es una de dichas nuevas tecnologías neurales emergentes. Estos dispositivos pueden superar las limitaciones mencionadas anteriormente gracias a las extraordinarias propiedades del grafeno, como su alta flexibilidad mecánica, estabilidad electroquímica, biocompatibilidad y sensibilidad. En esta tesis doctoral, se han fabricado matrices de gSGFET y se han optimizado iterativamente en términos de sensibilidad y relación señal/ruido, adoptando métodos de microfabricación a escala de oblea. Se ha caracterizado el ruido 1/f en los gSGFETs y optimizado haciendo un tratamiento UVO en la interfaz metal/grafeno y desacoplando el canal de grafeno del sustrato utilizando diferentes nanomateriales como la encapsulación con nitruro de boro hexagonal (hBN), monocapas autoensambladas y bicapas de grafeno. Además, se han fabricado con éxito sondas neurales epicorticales e intracorticales flexibles con matrices de gSGFET y se han utilizado durante las medidas de microelectrocorticografía in vivo en roedores. Se han insertado dispositivos intracorticales flexibles en el cerebro utilizando un protocolo de refuerzo de la capa posterior de los dispositivos con proteína de fibroína de seda biorresistente. Los resultados presentados en esta tesis demuestran la superior resolución espacio-temporal de los gSGFET en comparación con la tecnología estándar de microelectrodos; en particular, referente a la capacidad de mapear con alta fidelidad, la actividad de muy baja frecuencia (ISA, < 0,1 Hz) junto con las señales en el típico ancho de banda LFP. Hoy en día se sabe que la actividad cerebral de muy baja frecuencia, contribuye a la fisiopatología de varios trastornos neurológicos como el derrame cerebral, la lesión cerebral traumática, la migraña y la epilepsia. Sin embargo, esta actividad rara vez se registra debido a las limitaciones técnicas intrínsecas de los electrodos convencionales acoplados a la CA. Se han obtenido registros con sondas neuronales de profundidad de grafeno (gDNP) en modelos animales de epilepsia. Se detectó ISA a través de diferentes capas corticales y regiones subcorticales, registrando simultáneamente la actividad epiléptica en bandas de frecuencia más convencionales (1-600Hz). Además, se ha demostrado también la evaluación de la estabilidad y funcionalidad en registros crónicos, así como la biocompatibilidad del gDNP. La tecnología bioelectrónica basada en el grafeno aquí descrita tiene el potencial de convertirse en una herramienta de referencia para la electrofisiología de ancho de banda completo. Se prevé que esta tecnología tenga un gran impacto en una comunidad amplia y multidisciplinaria que incluya investigadores en neurotecnología, ingenieros biomédicos, neurocientíficos que estudien la dinámica cortical de banda ancha asociada con el comportamiento espontáneo y/o los estados cerebrales, así como investigadores clínicos interesados en la actividad de baja frecuencia en la epilepsia, los accidentes cerebrovasculares y la migraña.
Recent years have witnessed novel technology developments of neural implants for medical applications which are expected to pave the way to unveil functionalities of the central nervous system. Understanding the human brain is commonly considered one of the biggest scientific challenges of our time; as a consequence, we are witnessing an intensified research in the development of brain-machine-interfaces (BMIs), which would allow us to both read and stimulate brain activity. Nevertheless, currently available neural implants offer a modest clinical efficacy, partly due to the limitations posed by the invasiveness of the implants materials and technology and by the metals used at the electrical interface with the tissue. Such materials compromise the interfacing resolution, the performance and the long term stability of neural implants. Development of flexible electronics using biocompatible materials is key for the realisation of minimally invasive neural implants, which can be chronically implanted without causing rejection from the immune system. A relatively young yet very promising research field, that is increasingly drawing attention is the use of two dimensional materials, such as graphene, for bioelectronic applications. Graphene solution-gated field effect transistor (gSGFET) is one of several emerging new neural technologies. These devices can overcome the above-mentioned limitations thanks to the outstanding properties of graphene, such as mechanical flexibility, electrochemical inertness, biocompatibility and high sensitivity. In this PhD thesis, arrays of gSGFETs have been fabricated and iteratively optimized in terms of sensitivity and signal-to-noise ratio, adopting wafer-scale micro-fabrication methods. The 1/f noise in gSGFETs has been characterised and the optimisation of both, contact and channel noises was achieved by UVO-treatment at the metal/graphene interface, as well as by decoupling the graphene channel from the substrate, using different nanomaterials such as graphene encapsulation with hexagonal boron nitride (hBN), self assembled monolayers and double transferred graphene. Moreover, flexible and ultra-thin epicortical and intracortical neural probes, containing arrays of gSGFETs, have been successfully fabricated and used during in vivo microelectrocorticography recordings in anaesthesized and awake rodents. Flexible intracortical devices were inserted into the brain using a back-coating stiffening protocol with bioresobable silk fibroin protein, developed during this PhD thesis. The results presented in this PhD demonstrate the superior spatio-temporal resolution of gSGFETs compared to standard microelectordes technology; particularly the ability to map with high fidelity, infraslow activity (ISA, < 0.1 Hz) together with signals in the typical local field potential bandwidth. Today it is known that infraslow brain activity, including spreading depolarisations, contribute to the pathophysiology of several neurological disorders such as stroke, traumatic brain injury, migraine and epilepsy. However, this activity is seldom recorded due to intrinsic technical limitations of conventional AC-coupled electrodes. To demonstrate the usefulness of the developed flexible gSGFET arrays technology, recordings have been obtained with multichannel flexible graphene depth neural probes (gDNP) in relevant awake animal models of seizures and established epilepsy. ISA was detected and mapped through different cortical layers and subcortical regions, whilst simultaneously recording epileptiform activity in more conventional frequency bands (1-600Hz). Furthermore, the assessment of the long term recording stability and functionality, as well as biocompatibility of the gDNP has also been demonstrated as part of this thesis. The graphene based bioelectronic technology here described has the potential to become a gold standard tool for full bandwidth electrophysiology. This technology is envisioned to have a great impact on a broad and multidisciplinary community including neurotechnology researchers, biomedical engineers, neuroscientists studying wide-band cortical dynamics associated with spontaneous behaviour and/or brain states, as well as clinical researchers interested in the role of infraslow activity in epilepsy, stroke and migraine.

The objective of my Ph.D. thesis is the investigation of the role of Single Layer Graphene (SLG) as a biointerface for its possible future exploitation in various biomedical applications; in particular for the development of biosensors, substrates for regenerative medicine, interfacing platforms for better recording of electrophysiological activity of neuronal networks, among others. This Ph.D. project is multidisciplinary involving both the material transfer and characterization part from one side and the biological part from another side. The material part offers an in-depth explanation of SLG synthesis, transfer, characterization and functionalization while the biological section sheds light on the studies performed for investigation of the behavior of different types of cell lines on SLG substrates. For better understanding of the sequence of the performed work, I have divided this thesis into separate chapters. In the beginning and end of every chapter, I added an introduction and conclusions related to it. Chapter 1 acts as a general introduction to graphene and graphene-related materials where a detailed explanation on the evolution of those materials as a cell interface is provided leading to the introduction of SLG in the end of this chapter along with its production process. Chapter 2 is oriented on the surface characterization of SLG substrates; in this chapter, I described the SLG transfer method, creation of the micrometric ablated geometric patterns on the transferred substrates using excimer laser micromachining, a technique developed in our lab, then further functionalization of the substrates and finally all the techniques employed for their physicochemical characterization. Chapter 3 is dedicated to the biological part of the project; i.e. studying the behavior of different cell lines on the SLG substrates. In this chapter, I have described and explained the interest of using the selected cell lines and the experiments that were performed on them. Chapter 4 has been devoted to a complete and separate project that I performed in collaboration with the Neuroscience and Brain Technologies department. The main focus of the project was the functionalization of the commercial multi-electrode arrays (MEAs) with SLG and studying the neuronal network activity on them throughout the complete network development. Although the main focus of my Ph.D. project was studying SLG biointerface, I have also been involved in side projects, among which, studying the neuronal-like response of mouse neuroblastoma (N2a) living cells to nanoporous patterns of thin supported anodic alumina which I have described in Appendix A, and studying the surface potential of graphene by polyelectrolyte coating which I have presented in Appendix B. To summarize, this thesis reports an original investigation, since, to the best of our knowledge, there is no report yet about the study of the effect of SLG functionalized MEA on the neuronal network activity throughout the complete network maturation. Furthermore, proliferation curves of different cell lines on SLG versus control substrates have been presented; in addition to physicochemical characterization of ablated and functionalized SLG substrates as means of possible explanation of a certain cellular behavior on graphene.

L'extraction de sous-structures significatives a toujours été un élément clé de l’étude des graphes. Dans le cadre de l'apprentissage automatique, supervisé ou non, ainsi que dans l'analyse théorique des graphes, trouver des décompositions spécifiques et des sous-graphes denses est primordial dans de nombreuses applications comme entre autres la biologie ou les réseaux sociaux.Dans cette thèse, nous cherchons à étudier la dégénérescence de graphe, en partant d'un point de vue théorique, et en nous appuyant sur nos résultats pour trouver les décompositions les plus adaptées aux tâches à accomplir. C'est pourquoi, dans la première partie de la thèse, nous travaillons sur des résultats structurels des graphes à arête-admissibilité bornée, prouvant que de tels graphes peuvent être reconstruits en agrégeant des graphes à degré d’arête quasi-borné. Nous fournissons également des garanties de complexité de calcul pour les différentes décompositions de la dégénérescence, c'est-à-dire si elles sont NP-complètes ou polynomiales, selon la longueur des chemins sur lesquels la dégénérescence donnée est définie.Dans la deuxième partie, nous unifions les cadres de dégénérescence et d'admissibilité en fonction du degré et de la connectivité. Dans ces cadres, nous choisissons les plus expressifs, d'une part, et les plus efficaces en termes de calcul d'autre part, à savoir la dégénérescence 1-arête-connectivité pour expérimenter des tâches de dégénérescence standard, telle que la recherche d’influenceurs.Suite aux résultats précédents qui se sont avérés peu performants, nous revenons à l'utilisation du k-core mais en l’intégrant dans un cadre supervisé, i.e. les noyaux de graphes. Ainsi, en fournissant un cadre général appelé core-kernel, nous utilisons la décomposition k-core comme étape de prétraitement pour le noyau et appliquons ce dernier sur chaque sous-graphe obtenu par la décomposition pour comparaison. Nous sommes en mesure d'obtenir des performances à l’état de l’art sur la classification des graphes au prix d’une légère augmentation du coût de calcul.Enfin, nous concevons un nouveau cadre de dégénérescence de degré s’appliquant simultanément pour les hypergraphes et les graphes biparties, dans la mesure où ces derniers sont les graphes d’incidence des hypergraphes. Cette décomposition est ensuite appliquée directement à des architectures de réseaux de neurones pré-entrainés étant donné qu'elles induisent des graphes biparties et utilisent le core d'appartenance des neurones pour réinitialiser les poids du réseaux. Cette méthode est non seulement plus performant que les techniques d'initialisation de l’état de l’art, mais il est également applicable à toute paire de couches de convolution et linéaires, et donc adaptable à tout type d'architecture
Extracting Meaningful substructures from graphs has always been a key part in graph studies. In machine learning frameworks, supervised or unsupervised, as well as in theoretical graph analysis, finding dense subgraphs and specific decompositions is primordial in many social and biological applications among many others.In this thesis we aim at studying graph degeneracy, starting from a theoretical point of view, and building upon our results to find the most suited decompositions for the tasks at hand.Hence the first part of the thesis we work on structural results in graphs with bounded edge admissibility, proving that such graphs can be reconstructed by aggregating graphs with almost-bounded-edge-degree. We also provide computational complexity guarantees for the different degeneracy decompositions, i.e. if they are NP-complete or polynomial, depending on the length of the paths on which the given degeneracy is defined.In the second part we unify the degeneracy and admissibility frameworks based on degree and connectivity. Within those frameworks we pick the most expressive, on the one hand, and computationally efficient on the other hand, namely the 1-edge-connectivity degeneracy, to experiment on standard degeneracy tasks, such as finding influential spreaders.Following the previous results that proved to perform poorly we go back to using the k-core but plugging it in a supervised framework, i.e. graph kernels. Thus providing a general framework named core-kernel, we use the k-core decomposition as a preprocessing step for the kernel and apply the latter on every subgraph obtained by the decomposition for comparison. We are able to achieve state-of-the-art performance on graph classification for a small computational cost trade-off.Finally we design a novel degree degeneracy framework for hypergraphs and simultaneously on bipartite graphs as they are hypergraphs incidence graph. This decomposition is then applied directly to pretrained neural network architectures as they induce bipartite graphs and use the coreness of the neurons to re-initialize the neural network weights. This framework not only outperforms state-of-the-art initialization techniques but is also applicable to any pair of layers convolutional and linear thus being applicable however needed to any type of architecture

Nous sommes entourés par une multitude de réseaux d'interactions, issus de contextes très différents. Ces réseaux peuvent être modélisés par des graphes, appelés graphes de terrain. Ils possèdent une structure en communautés, c'est-à-dire en groupes de nœuds très liés entre eux, et peu liés avec les autres. Un phénomène que l'on étudie sur les graphes dans de nombreux contextes est la diffusion. La propagation d'une maladie en est un exemple. Ces phénomènes dépendent d'un paramètre important, mais souvent peu étudié : l'échelle de temps selon laquelle on les observe. Selon l'échelle choisie, la dynamique du graphe peut varier de manière très importante.Dans cette thèse, nous proposons d'étudier des processus dynamiques en utilisant une échelle de temps adaptée. Nous considérons une notion de temps relatif, que nous appelons le temps intrinsèque, par opposition au temps "classique", que nous appelons temps extrinsèque. Nous étudions en premier lieu des phénomènes de diffusion selon une échelle de temps intrinsèque, et nous comparons les résultats obtenus avec une échelle extrinsèque. Ceci nous permet de mettre en évidence le fait qu'un même phénomène observé dans deux échelles de temps différentes puisse présenter un comportement très différent. Nous analysons ensuite la pertinence de l'utilisation du temps intrinsèque pour la détection de communautés dynamiques. Les communautés obtenues selon les échelles de temps extrinsèques et intrinsèques nous montrent qu'une échelle intrinsèque permet la détection de communautés beaucoup plus significatives et détaillées que l'échelle extrinsèque
We are surrounded by a multitude of interaction networks from different contexts. These networks can be modeled as graphs, called complex networks. They have a community structure, i.e. groups of nodes closely related to each other and less connected with the rest of the graph. An other phenomenon studied in complex networks in many contexts is diffusion. The spread of a disease is an example of diffusion. These phenomena are dynamic and depend on an important parameter, which is often little studied: the time scale in which they are observed. According to the chosen scale, the graph dynamics can vary significantly. In this thesis, we propose to study dynamic processes using a suitable time scale. We consider a notion of relative time which we call intrinsic time, opposed to "traditional" time, which we call extrinsic time. We first study diffusion phenomena using intrinsic time, and we compare our results with an extrinsic time scale. This allows us to highlight the fact that the same phenomenon observed at two different time scales can have a very different behavior. We then analyze the relevance of the use of intrinsic time scale for detecting dynamic communities. Comparing communities obtained according extrinsic and intrinsic scales shows that the intrinsic time scale allows a more significant detection than extrinsic time scale

Cette these traite de la resolution de problemes d'optimisation tres complexes (np. Complets) par le biais de l'etude des systemes complexes artificiels qui imitent les systemes physiques et qui sont simules avec des reseaux neuromimetiques. La solution optimale est identifiee a un etat fondamental d'un systeme physique. Plusieurs techniques neuronales sont presentees pour approcher la solution optimale. Elles utilisent soit l'analyse canonique, soit l'analyse microcanonique, definies en mecanique statistique. Parmi ces methodes, nous presentons l'utilisation des reseaux de hopfield analogiques, le recuit simule, l'approximation du champ moyen, le recuit en champ moyen et le recuit microcanonique. Elles sont particulierement bien adaptees aux problemes de graphes qui traitent de coupure et de connectivite, de morphisme et d'extraction de sous-graphes possedant des proprietes extremales. Dans ce cadre, les problemes de k-partitionnement de graphe, de mise en correspondance de graphes, et d'extraction de la plus grande clique sont traites. Dans la derniere partie, nous abordons le probleme de groupement perceptif en vision par ordinateur. On montre que ce probleme se ramene, par le biais de la theorie de la gestalt definie en psychologie experimentale, a un probleme d'optimisation combinatoire soluble par reseaux de neurones

AbstractNeurodegenerative diseases are incurable diseases that get worse as time passes. These diseases are very heterogeneous in nature but have common characteristics like abnormal deposition of protein, glycation, inflammation in particular areas of the brain, and progressive neuronal loss due to oxidative stress. Among these, oxidative stress alone causes a high level of degeneration of neurons. To reduce oxidative stress, natural antioxidants are used but they have some drawbacks like instability, high cost and low reusability. To overcome this, nanozymes are introduced and we have emphasized on major nanozymes whose antioxidant capability has been proven which are gold nanozymes, fullerene, nanoceria, and quantum dots. Gold nanoparticles and their conjugates with other molecules can mimic the enzymatic activity of superoxide dismutase and catalase which decrease the amount of hydrogen peroxide and superoxide radicals in cells. Gold Nanozyme treatment reduces the oxidative stress, nitrite, and sulfhydryl levels in the brain and also rectifies the superoxide dismutase, glutathione, and catalase activity levels. Fullerenols has shown superoxide dismutase activity which was 268 times more effective than mannitol and 37 times more effective than Vitamin E for lipid radicals. Nanoceria has the ability to mimic Superoxide Dismutase as well as catalase activity, can also detoxify peroxynitrite. Quantum dots (QDs) like Graphene Oxide QDs can scavenge the reactive oxygen species and also show indirect activity which alleviates the pathogenesis of the disease. Thus, a nanozyme can be used as an efficient nanomedicine if it is tailored to possess high catalytic activity while eliminating all complications.

TiO2 is one of the most common materials for photocatalytic applications due to its stability, affordability, and photoactive efficiency. However, it has some drawbacks, such as limited solar radiation response and quick recombination of excitons. Using graphene could be one of the methods to enhance the photocatalytic properties of TiO2. This study intends to optimize the photocatalytic performance of TiO2/Graphene (TiO2/G) nanocomposite by using neuro-regression analysis. In the analysis, the effect of some hydrothermal synthesis parameters, namely, amount of graphene oxide, ethanol/water ratio, and hydrothermal reaction time on the photocatalytic activity of TiO2/G nanocomposite, have been investigated. The parameters were determined from a literature study focused on overcoming the drawbacks of TiO2 by combining it with graphene oxide. Nelder-Mead, Simulated Annealing, Differential Evolution, and Random Search algorithms are used to obtain the optimum synthesis parameters for maximum photocatalytic activity in the optimization process. The results are indicated that all algorithms give the realizable value for design variables and photodegradation rate.