Tesis sobre el tema "Neural activity recording"

The study of neuronal and neurodegenerative diseases requires the development of new tools and technologies to create functional neuroelectronics allowing both stimulation and recording of cellular electrical activity. In the last decade organic electronics is digging its way in the field of bioelectronics and researchers started to develop neural interfaces based on organic semiconductors. The interest in such technologies arise from the intrinsic properties of organic materials such as low cost, transparency, softness and flexibility, as well the biocompatibility and the suitability in realizing all organic printed systems. In particular, organic field-effect transistor (OFET) -based biosensors integrate the sensing and signal amplification in a single device, paving the way to new implantable neural interfaces for in vivo applications. To master the sensing and amplification properties of the OFET-based sensors, it is mandatory to gain an intimate knowledge of the single transistors (without any analytes or cells) that cannot be limited to basic characterizations or to general models. Moreover, organic transistors are characterized by different working principles and properties as respect to their inorganic counterpart. We performed pulsed and transient characterization on different OFETs (both p-type and n-type) showing that, even though the transistors can switch on and off very fast, the accumulation and/or the depletion of the conductive channel continues for times as long as ten seconds. Such phenomenon must be carefully considered in the realization of a biosensor and in its applications, since the DC operative point of the device can drift during the recording of the cellular signals, thus altering the collected data. We further investigate such phenomenon by performing characterizations at different temperatures and by applying the deep level transient spectroscopy technique. We showed that the slow channel accumulation (and depletion) is due to the semiconductor density-of-states that must be occupied in order to bring the Fermi energy level close to the conduction band. This is a phenomenon that can takes several seconds and we described it by introducing a time-depend mobility. We also proposed a technique to estimate the behavior, in time, of the position of the Fermi energy level as respect to the conduction band. To understand the electrochemical transduction processes between living cell and organic biosensor, we realized two-electrodes structure (STACKs) where a drop of saline solution is put directly in contact with the organic semiconductor. On these devices, we performed electrochemical impedance spectroscopy at different DC polarizations and we developed an equivalent circuit model for the metal-organic semiconductor-solution structures that are typically used as transducers in biosensor devices. Our approach was extending the standard range of the bias voltages applied for devices that operate in water. This particular characterization protocol allowed to distinguish and investigate the different mechanisms that occur at the different layers and interfaces: adsorption of ions in the semiconductor; accumulation and charge exchange of carriers at the semiconductor/electrolyte interface; percolation of the ionic species through the organic semiconductor; ion diffusion across the electrolyte; ion adsorption and charge exchange at the platinum interface. We highlighted the presence of ion percolation through the organic semiconductor layer, which is described in the equivalent circuit model by means of a de Levie impedance. The presence of percolation has been demonstrated by environmental scanning electron microscopy and profilometry analysis. Although percolation is much more evident at high negative bias values, it is still present even at low bias conditions. In addition, we analyze two case studies of devices featuring NaCl (concentration of 0.1M) and MilliQ water as solution, showing that both cases can be considered as a particular case of the general model presented in this manuscript. The very good agreement between the model and the experimental data makes the model a valid tool for studying the transducing mechanisms between organic films and the physiological environment. Hence this model could be a useful tool not only for the characterization and failure analysis of electronic devices, such as water-gated transistors, electrophysiological interfaces, fuel cells, and others electrochemical systems, but also this model might be used in other applications, in which a solution is in intimate contact with another material to determine and quantify, if undesired mechanisms such as percolation and/or redox corrosive processes occur. Lastly, the knowledge gain on OFETs and STACKs were put together to realize electrolyte-gated field effect transistors (EGOFETs). We then developed a model to describes EGOFETs as neural interfaces. We showed that our model can be successfully applied to understand the behaviour of a more general class of devices, including both organic and inorganic transistors. We introduced the reference-less (RL-) EGOFET and we showed that it might be successfully used as a low cost and flexible neural interface for extracellular recording in vivo without the need of a reference electrode, making the implant less invasive and easier to use. The working principle underlying RL-EGOFETs involves self-polarization and back-gate stimulation, which we show experimentally to be feasible by means of a custom low-voltage high-speed acquisition board that was designed to emulate a real-time neuron response. Our results open the door to using and optimizing EGOFETs and RL-EGOFETs for neural interfaces.
Lo studio delle malattie neuronali e neuro-degenerative richiede lo sviluppo di nuovi strumenti e tecnologie per creare dispositivi neuro-elettronici funzionali che consentano sia la stimolazione che la registrazione dell'attività elettrica cellulare. Nell'ultimo decennio l'elettronica organica sta emergendo nel campo della bioelettronica e diversi gruppi di ricerca hanno iniziato a sviluppare interfacce neurali basate su semiconduttori organici. L'interesse per tali tecnologie deriva dalle proprietà intrinseche dei materiali organici quali basso costo, trasparenza, morbidezza e flessibilità, nonché la biocompatibilità e l'idoneità nella realizzazione di sistemi stampati completamente organici. In particolare, i biosensori basati sulla tecnologia a transistor ad effetto campo organico (OFET) integrano il sensing e l'amplificazione del segnale in un singolo dispositivo, aprendo la strada a nuove interfacce neurali impiantabili per applicazioni in vivo. Per padroneggiare le proprietà di rilevamento e amplificazione dei sensori basati su OFET, è obbligatorio acquisire una conoscenza approfondita dei singoli transistor (senza la presenza di analiti e/o cellule) che vadano oltre le caratterizzazioni di base o modelli generali. Inoltre, i transistor organici sono caratterizzati da diversi principi di funzionamento e diverse proprietà rispetto alla loro controparte inorganica. In questo lavoro abbiamo svolto caratterizzazioni impulsate e transienti su diversi OFET (sia di tipo p che di tipo n) mostrando che, anche se i transistor possono accendersi e spegnersi molto velocemente, l'accumulo e/o lo svuotamento del canale conduttivo continua per tempi che possono superare le decine di secondi. Tale fenomeno deve essere attentamente considerato nella realizzazione di un biosensore e nelle sue applicazioni, poiché il punto operativo DC del dispositivo può andare alla deriva durante la registrazione dei segnali cellulari, alterando così i dati raccolti. Questo fenomeno viene ulteriormente approfondito caratterizzano i dispositivi a diverse temperature e per mezzo della tecnica DLTS. Abbiamo dimostrato che il lento accumulo (e svuotamento) del canale è dovuto alla densità di stati del semiconduttore organico che devono poter essere occupati per portare il livello energetico di Fermi vicino alla banda di conduzione. Questo è un fenomeno che può richiedere diversi secondi che possiamo descrivere introducendo una mobilità dipendente dal tempo. Per comprendere i processi di trasduzione elettrochimica tra cellule viventi ed il biosensore organico, abbiamo realizzato una struttura a due elettrodi (STACK) in cui una goccia di soluzione salina viene messa direttamente a contatto con il semiconduttore organico. Su questi dispositivi, abbiamo eseguito la spettroscopia di impedenza elettrochimica a diverse polarizzazioni DC e abbiamo sviluppato un modello circuitale equivalente per le strutture metallo/semiconduttore organico/soluzione che vengono tipicamente utilizzate per la realizzazione di bio-trasduttori. Il nostro approccio prevede di estendere il range standard delle tensioni operative per questo genere di dispositivi. Ciò ha permesso di investigare e distinguere i diversi fenomeni che si verificano nei diversi strati e interfacce: adsorbimento di ioni nel semiconduttore; accumulo e scambio di cariche di portanti all'interfaccia semiconduttore/elettrolita; percolazione delle specie ioniche attraverso il semiconduttore organico; diffusione di ioni attraverso l'elettrolita; adsorbimento di ioni e scambio di carica all'interfaccia col metallo. Abbiamo evidenziato la presenza di percolazione ionica attraverso lo strato di semiconduttore organico, che è descritto nel modello circuitale per mezzo di un'impedenza di de Levie. La presenza di percolazione è stata dimostrata mediante microscopia elettronica a scansione ambientale e analisi profilometrica. Sebbene la percolazione sia molto più evidente a valori di bias negativi elevati, risulta presente anche a basse condizioni di bias. L'ottimo accordo tra il modello e i dati sperimentali rende il modello un valido strumento per studiare i meccanismi di trasduzione tra film organici e l'ambiente fisiologico. Quindi questo modello può essere uno strumento utile non solo per la caratterizzazione e l'analisi dei guasti dei dispositivi elettronici, come water-gated transistor, interfacce elettrofisiologiche, celle a combustibile e altri sistemi elettrochimici, ma anche nel caso in cui una soluzione è in intimo contatto con un altro materiale per determinare e/o quantificare se si verificano meccanismi indesiderati come percolazione e/o processi corrosivi. Infine, il bagaglio di conoscenze ottenuto studiando i dispositivi OFET e STACK è stato messo utillizato per realizzare dispositivi EGOFET. Abbiamo quindi sviluppato un modello per descrivere gli EGOFET come interfacce neurali. Abbiamo dimostrato che il nostro modello può essere applicato con successo per comprendere il comportamento di una classe più generale di dispositivi, compresi i transistor sia organici che inorganici. Abbiamo introdotto l'RL-EGOFET (reference-less EGOFET) e abbiamo dimostrato che questa struttura può essere utilizzata con successo come interfaccia neurale flessibile per il recording extracellulare in vivo senza la necessità di un elettrodo di riferimento, rendendo l'impianto meno invasivo e più facile da usare. I nostri risultati aprono la strada all'utilizzo e all'ottimizzazione di EGOFET e RL-EGOFET come interfacce neurali.

Throughout most of the 20th century the brain has been studied as a reflexive system with ever improving recording methods being applied within a variety of sensory and behavioural paradigms. Yet the brains of most animals (and all mammals) are spontaneously active with incoming sensory stimuli modulating rather than driving neural activity. The aim of this thesis is to characterize spontaneous neural activity across multiple temporal and spatial scales relying on biophysical simulations, experiments and analysis of recordings from the visual cortex of cats and dorsal cortex and thalamus of mouse. Biophysically detailed simulations yielded novel datasets for testing spike sorting algorithms which are critical for isolating single neuron activity. Sorting algorithms tested provided low error rates with operator skill being as important as sorting suite. Simulated datasets have similar characteristics to in vivo acquired data and ongoing larger-scope efforts are proposed for developing the next generation of spike sorting algorithms and extracellular probes. Single neuron spontaneous activity was correlated to dorsal cortex neural activity in mice. Spike-triggered-maps revealed that spontaneously firing cortical neurons were co-activated with homotopic and mono-synaptically connected cortical areas, whereas thalamic neurons co-activated with more diversely connected areas. Both bursting and tonic firing modes yielded similar maps and the time courses of spike-triggered-maps revealed distinct patterns suggesting such dynamics may constitute intrinsic single neuron properties. The mapping technique extends previous work to further link spontaneous neural activity across temporal and spatial scales and suggests additional avenues of investigation. Synchronized state cat visual and mouse sensory cortex electrophysiological recordings revealed that spontaneously occurring activity UP-state transitions fall into stereotyped classes of events that can be grouped. Single visual cortex neurons active during UP-state transitions fire in a partially preserved order extending previous findings on high firing rate neurons in rat somatosensory and auditory cortex. The firing order for many neurons changes over periods longer than 30-minutes suggesting a complex non-stationary temporal neural code may underlie spontaneous and stimulus evoked neural activity. This thesis shows that ongoing spontaneous brain activity contains substantial structure that can be used to further our understanding of brain function.
Medicine, Faculty of
Graduate

To discover general principles of biological sensorimotor control, insects have become remarkably successful model systems. In contrast to highly complex mammals, the functional organization of the insect nervous system in combination with a well-defined behavioural repertoire turned out to provide ideal conditions for quantitative studies into the neural control of behaviour. In addition, the search for biologically inspired control algorithms has further accelerated research into the neuronal mechanisms underlying flight and gaze stabilization, especially in blowflies. However, recording the neuronal activity in freely behaving insects, in particular in comparatively small insects such as blowflies, still imposes a major technical challenge. To date, electrophysiological recordings in unrestrained flies have never been achieved. This thesis describes the design and testing of a micro recording probe to be used for monitoring extracellular electrical activity in the nervous system of freely moving blowflies. In principle, this probe could also be used to study the neuronal control of behaviour in any other animal species the size of which is bigger than that of a blowfly. The nature of neuronal signals and the objective to record neuronal activity from behaving blowflies puts massive constraints on the specifications of the probe. I designed a differential amplifier with high gain, high linearity, low noise, and low power consumption. To fit the probe in the blowfly‟s head capsule and in direct contact with the animal‟s brain, the amplifier is on an unpackaged die. The neuronal signals are in the order of a few 100s of μV in amplitude. To be able to digitize such small signals >1000 times amplification is desirable. The small signal amplitudes also necessitate minimization of circuit noise. Linearity is necessary to prevent distortion of signal shape. Since connecting wires would impede movement of the animal, the probe would need to be powered by batteries. Therefore, low power is needed for two reasons: (i) to increase battery life, and therefore recording time, and (ii) because heat caused by power expenditure may damage the blowfly‟s brain or change its behaviour. To reduce power consumption I used CMOS transistors biased in the subthreshold region and a 2.2 V low power supply. The amplifier was characterized after fabrication by means of measuring its frequency response, linearity, and noise. I also recorded signals from a blowfly's brain and compared the performance of my recording probe with the performance of a high specification commercial amplifier in the time and frequency domains.

The study of the central nervous system represents a great challenge in the field of neuroscience. For years, various techniques have been developed to study neuronal cells in-vitro as it is difficult to conduct in-vivo experiments due to ethical problems deriving from its anatomical location. Consequently, both in-vivo and in-vitro animal models have been used extensively to gain new insights into basic functioning principles of neuronal tissue and therapeutic approaches for brain diseases. Over time, we have seen that there is a poor correlation between the clinical diagnosis and the underlying pathological mechanisms. In fact, some symptoms that may occur in the patient are not replicated in the animal, making many promising approaches in animal studies not translatable in the clinic. With the advent of human-induced pluripotent stem cells (h-iPSC) several protocols for the generation of human-neuronal cells are becoming available for all laboratories. The importance of this technique lies in the opportunity to develop a human model derived directly from the patient: the patient's in-vitro cells will exhibit the same genetic and epigenetic modifications as the in-vivo cells. This has raised hopes for the generation of engineered brain models that can be coupled to sensors / actuators in order to better investigate their functional properties in-vitro (i.e. brain-on-a-chip). A reliable method for evaluating the functionality of neuronal cultures is the study of the spontaneous electrophysiological activity using microelectrode arrays (MEA). There are numerous studies in the literature that used h-iPSC on MEAs, showing the characterization of neuronal patterns of patient-derived cultures, demonstrating how this platform is valid for disease phenotyping, drug discovery and translational medicine. Although these models helped to shed light on fundamental biological mechanisms, the majority is based on two-dimensional neuronal cultures, which lack some key features to mimic in-vivo behavior. Three-dimensional h-iPSC-derived models possess a microenvironment, tissue architecture and potential to model network activity with greater complexity than two-dimensional models. Depending on the purpose of the study, we can choose different approaches to recreate 3D in-vitro brain, from those that aim to reproduce the trajectories of neurodevelopment (i.e. brain-organoids) to the use of synthetic materials that reproduce the functionalities of the extracellular matrix (ECM) (i.e. scaffold-based) (Chiaradia and Lancaster, 2020, Tang et al., 2006). Although h-iPSC-derived brain models summarize many aspects of network function in the human brain, they are subject to variability and still do not perfectly mimic behavior in-vivo. Therefore, to reach the full potential of this model we need improvements in differentiation methods and bioengineering, making these models engineered and reproducible. The aim of this PhD thesis was to implement different 3D neuronal culture generation methodologies that can be integrated on MEA devices to offer robust engineered platforms for functional studies.

Disertacija se se bavi analizom mogućnosti brze, efikasnei pouzdane klasterizacije masivnog skupa neuralnihsnimaka na osnovu probabilističkih parametara procenjenihiz obrazaca generisanja akcionih potencijala, tzv."spajkova", na izlazu pojedinih neurona. Neuralnaaktivnost se grubo može podeliti na periode intezivne,umerene i niske aktivnosti. Shodno tome, predložena jegruba dekompozicija neuralne aktivnosti na tri moda kojaodgovaraju navedenim obrascima neuralne aktivnosti, naosnovu dobro poznatog Gilbert-Eliot modela. Modovi sudodatno raščlanjeni na sopstvena stanja na osnovu osobina sukcesivnih spajkova, omogućujući finiji, kompozitniopis neuralne aktivnosti. Za svaki neuron empirijski seprocenjuju probabilistički parametri grube dekompozicije- na osnovu Gilbert-Eliotovog modela i finije dekompozicije- na osnovu sopstvenih stanja modova, obezbeđujućiželjeni skup deskriptora. Dobijeni deskriptorikoriste se kao obeležja nekoliko algoritama klasterizacijenad simuliranim i eksperimentalnim podacima. Za generisanjesimuliranih podataka primenjen je jednostavanmodel za generisanje akcionih potencijala različitihoscilatornih ponašanja pobuđujućih i blokirajućih kortikalnihneurona. Validacija primene probabilističkih parametaraza klasterizaciju rada neurona izvršena je naosnovu estimacije parametera nad generisanim neuralnimodzivima. Eksperimentalni podaci su dobijenisnimanjem kortikografskih signala iz dorzalnog anteriornogcingularanog korteksa i lateralnog prefrontalnogkorteksa korteksa budnih rezus majmuna. U okviru predloženogprotokola evaluacije različitih pristupaklasterizacije testirano je nekoliko metoda. Klasterizacijazasnovana na akumulaciji dokaza iz ansambla particijadobijenih k-means klasterovanjem dala je najstabilnijegrupisanje neuralnih jedinica uz brzu i efikasnu implementaciju.Predložena empirijska karakterizacija može daposluži za identifikaciju korelacije sa spoljašnjim stimulusima,akcijama i ponašanjem životinja u okvirueksperimentalne procedure. Prednosti ovog postupka zaopis neuralne aktivnosti su brza estimacija i mali skupdeskriptora. Računarska efikasnost omogućuje primenunad obimnim, paralelno snimanim neuralnim podacima utoku snimanja ili u periodima od interesa za identifikacijuaktiviranih i povezanih zona pri određenim aktivnostima.
The advances in extracellular neural recording techniquesresult in big data volumes that necessitate fast,reliable, and automatic identification of statisticallysimilar units. This study proposes a single frameworkyielding a compact set of probabilistic descriptors thatcharacterise the firing patterns of a single unit. Probabilisticfeatures are estimated from an inter-spikeintervaltime series, without assumptions about the firing distribution or the stationarity. The first level of proposedfiring patterns decomposition divides the inter-spikeintervals into bursting, moderate and idle firing modes,yielding a coarse feature set. The second level identifiesthe successive bursting spikes, or the spiking acceleration/deceleration in the moderate firing mode, yieldinga refined feature set. The features are estimated fromsimulated data and from experimental recordings fromthe lateral prefrontal cortex in awake, behaving rhesusmonkeys. An effcient and stable partitioning of neuralunits is provided by the ensemble evidence accumulationclustering. The possibility of selecting the number ofclusters and choosing among coarse and refined featuresets provides an opportunity to explore and comparedifferent data partitions. The estimation of features, ifapplied to a single unit, can serve as a tool for the firinganalysis, observing either overall spiking activity or theperiods of interest in trial-to-trial recordings. If applied tomassively parallel recordings, it additionally serves as aninput to the clustering procedure, with the potential tocompare the functional properties of various brainstructures and to link the types of neural cells to theparticular behavioural states.

La mémoire épisodique, notre capacité de se rappeler des épisodes particuliers de notre vie, a été initialement définie chez l'homme en termes de l'information qu'elle contient, quel événement a eu lieu, où et dans quel contexte /quand s'est-il produit? La démonstration de l'existence de cette forme de mémoire chez l'animal a été réalisée chez le geais buissonnier. En effet, cet oiseau cacheur est capable de former une représentation mentale complexe du type de nourriture qu'il a caché, où et quand. Cette forme de mémoire qualifiée d' « episodic-like » a depuis une dizaine d'année été établie chez le rongeur. Au cours de ma thèse, j'ai suivi deux objectifs: valider un nouveau paradigme de mémoire épisodique chez le rat et l'utiliser pour étudier les circuits neuronaux qui sous-tendent cette forme particulière de mémoire. La première partie du manuscrit présente le développement et la validation d'un protocole original destiné à l'étude de la mémoire épisodique chez le rat. Lors de la conception de cette tâche, nous avons essayé de réduire au minimum la procédure d'entrainement des animaux afin de préserver l'essence même de la mémoire épisodique qui est la mémoire d'épisodes uniques. Pendant la tâche les rats ont été exposés à deux épisodes différents, au cours desquels des combinaisons uniques odeurs-place (information « quoi et où ») ont été présentées dans des contextes différents enrichis et multi-sensoriels (information « dans quel contexte »). Nous avons démontré que certains rats («ww») étaient capables de former des associations de mémoire (« episodic-like ») qui leur permettent de se souvenir de l'intégralité de l'épisode présenté après des délais courts (24h) et longs (24 jours) et dans différentes situations de rappel, tandis que d'autres («rest») ne se souvenaient que partiellement des informations présentes lors de l'épisode. Une approche pharmacologique réalisée lors de la validation de la tâche nous a permis de confirmer que l'hippocampe dorsal était nécessaire au rappel épisodique complet. Dans une version étendue du protocole dans laquelle des rats ont été exposés à deux épisodes supplémentaires, nous avons trouvé que l'expérience des épisodes préalablement acquis par les rats facilite l'encodage de nouveaux épisodes et que la mémoire de ces épisodes est plus stable. La deuxième partie de la thèse présente une première approche de l'étude des circuits neuronaux sous tendant la formation et la récupération de la mémoire épisodique. L'approche méthodologique utilisée est l'enregistrement multi-site de potentiels de champs locaux chez l'animal vigile. Le réseau de structures enregistrées inclut les aires sensorielles olfactives, des régions du cortex préfrontal médian et latéral ainsi que les régions dorsales et ventrales de l'hippocampe. Après avoir extrait des signaux le contenu fréquentiel dans deux bandes de fréquences (béta et théta), nous avons analysé les variations de puissance de l'activité oscillatoire dans ces bandes en utilisant des analyses en transformées de Hilbert et ondelette de Morlet. La période d'analyse est centrée sur l'échantillonnage de l'odeur, dernière information traitée avant que l'animal produise sa réponse comportementale. Les changements de puissance dans les deux bandes en réponse à l'odeur ont été comparés dans les différentes situations expérimentales pour les rats «ww» et les rats «rest». Les résultats obtenus montrent que le réseau de structures activées dans la bande béta en réponse à l'odeur est différent en fonction du profil de rappel des animaux (les rats du profil «ww» versus les rats «rest») à la fois en encodage et en situation de rappel. L'activité dans le réseau est également différente en fonction du type de réponse (hit versus rejet correct)
Episodic memory, our capacity to recollect particular life episodes, has been initially defined in terms of the information it contains, what kind of event, where and in which context/when did it take place. Pioneering studies on food-caching birds have demonstrated that animals are also able to form such complex memories, referred to as episodic-like memories in animals, however its modelling in rodents has proved challenging. The aim of this thesis was twofold: further development and validation in rats of a new episodic-like memory paradigm and study of neural circuits involved in formation and retrieval of this particular memory. The first part of the thesis presents the original behavioral paradigm developed in our group. In our task we tried to minimize training procedure in order to preserve the nature of episodic memory which is the memory for unique life episodes. Hereby rats were exposed to two different episodes, during which unique odor-place combinations (“what and where” information) were presented in different enriched multisensory contexts (“in which context” information). We found that some rats (“ww” group) were indeed able to form episodic-like memory associations which can be recalled after short (24 h) and long delays (24 days) in different experimental situations, while other animals (“rest” group) remembered only parts of the information contained in the initial episodes. Using pharmacological inactivation of dorsal hippocampus we have demonstrated that hippocampus is required specifically for retrieval of associated episodic-like memory information, but not for retrieval of single elements of the presented episodes in our task. In an extended version of the protocol in which rats were exposed to two additional episodes we found that previously acquired experience of the rats facilitates the encoding of new episodes and that the memory of these new episodes is more stable. The second part of the manuscript presents the first approach to study neural circuits involved in episodic-like memory encoding and retrieval in our task. Electrophysiological methodology was based on local field potential recordings obtained in parallel in several brain regions in behaving animals. The network of structures investigated included olfactory neocortical brain areas, brain regions in lateral and medial prefrontal cortex and the dorsal and ventral part of the hippocampus. The analysis was based on the estimation of magnitude of the oscillatory activity (described as power changes) in theta and beta frequency bands using Hilbert and Morlet wavelet transform for the analyses. The power analysis evolved around odor sampling event which constituted the last piece of information required for recollection of the whole episodic-like memory association. The odor-induced changes in power were compared between “ww” and “rest” animals in different experimental situations. We found that the network of activated brain regions in beta frequency band differed as a function of the memory profile of the rats (complete episodic-like memory recollection versus remembering partial information of the episodes) during both memory encoding as well as retrieval. We have also demonstrated that this active network changes when memory becomes consolidated (recent versus remote memory). Additionally we have shown that the activity in the network depends on the type of the response (hit versus correct rejection) given by the rat during memory encoding and retrieval. The network of brain regions that showed changes in theta power during memory formation and retrieval differed strongly from beta band network. In contrast to beta, the memory profile effect was much less prominent for theta band. However similarly to beta, there were also significant changes in network depending on the encoding session and the age of memory at test

Neuronal networks are at the base of information processing in the brain. They are series of interconnected neurons whose activation defines a recognizable linear pathway. The main goal of studying neural ensembles is to characterize the relationship between the stimulus and the individual or general neuronal responses and the relation amongst the electrical activities of neurons within the network, also understanding how topology and connectivity relates to their function. Many techniques have been developed to study these complex systems: single-cell approaches aim to investigate single neurons and their connections with a limited number of other nerve cells; on the opposite side, low-resolution large-scale approaches, such as functional MRI (Magnetic Resonance Imaging) or electroencephalography (EEG), record signal changes in the brain that are generated by large populations of cells. More recently, multisite recording techniques have been developed to overcome the limitations of previous approaches, allowing to record simultaneously from huge neuronal ensembles with high spatial resolution and in different brain regions, i.e. by using implantable semiconductor chips. Local Field Potentials (LFPs), the part of electrophysiological signals that has frequencies below 500 Hz, capture key integrative synaptic processes that cannot be measured by analyzing the spiking activity of few neurons alone. Several studies have used LFPs to investigate cortical network mechanisms involved in sensory processing, motor planning and higher cognitive processes, like memory and perception. LFPs are also promising signals for steering neuroprosthetic devices and for monitoring neural activity even in human beings, since they are more easily and stably recorded in chronic settings than neuronal spikes. In this work, LFP profiles recorded in the rat barrel cortex through high-resolution CMOS-based needle chips are presented and compared to those obtained by means of conventional Ag/AgCl electrodes inserted into glass micropipettes, which are widely used tools in electrophysiology. The rat barrel cortex is a well-known example of topographic mapping where each of the whiskers on the snout of the animal is mapped onto a specific cortical area, called a barrel. The barrel cortex contains the somatosensory representation of the whiskers and forms an early stage of cortical processing for tactile information, along with the trigeminal ganglion and the thalamus. It is an area of great importance for understanding how the cerebral cortex works, since the cortical columns that form the basic building blocks of the neocortex can be actually seen within the barrel. Moreover, the barrel cortex has served as a test-bed system for several new methodologies, partly because of its unique and instantly identifiable form, and partly because the species that have barrels, i.e. rodents, are the most commonly used laboratory mammal. The barrel cortex, the whiskers that activate it and the intervening neural pathways have been increasingly the subject of focus by a growing number of research groups for quite some time. Nowadays, studies (such this one) are directed not only at understanding the barrel cortex itself but also at investigating issues in related fields using the barrel cortex as a base model. In this study, LFP responses were evoked in the target barrel by repeatedly deflecting the corresponding whisker in a controlled fashion, by means of a specifically designed closed-loop piezoelectric bending system triggered by a custom LabView acquisition software. Evoked LFPs generated in the barrel cortex by many consecutive whiskers' stimulations show large variability in shapes and timings. Moreover, anesthetics can deeply affect the profile of evoked responses. This work presents preliminary results on the variability and the effect of commonly used anesthetics on these signals, by comparing the distributions of evoked responses recorded from rats anesthetized with tiletamine-xylazine, which mainly blocks the excitatory NMDA receptors, and urethane, which conversely affects both the excitatory and inhibitory system, in a complex and balanced way yet preserving the synaptic plasticity. Representative signal shape characteristics (e.g., latencies and amplitude of events) extracted from evoked responses acquired from different cortical layers are analyzed and discussed. Statistical distributions of these parameters are estimated for all the different depths, in order to assess the variability of LFPs generated by individual mechanical stimulations of single whiskers along the entire cortical column. Preliminary results showed a great variability in cortical responses, which varied both in latency and amplitude across layers. We found significant difference in the latency of the first principal peak of the responses: under tiletamine-xylazine anesthetic, the responses or events of the evoked LFPs occurred later than the ones recorded while urethane was administered. Furthermore, the distributions of this parameter in all cortical layers were narrower in case of urethane. This behavior should be attributed to the different effects of these two anesthetics on specific synaptic receptors and thus on the encoding and processing of the sensory input information along the cortical pathway. The role of the ongoing basal activity on the modulation of the evoked response was also investigated. To this aim, spontaneous activity was recorded in different cortical layers of the rat barrel cortex under the two types of anesthesia and analyzed in the statistical context of neuronal avalanches. A neuronal avalanche is a cascade of bursts of activity in neural networks, whose size distribution can be approximated by a power law. The event size distribution of neuronal avalanches in cortical networks has been reported to follow a power law of the type P(s)= s^-a, with exponent a close to 1.5, which represent a reflection of long-range spatial correlations in spontaneous neuronal activity. Since negative LFP peaks (nLFPs) originates from the sum of synchronized Action Potentials (AP) from neurons within the vicinity of the recording electrode, we wondered if it were possible to model single nLFPs recorded in the basal activity traces by means of only one electrode as the result of local neuronal avalanches, and thus we analyzed the size (i.e. the amplitude in uV) distribution of these peaks so as to identify a suitable power-law distribution that could describe also these single-electrode records. Finally, the results of the first ever measurements of evoked LFPs within an entire column of the barrel cortex obtained by means of the latest generation of CMOS-based implantable needles, having 256 recording sites arranged into two different array topologies (i.e. 16 x 16 or 4 x 64, pitches in the x- and y-direction of 15 um and 33 um respectively), are presented and discussed. A propagation dynamics of the LFP can be already recognized in these first cortical profiles. In the next future, the use of these semiconductor devices will help, among other things, to understand how degenerating syndromes like Parkinson or Alzheimer evolve, by coupling detected behaviors and symptoms of the disease to neuronal features. Implantable chips could then be used as 'electroceuticals', a newly coined term that describes one of the most promising branch of bioelectronic medicine: they could help in reverting the course of neurodegenerative diseases, by constituting the basis of neural prostheses that physically supports or even functionally trains impaired neuronal ensembles. High-resolution extraction and identification of neural signals will also help to develop complex brain-machine interfaces, which can allow intelligent prostheses to be finely controlled by their wearers and to provide sophisticated feedbacks to those who have lost part of their body or brain functions.
Le reti neuronali sono alla base della codifica dell'informazione cerebrale. L'obiettivo principale dello studio delle popolazioni neuronali è quello di caratterizzare la relazione tra uno stimolo e la risposta individuale o globale dei neuroni e di studiare il rapporto tra le varie attività elettriche dei neuroni appartenenti ad una particolare rete, comprendendo anche come la topologia e la connettività della rete neuronale influiscano sulla loro funzionalità. Fino ad oggi, molte tecniche sono state sviluppate per studiare questi sistemi complessi: studi a singola cellula mirano a studiare singoli neuroni e le loro connessioni con un numero limitato di altre cellule; sul lato opposto, approcci su larga scala e a bassa risoluzione, come la risonanza magnetica funzionale o l'elettroencefalogramma, registrano segnali elettrofisiologici generati nel cervello da vaste popolazioni di cellule. Più recentemente, sono state sviluppate tecniche di registrazione multisito che mirano ad abbattere le limitazioni dei precedenti approcci, rendendo possibile la misurazione ad alta risoluzione di segnali generati da grandi ensamble neuronali e da diverse regioni del cervello simultaneamente, ad esempio mediante l'uso di chip impiantabili a semiconduttore. I potenziali di campo locali (LFP) catturano processi sinaptici chiave che non possono essere estratti dall'attività di spiking di qualche neurone isolato. Numerosi studi hanno utilizzato gli LFP per studiare i meccanismi corticali coinvolti nei processi sensoriali, motori e cognitivi, come la memoria e la percezione. Gli LFP rappresentano anche dei segnali interessanti nell'ambito delle applicazioni neuroprotesiche e per monitorare l'attività cerebrale negli esseri umani, dal momento che possono essere registrati più stabilmente e facilmente in impianti cronici rispetto agli spike neuronali. In questo studio, sono riportati dei profili LFP registrati dalla barrel cortex di ratto tramite chip ad ago ad alta risoluzione basati su tecnologia CMOS e confrontati con quelli ottenuti tramite elettrodi convenzionali in Ag/AgCl inseriti in micropipette di vetro, strumenti comunemente usati in elettrofisiologia. La barrel cortex di ratto è un esempio ben noto di mapping topografico, nel quale ogni baffo sul muso dell'animale è mappato in una specifica area corticale, chiamata barrel. La barrel cortex contiene la rappresentazione sensoriale dei baffi dell'animale e rappresenta uno dei primi stadi di elaborazione dell'informazione tattile, insieme al ganglio del trigemino e al talamo. Essa è un'area di primaria importanza per lo studio del funzionamento della corteccia cerebrale, visto che le colonne corticali che formano i blocchi di base della neocorteccia possono essere visualizzati facilmente all'interno della barrel cortex. La barrel cortex inoltre è utilizzata come sistema di test in numerose metodologie innovative, grazie alla sua struttura unica ed istantaneamente identificabile, e grazie anche al fatto che le specie dotate di barrel, i roditori, sono gli animali da laboratorio più comuni. La barrel cortex e le sue interconnessioni neuronali sono stati oggetto delle ricerche più disparate in questi ultimi decenni. Attualmente, alcuni studi (come questo) non mirano solamente a comprendere meglio la barrel cortex, ma anche ad analizzare problematiche in campi scientifici collegati, utilizzando la barrel cortex come modello base. In questo lavoro, sono stati evocati segnali LFP nella barrel cortex tramite deflessioni ripetute dei baffi dell'animale, realizzate in modo controllato tramite un sistema di deflessione piezoelettrica a closed-loop innescato da un sistema di acquisizione LabView. Le risposte evocate generate nella barrel dalla stimolazione ripetuta dei baffi presentano elevata variabilità nella forma e nelle latenze temporali. Inoltre, il tipo di anestesia utilizzata può influenzare profondamente il profilo della risposta evocata. Questo studio riporta i risultati preliminari sulla variabilità della risposta neuronale e sull'effetto di due anestetici di uso comune su questi segnali, confrontando le distribuzioni delle risposte evocate in ratti anestetizzati con tiletamina-xylazina (il quale agisce prevalentemente sui recettori eccitatori di tipo NMDA) e uretano (che agisce in modo più bilanciato e complesso su entrambi i sistemi eccitatori ed inibitori, preservando la plasticità sinaptica). Sono state analizzate e discusse alcune caratteristiche rappresentative del segnale evocato (ad esempio, le latenze temporali e l'ampiezza degli eventi), registrato a varie profondità corticali. Per tutte le prondità corticali acquisite, sono state stimate le distribuzioni statistiche di tali parametri, in modo da valutare la variabilità degli LFP evocati dalle stimolazioni meccaniche individuali delle vibrisse del ratto lungo l'intera colonna corticale. I primi risultati presentano una grande variabilità nelle risposte corticali, sia in latenza che in ampiezza. Inoltre, è stata riscontrata una differenza significativa nella latenza del primo picco principale delle risposte evocate: gli LFP evocati in animali anestetizzati con tiletamina-xylazina presentavano una latenza più lunga di quelli registrati in ratti anestetizzati con uretano. Inoltre, le distribuzioni dei parametri analizzati erano più strette e piccate in uretano, in corrispondenza di tutte le profondità corticali. Questo comportamento è sicuramente da attribuire al differente meccanismo d'azione dei due anestetici su specifici recettori sinaptici, e quindi nell'elaborazione e nella trasmissione dell'informazione sensoriale lungo tutto il percorso corticale. E' stato inoltre discusso il ruolo della attività basale nella modulazione della risposta evocata. A questo proposito, è stata registrata l'attività spontanea in corrispondenza dei vari layer corticali ed analizzata nel contesto statistico delle 'valanghe neuronali'. Una valanga neuronale è una cascata di attività elettrica in una rete neuronale, la cui distribuzione statistica dei parametri principali (dimensione e vita media) può essere approssimata da una legge di potenza. La distribuzione delle dimensioni di una valanga in una rete neuronale segue una legge di potenza del tipo P(s)=s^-a, con a=1.5. Tale esponente è un riflesso delle correlazioni spaziali a lungo raggio nell'attività neuronale spontanea. Dal momento che i picchi negativi (nLFPs) nelle tracce elettrofisiologiche originano dalla somma di potenziali d'azione sincronizzati generati da neuroni posti nelle vicinanze dell'elettrodo di registrazione, ci siamo chiesti se fosse possibile modellizare i singoli nLFP registrati nell'attività basale tramite un singolo elettrodo come il risultato di valanghe neuronali locali. Pertanto, abbiamo analizzato la distribuzione della dimensione (cioè l'ampiezza in uV) di tali picchi, in modo da identificare una distribuzione power-law appropriata, che potesse descrivere anche le registrazioni a singolo elettrodo. Infine, sono presentate e discusse le prime registrazioni in assoluto degli LFP evocati lungo un'intera colonna corticale ottenute tramite l'ultima generazione di chip impiantabili a tecnologia CMOS. Questi ultimi presentano una matrice di 256 siti di registrazione, organizzata secondo due possibili topologie, 16 x 16 o 4 x 64, e avente una distanza tra gli elettrodi pari a 15 um o 33 um rispettivamente. Una precisa dinamica di propagazione dei potenziali evocati può già essere riconosciuta in questi primissimi profili corticali. Nel prossimo futuro, l'uso di questi dispositivi a semiconduttore potrà aiutare a comprendere il decorso di sindromi neurodegerative come il Parkinson o l'Alzheimer, associando sintomi e comportamenti tipo della malattia a specifiche caratteristiche neuronali. I chip impiantabili potranno anche essere utilizzati come 'electroceuticals', ossia potranno aiutare a rallentare (o addirittura a capovolgere) il decorso delle malattie neurogenerative, costituendo le basi di protesi neuronali in grado di sostenere fisicamente o allenare funzionalmente le popolazioni neuronali danneggiate. L'identificazione e il rilevamento di segnali neuronali ad alta risoluzione aiuterà anche a sviluppare complesse interfacce cervello-macchina, che consentiranno il controllo di protesi intelligenti e che forniranno sofisticati meccanismi di feedback a chi ha perso l'uso di alcune parti del proprio corpo o determinate funzioni cerebrali.

Reliable chronic neural recording from focal deep brain structures is impeded by insertion injury and foreign body response, the magnitude of which is correlated with the mechanical mismatch between the electrode and tissue. Thin and flexible neural electrodes cause less glial scarring and record longer than stiff electrodes. However, the insertion of flexible microelectrodes in the brain has been a challenge. A novel insertion method is proposed, and demonstrated, for precise targeting deep brain structures using flexible micro-wire electrodes. A novel electrode guiding system is designed based on the principles governing the buckling strength of electrodes. The proposed guide significantly increases the critical buckling force of the microelectrode. The electrode insertion mechanism involves spinning of the electrode during insertion. The spinning electrode is slowly inserted in the brain through the electrode guide. The electrode guide does not penetrate into cortex. The electrode is inserted in the brain without stiffening it by coating with foreign material or by attaching a rigid support and hence the method is less invasive. Based on two new mechanisms, namely spinning and guided insertion, it is possible to insert ultra-thin micro-wire flexible electrodes in rodent brains without buckling. I have demonstrated successful insertion of 25 µm platinum micro-wire electrodes about 10 mm deep in rat brain. A novel micro-motion compensated ultra-thin flexible platinum microelectrode has been presented for chronic single unit recording. Since manual insertion of the proposed microelectrode is not possible, I have developed a microelectrode insertion device based on the proposed method. A low power low noise 16 channel programmable neural amplifier ASIC has been designed and used to record the neural spikes. The ability to record neural activity during insertion is a unique feature of the developed inserter. In vivo implantation process of the microelectrode has been demonstrated. Microelectrodes were inserted in the Botzinger complex of rat and long term respiratory related neural activity was recorded from live rats. The developed microelectrode has also been used to study brain activity during seizures. In-vivo experimental results show that the proposed method and the prototype insertion system can be used to implant flexible microelectrode in deep brain structures of rodent for brain studies.

Obesity is pandemic. Pharmacological treatment development depends on modeling the regulation of feeding, particularly by free fatty acids (FFA). Most models have been employed in the rat in vivo, and show FFA-stimulated intestinal satiety signals are dependent on the fat’s acyl chain-length, involve cholecystokinin (CCK) secretion, and are mediated by vagal afferents. I hypothesized that an in vitro mouse model could be employed, with sensitivity to measure afferent responses to nutrient stimuli. Male C57BL/6N mice were killed, the intestine harvested en bloc, and a jejunal section dissected with neurovascular mesenteric arcade emanating centrally. The tissue was placed in a Krebs-superfused chamber, the lumen cannulated with the outlet open to drain, and Krebs or other mediators were continuously perfused intraluminally. The dissected afferent nerve was placed in a suction electrode for extracellular recording. Afferent responses to distension and the perfusion of mediators (e.g. CCK or FFA) were tested. Preparations from normal mice (no surgery), or from mice following chronic subdiaphragmatic vagotomy or sham operation, were used to assess vagal afferent contributions. Luminally-perfused CCK (100 nM) increased afferent firing. This response was abolished with the CCK-1 receptor antagonist lorglumide (10 µM). The short-chain fatty acid (SCFA) sodium butyrate (30 mM) potentiated firing. The long-chain fatty acid (LCFA) sodium oleate (1-300 mM) activated concentration-dependent firing (EC50=25.35 mM) that was significantly greater at 30 mM than that evoked by butyrate. Lorglumide (30 µM) abolished the oleate (30 mM) response. The L-type Ca2+ channel (LTCC) inhibitor nicardipine (3 µM), intraluminally, potentiated the oleate response, while bath application abolished it. Vagotomy attenuated the oleate response. Vagotomy abolished the intraluminal CCK (100 nM) response, and attenuated the response to bath-superfused CCK. These findings support FFA chain-length-dependent mesenteric afferent activation and CCK involvement in oleate-induced firing, and suggest LTCC mediation of excitatory and inhibitory oleate response transduction pathways. The murine oleate response was shown to be mostly vagally-mediated, with some spinal contribution, and both vagal and spinal contributions to CCK responses were suggested. These data provide a basis for further investigation in vitro of cellular and molecular mechanisms of afferent satiety signals, and ultimately of obesity pathogenesis.
Thesis (Master, Physiology) -- Queen's University, 2010-06-29 15:56:08.387

My PhD project is focused on two pivotal steps towards understanding the functionality of the brain: - Developing a fluorescence microscope based on Bessel light-sheet illumination to record neural activities by means of calcium imaging. - Providing a suitable transgenic zebrafish model expressing Channelrhodopsins for optogenetic manipulation; Optogenetics apply light to facilitate the interaction with a genetically-engineered cell and/or populations of cells.

Computations in brain circuits involve the coordinated activation of large populations of neurons distributed across brain areas. However, monitoring neuronal activity in the brain of intact animals with high temporal and spatial resolution has remained a technological challenge. Here we address this challenge by developing dense, three-dimensional (3-D) electrode array system for electrophysiology. The front-end of the system is composed of nanofabricated neural probes with ultrathin shanks that are engineered to minimize tissue damage. The probes are connected via flexible cables to custom PCBs that multiplex the electrophysiological signals. This system architecture decouples the front-end both mechanically and thermally from the PCB which carries all active electronics for signal conditioning and multiplexing. This system was validated in vivo with hippocampal recordings from head-fixed mice. The culmination of these efforts was a 3-D array with 1024 sites packed within 0.6 mm³ of tissue that yielded the densest electrophysiological recordings to date.

A thorough understanding of how neurons work is one of the greatest scientific goals in the field of experimental neuroscience. However, four fundamental technical limitations complicate any attempt to study neuronal function with sub-cellular resolution: First, neurons and neuronal processes are small, second, in realistic experimental situations they can be located deep within optically scattering tissue, third, the chemical and electrical signaling that characterizes neuronal behavior happens quickly, and fourth, neurons and neuronal processes have very three dimensional (3D) shapes. Here we develop a tool that overcomes all four listed limitations by combining the technique of multi-photon microscopy with a unique method for 3D laser beam steering. The result is an instrument capable of monitoring physiological signals at multiple locations in the volume of space occupied by a neuron, a task that is unachievable with any other available instrument.

The loss of the ability to walk as the result of neurological injury or disease critically impacts the mobility and everyday lifestyle of millions. The World Heath Organization (WHO) estimates that approximately 1% of the world's population needs the use of a wheelchair to assist their personal mobility. Advances in the field of brain-machine interfaces (BMIs) have recently demonstrated the feasibility of using neuroprosthetics to extract motor information from cortical ensembles for more effective control of upper-limb replacements. However, the promise of BMIs has not yet been brought to bear on the challenge of restoring the ability to walk. A future neuroprosthesis designed to restore walking would need two streams of information flowing between the user's brain and the device. First, the motor control signals would have to be extracted from the brain, allowing the robotic prosthesis to behave in the manner intended by the user. Second, and equally important would be the flow of sensory and proprioceptive information back to the user from the neuroprosthesis. Here, I contribute to the foundation of such a bi-directional brain machine interface for the restoration of walking in a series of experiments in two animal models, designed to show the feasibility of (1) extracting locomotor information from neuronal ensemble activity and (2) sending information back into the brain via cortical microstimulation.

In a set of experiments designed to investigate the extraction of locomotor parameters, I chronically recorded from ensembles of neurons in primary motor (M1) and primary somatosensory (S1) cortices in two adult female rhesus macaques as they walked bipedally, at various speeds, both forward and backward on a custom treadmill. For these experiments, rhesus monkeys were suitable because of their ability to walk bipedally in a naturalistic manner with training. I demonstrate that the kinematics of bipedal walking in rhesus macaques can be extracted from neuronal ensemble recordings, both offline and in real-time. The activity of hundreds of neurons was processed by a series of linear decoders to extract accurate predictions of leg joints in three dimensional space, as well as leg muscle electromyograms (EMGs). Using a multi-layered switching model allowed us to achieve increased extraction accuracy by segregating different behavioral modes of walking.

In a second set of experiments designed to investigate the usage of microstimulation as a potential artificial sensory channel, I instructed two adult female Aotus trivirgatus (owl monkeys) about the location of a hidden food reward using a series of cortical microstimulation patterns delivered to primary somatosensory (S1) cortex. The owl monkeys discriminated these microstimulation patterns and used them to guide reaching movements to one of two targets. Here, owl monkeys were used which were previously implanted with electrode arrays of high longevity and stability. These monkeys were previously trained on a somatosensory cued task, which allowed a quick transition to microstimulation cueing. The owl monkeys learned to interpret microstimulation patterns, and their skill and speed of learning new patterns improved over several months. Additionally, neuronal activity recorded on non-stimulated electrodes in motor (M1), premotor (PMD) and posterior parietal (PP) cortices allowed us to examine the immediate neural responses to single biphasic stimulation pulses as well as overall responses to the spatiotemporal pattern. Using this recorded neuronal activity, I showed the efficacy of several linear classification algorithms during microstimulation.

These results demonstrate that locomotor kinematic parameters can be accurately decoded from the activity of neuronal ensembles, that multichannel microstimulation is a viable information channel for sensorized prosthetics, and that the technical limitations of combining these techniques can be overcome. I propose that bi-directional BMIs integrating these techniques will one day restore the ability to walk to severely paralyzed patients.

Dissertation