Ionic Imbalances and Coupling in Synchronization of Responses in Neurons

Most neurodegenerative diseases (NDD) are a result of changes in the chemical composition of neurons. For example, Alzheimer's disease (AD) is the product of A? peptide deposition which results in changes in the ion concentration. These changes in ion concentration affect the responses of the neuron to stimuli and often result in inducing excessive excitation or inhibition. This paper investigates the dynamics of a single neuron as ion changes occur. These changes are incorporated using the Nernst equation. Within the central and peripheral nervous system, signals and hence rhythms, are propagated through the coupling of the neurons. It was found that under certain conditions the coupling strength between two neurons could mitigate changes in ion concentration. By defining the state of perfect synchrony, it was shown that the effect of ion imbalance in coupled neurons was reduced while in uncoupled neurons these changes had a more significant impact on the neuronal behavior

Perspectives on the Neuroscience of Cognition and Consciousness

The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness

Computational methods toward early detection of neuronal deterioration

In today's world, because of developments in medical sciences, people are living longer, particularly in the advanced countries. This increasing of the lifespan has caused the prevalence of age-related diseases like Alzheimer’s and dementia. Researches show that ion channel disruptions, especially the formation of permeable pores to cations by Aβ plaques, play an important role in the occurrence of these types of diseases. Therefore, early detection of such diseases, particularly using non-invasive tools can aid both patients and those scientists searching for a cure. To achieve the goal toward early detection, the computational analysis of ion channels, ion imbalances in the presence of Aβ pores in neurons and fault detection is done. Any disruption in the membrane of the neuron, like the formation of permeable pores to cations by Aβ plaques, causes ionic imbalance and, as a result, faults occur in the signalling of the neuron.The first part of this research concentrates on ion channels, ion imbalances and their impacts on the signalling behaviour of the neuron. This includes investigating the role of Aβ channels in the development of neurodegenerative diseases. Results revealed that these types of diseases can lead to ionic imbalances in the neuron. Ion imbalances can change the behaviour of neuronal signalling. Therefore, by identifying the pattern of these changes, the disease can be detected in the very early stages. Then the role of coupling and synchronisation effects in such diseases were studied. After that, a novel method to define minimum requirements for synchronicity between two coupled neurons is proposed. Further, a new computational model of Aβ channels is proposed and developed which mimics the behaviour of a neuron in the course of Alzheimer's disease. Finally, both fault computation and disease detection are carried out using a residual generation method, where the residuals from two observers are compared to assess their performance

Synchronization in dynamical networks:synchronizability, neural network models and EEG analysis

Complex dynamical networks are ubiquitous in many fields of science from engineering to biology, physics, and sociology. Collective behavior, and in particular synchronization,) is one of the most interesting consequences of interaction of dynamical systems over complex networks. In this thesis we study some aspects of synchronization in dynamical networks. The first section of the study discuses the problem of synchronizability in dynamical networks. Although synchronizability, i.e. the ease by which interacting dynamical systems can synchronize their activity, has been frequently used in research studies, there is no single interpretation for that. Here we give some possible interpretations of synchronizability and investigate to what extent they coincide. We show that in unweighted dynamical networks different interpretations of synchronizability do not lie in the same line, in general. However, in networks with high degrees of synchronization properties, the networks with properly assigned weights for the links or the ones with well-performed link rewirings, the different interpretations of synchronizability go hand in hand. We also show that networks with nonidentical diffusive connections whose weights are assigned using the connection-graph-stability method are better synchronizable compared to networks with identical diffusive couplings. Furthermore, we give an algorithm based on node and edge betweenness centrality measures to enhance the synchronizability of dynamical networks. The algorithm is tested on some artificially constructed dynamical networks as well as on some real-world networks from different disciplines. In the second section we study the synchronization phenomenon in networks of Hindmarsh-Rose neurons. First, the complete synchronization of Hindmarsh-Rose neurons over Newman-Watts networks is investigated. By numerically solving the differential equations of the dynamical network as well as using the master-stability-function method we determine the synchronizing coupling strength for diffusively coupled Hindmarsh-Rose neurons. We also consider clustered networks with dense intra-cluster connections and sparse inter-cluster links. In such networks, the synchronizability is more influenced by the inter-cluster links than intra-cluster connections. We also consider the case where the neurons are coupled through both electrical and chemical connections and obtain the synchronizing coupling strength using numerical calculations. We investigate the behavior of interacting locally synchronized gamma oscillations. We construct a network of minimal number of neurons producing synchronized gamma oscillations. By simulating giant networks of this minimal module we study the dependence of the spike synchrony on some parameters of the network such as the probability and strength of excitatory/inhibitory couplings, parameter mismatch, correlation of thalamic input and transmission time-delay. In the third section of the thesis we study the interdependencies within the time series obtained through electroencephalography (EEG) and give the EEG specific maps for patients suffering from schizophrenia or Alzheimer's disease. Capturing the collective coherent spatiotemporal activity of neuronal populations measured by high density EEG is addressed using measures estimating the synchronization within multivariate time series. Our EEG power analysis on schizophrenic patients, which is based on a new parametrization of the multichannel EEG, shows a relative increase of power in alpha rhythm over the anterior brain regions against its reduction over posterior regions. The correlations of these patterns with the clinical picture of schizophrenia as well as discriminating of the schizophrenia patients from normal control subjects supports the concept of hypofrontality in schizophrenia and renders the alpha rhythm as a sensitive marker of it. By applying a multivariate synchronization estimator, called S-estimator, we reveal the whole-head synchronization topography in schizophrenia. Our finding shows bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal brain regions. The topography is stable over the course of several months as well as over all conventional EEG frequency bands. Moreover, it correlates with the severity of the illness characterized by positive and negative syndrome scales. We also reveal the EEG features specific to early Alzheimer's disease by applying multivariate phase synchronization method. Our analyses result in a specific map characterized by a decrease in the values of phase synchronization over the fronto-temporal and an increase over temporo-parieto-occipital region predominantly of the left hemisphere. These abnormalities in the synchronization maps correlate with the clinical scores associated to the patients and are able to discriminate patients from normal control subjects with high precision

Dynamics and Effective Connectivity in Bi- and Three–dimensional Neuronal Cultures: from Self–organization to Engineering

[eng] This thesis was part of the European consortium MESOBRAIN, a team of 5 organizations that joined efforts in nanofabrication, cell culturing, imaging and data analysis to build tailored human 3D networks. The thesis timing was limited to 3 years, and several of the resources needed for its development were built from scratch. The main objective of this Ph.D. thesis was to explore complex characteristics of cortical neuronal cultures in terms of effective connectivity and exhaustive network analyses. This objective comprised four research lines: (i) The evaluation of neuronal network resilience and emerging plasticity mechanisms, (ii) the characterization of functional development to underline crucial timepoints in healthy neuronal networks, (iii) the study of 3D network interactions of neurons embedded inside an ECM--like environment, and (iv) the design, construction and viability inspection of neurons seeded on tiny 3D nanoprinted solid scaffold structures as a first step towards recreating cortical columns in vitro. For these multiple lines, we used either primary rat cultures (i,iii,iv) or human--derived neurons (ii). The former group corresponds to cultures with long established protocols that have been thoroughly studied in the field. The latter group corresponds to human neurons derived from iPSCs, a relatively novel model with promising and thrilling applications in regenerative medicine. Despite the increasing use of stem cells in neuroscience, complex systems and medicine, they still lack a thorough exploration in terms of neuronal and circuit formation as well as the properties of the emergent activity patterns. With either primary or stem cells, we explored the formation of neuronal circuits in 2D and 3D, characterized the effective connectivity and rendered a number of network traits. This Thesis combines experiments of highly difficult implementation with detailed data analysis. It was necessary to develop brand new protocols for culturing 3D neuronal networks and for human-derived neurons, the use of different microscopy setups the programming of object detection and tracking software and advance the analysis toolbox of calcium fluorescence data. First, resilience experiments on primary clustered neuronal cultures consisted on progressive perturbations through chemical receptor antagonists. This study represents an inspiring numerical--experimental model to comprehend the impact of plasticity mechanisms in the spontaneous activity of neuronal circuits. The results showed that, upon progressive connectivity blockade through chemical receptors' antagonists, only--excitatory neuronal networks displayed a surprising hyper--efficiency (HE) state for early--onset doses. As plasticity mechanisms influence the response of effective connectivity in the presence of perturbations, these compensatory mechanisms, usually disregarded, must be included in biological modeling as accurately as possible. Otherwise, episodes of functional rewiring and synaptic strengthening could mask important phenomena during experiments that alter channel communication. A simple algorithm that hypothesized an effective synaptic scaling was able to capture the hyper--efficiency state seen in experimental data, while percolation models wrongly predicted a progressive decay. The second research line was a sum of engineering efforts within the MESOBRAIN consortium, the European adventure to build 3D neuronal cultures embedded in hydrogels and with the presence of scaffolds. After several months of biomaterials testing, the candidate D--Clear resulted suitable for the construction of scaffolds, both with primary rat cells and hiPSCs, due to its good optical properties, manageability and biocompatibility. To our knowledge, D--Clear was never used before outside the orthodontic field and could provide a new catalogue of interesting designs for support and guidance of neuronal assemblies. Using this material, we developed a series of designs to offer support and guidance to cortical neurons in a 3D platform. The third research line focused on the study of neuronal development and cell-to-cell interactions in a semi-synthetic hydrogel that resembles the extracellular matrix of the brain. These hydrogel cultures keep the advantages of in vitro models while achieving an effective connectivity and architecture closer to in vivo. Finally, the fourth line of research applied cortical neurons from human-derived pluripotent stem cells to study key developmental stages and characterize the healthy maturation of these cells in vitro. As this technology has tremendous potential for regenerative medicine and to model neuronal diseases, it is urgent to consolidate the capacity of these human neuronal networks to reproduce efficient activity patterns of healthy patients, and explore the differences against the results obtained with animal models.[spa] La presente tesis doctoral se enmarca en el contexto de la Física de la Materia Condensada, la Biofísica y la Neurociencia. Principalmente, se centra en el estudio de la conectividad funcional en cultivos neuronales bidimensionales (2D) y tridimensionales (3D). El trabajo se ha desarrollado en el Laboratorio del director de tesis Dr. Jordi Soriano, en la Facultad de Física de la Universitat de Barcelona, junto con el codirector Dr. Daniel Tornero, en el Hospital Clínic de Barcelona. Esta tesis forma parte del proyecto europeo MESO-BRAIN, del programa Future and Emergent Technologies (FET) de la Comisión Europea, Horizon2020. El trabajo de investigación combina experimentos con cultivos neuronales (de rata embrionaria o células humanas pluripotentes) y un análisis detallado en el contexto de teoría de redes y sistemas complejos. Los principales núcleos del trabajo realizado son los siguientes: (i) Actividad funcional en cultivos de redes neuronales y los mecanismos homeostáticos que emergen en presencia de perturbaciones; (ii) el desarrollo de herramientas de neuroingeniería para preparar cultivos ad hoc con conectividad dirigida mediante scaffolds; (iii) el análisis exhaustivo de los procesos de formación y madurez de redes funcionales humanas obtenidas de células madre pluripotentes inducidas, una nueva tecnología que promete revolucionar el campo de la medicina regenerativa; y (iv) la caracterización de cultivos neuronales 3D en estructuras que imitan la matriz extracelular natural de su entorno. Entre las diversas técnicas para la realización de cultivos tridimensionales, destacan los hidrogeles semi-sintéticos, constituidos en base a polímeros altamente hidratados con alta biocompatibilidad y cuyas propiedades mecánicas pueden ser manipuladas para obtener la estructura óptima según el tipo de tejido. En conjunto, los resultados de la presente tesis muestran la gran versatilidad de los cultivos neuronales y aportan avances relevantes en el estudio de plasticidad en redes neuronales, madurez y desarrollo tanto en 2D como en 3D, con sus correspondientes diferencias, incluyendo el uso de neuronas humanas derivadas de células madre inducidas. En el futuro, estos estudios nos permitirán incrementar nuestro conocimiento sobre el funcionamiento global del cerebro y avanzar en la investigación de diferentes enfermedades neurodegenerativas

Locomotor patterns and persistent activity in self-organizing neural models

The thesis investigates principles of self-organization that may account for the observed structure and behaviour of neural networks that generate locomotor behaviour and complex spatiotemporal patterns such as spiral waves, metastable states and persistent activity. This relates to the general neuroscience problem of finding the correspondence between the structure of neural networks and their function. This question is both extremely important and difficult to answer because the structure of a neural network defines a specific type of neural dynamics which underpins some function of the neural system and also influences the structure and parameters of the network including connection strengths. This loop of influences results in a stable and reliable neural dynamics that realises a neural function. In order to study the relationship between neural network structure and spatiotemporal dynamics, several computational models of plastic neural networks with different architectures are developed. Plasticity includes both modification of synaptic connection strengths and adaptation of neuronal thresholds. This approach is based on a consideration of general modelling concepts and focuses on a relatively simple neural network which is still complex enough to generate a broad spectrum of spatio-temporal patterns of neural activity such as spiral waves, persistent activity, metastability and phase transitions. Having considered the dynamics of networks with fixed architectures, we go on to consider the question of how a neural circuit which realizes some particular function establishes its architecture of connections. The approach adopted here is to model the developmental process which results in a particular neural network structure which is relevant to some particular functionality; specifically we develop a biologically realistic model of the tadpole spinal cord. This model describes the self-organized process through which the anatomical structure of the full spinal cord of the tadpole develops. Electrophysiological modelling shows that this architecture can generate electrical activity corresponding to the experimentally observed swimming behaviour

Neuroendocrine Modulation of Complex Behavior and Physiology in C. elegans

To survive, animals must adapt to a complex and challenging world in a way that is flexible and responsive, while maintaining internal homeostasis. Neuromodulators provide a means to systemically alter behavioral or physiological state based on intrinsic or extrinsic cues, however dysregulated neuroendocrine signaling has negative consequences for fitness and survival. Here I examine neuroendocrine function and dysfunction using the escape response in Caenorhabditis elegans. The RFamide neuropeptide FLP-18 is a co-transmitter with the monoamine tyramine and functions both synergistically and antagonistically to tyramine in coordinating escape behavior. Using behavioral analysis and calcium imaging, I show that FLP-18 functions primarily through the G-protein coupled receptor (GPCR) NPR-5 to increase calcium levels in muscle, enhancing locomotion rate, bending and reversal behavior during the escape response. Furthermore, I examine the relationship between persistent acute stress and resilience using repeated activation of the escape response as a model of neuroendocrine dysregulation. Repeated activation of the escape response shortens lifespan and renders animals more susceptible to thermal, oxidative, and nutritional stress. Tyramine release is necessary and sufficient for this effect and activity of the tyraminergic RIM neurons is differentially regulated by acute versus long-term stressors. Impaired stress resistance requires both the GPCR TYRA-3 in the intestine and intestinal neuropeptide release. Activation of the insulin receptor DAF-2 is downstream of TYRA-3 and inhibits the transcription factors DAF-16/FOXO, SKN-1/Nrf2 and HSF-1, linking monoamine signaling in acute stress to the insulin signaling pathway and impaired resilience to long-term stressors

Dynamics meets Morphology: towards Dymorph Computation

In this dissertation, approaches are presented for both technically using and investigating biological principles with oscillators in the context of electrical engineering, in particular neuromorphic engineering. Thereby, dynamics as well as morphology as important neuronal principles were explicitly selected, which shape the information processing in the human brain and distinguish it from other technical systems. The aspects and principles selected here are adaptation during the encoding of stimuli, the comparatively low signal transmission speed, the continuous formation and elimination of connections, and highly complex, partly chaotic, dynamics. The selection of these phenomena and properties has led to the development of a sensory unit that is capable of encoding mechanical stress into a series of voltage pulses by the use of a MOSFET augmented by AlScN. The circuit is based on a leaky integrate and fire neuron model and features an adaptation of the pulse frequency. Furthermore, the slow signal transmission speed of biological systems was the motivation for the investigation of a temporal delay in the feedback of the output pulses of a relaxation oscillator. In this system stable pulse patterns which form due to so-called jittering bifurcations could be observed. In particular, switching between different stable pulse patterns was possible to induce. In the further course of the work, the first steps towards time-varying coupling of dynamic systems are investigated. It was shown that in a system consisting of dimethyl sulfoxid and zinc acetate, oscillators can be used to force the formation of filaments. The resulting filaments then lead to a change in the dynamics of the oscillators. Finally, it is shown that in a system with chaotic dynamics, the extension of it with a memristive device can lead to a transient stabilisation of the dynamics, a behaviour that can be identified as a repeated pass of Hopf bifurcations