Novel Tools to Investigate Cortical Activity in Paroxysmal Disorders

This PhD project is at the interface between academic research and industry, and is jointly sponsored by the BBSRC and the industrial partner– Scientifica UK. The goal of this research is the development of new instruments and approaches to monitor and manipulate neuronal network activity in disease states. Firstly, (I) I collaborated with Scientifica to develop and utilise the newly developed Laser Applied Stimulation and Uncaging (LASU) system. The combined usage of the LASU system, alongside novel spatially-targeted channelrhodopsin variants, has al- lowed me to test the limits of single-photon optogenetic stimulation in achieving specific activation of targeted neurons. The presented findings demonstrate that, al- though high-resolution stimulation is achievable in the rodent cortex, single-photon stimulation is insufficient to achieve single-cell resolution stimulation. Secondly, (II) I have combined the high temporal resolution of novel, transparent 16-channel epicortical graphene solution-gated field effect transistor (gSGFET) arrays with the large spatial coverage of bilateral widefield Ca2+ fluorescence imaging; to per- form investigations of the relationship between spreading depolarisation (SD) and cortical seizures in awake head-fixed mouse models of epilepsy. To analyse these complex datasets, I developed a bespoke, semi-automated analysis pipeline to pro- cess the data and probe the seizure-SD relationship. I present the advantages of this dual-modality approach by demonstrating the strengths and weaknesses of each recording method, and how a synergistic approach overcomes the limitations of each technique alone. I utilise widefield imaging to perform systematic classification of SD and seizures both temporally and spatially. Detailed electrophysiological anal- ysis of gSGFET data is then performed on extracted time periods of interest. This work demonstrates the complex interaction between seizures and SD, and proposes several mechanisms describing these interactions. The technological and analytical tools presented here lay the groundwork for insightful and flexible experimental paradigms; altogether, able to probe paroxysmal activity in profound detail

Advanced signal processing techniques for multimodal ultrasonic guided wave response

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonUltrasonic technology is commonly used in the eld of Non-Destructive Testing (NDT) of metal structures such as steel, aluminium, etc. Compared to ultrasonic bulk waves that travel in infinite media with no boundary influence, Ultrasonic Guided Waves (UGWs) require a structural boundary for propagation such that they can be used to inspect and monitor long elements of a structure from a single position. The greatest challenges for any UGW system are the plethora of wave modes arising from the geometry of the structural element which propagate with a range of frequency dependent velocities and the interpretation of these combined signals reflected by discontinuities in the structural element. In this thesis, a technique is developed which facilitates the measurement of Time of Arrival (ToA) and group velocity dispersion curves of wave modes for one dimensional structures as far as wave propagation is concerned. A second technique is also presented which employs the dispersion curves to deliver enhanced range measurements in complex multimodal UGW responses. Ultimately, the aforementioned techniques are used as a part of the analysis of previously unreported signals arising from interactions of UGWs with piezoelectric transducers. The first signal processing technique is presented which used a combination of frequency-sweep measurement, sampling rate conversion and the Fourier transform. The technique is applied to synthesized and experimental data in order to identify different wave modes in complex UGW signals. It is demonstrated that the technique has the capability to derive the ToA and group velocity dispersion curve of the wave modes of interest. The second signal processing technique uses broad band excitation, dispersion compensation and cross-correlation. The technique is applied to synthesized and experimental data in order to identify different wave modes in complex UGW signals. It is demonstrated that the technique noticeably improves the Signal to Noise Ratio (SNR) of the UGW response using a priori knowledge of the dispersion curve. It is also able to derive accurate quantitative information about the ToA and the propagation distance. During the development of the aforementioned signal processing techniques, some unwanted wave-packets are identified in the UGW responses which are found to be induced by the coupling of a shear mode piezoelectric transducer at the free edge of the waveguide. Accordingly, the effect of the force on the piezoelectric transducers and the corresponding reflections and mode conversions are studied experimentally. The aforementioned signal processing techniques are also employed as a part of the study. A Finite Element Analysis (FEA) procedure is also presented which can potentially improve the theoretical predictions and converge to results found in experimental routines. The approach enhances the con dence in the FEA models compared to traditional approaches. The outcome of the research conducted in this thesis paves the way to enhance the reliability of UGW inspections by utilizing the signal processing techniques and studying the multimodal responses.The Engineering and Physical Sciences Research Board (EPSRC), The Centre for Electronic Systems Research (CESR) of Brunel University London, The Integrity Management Group (IMG) of TWI and Plant Integrity Ltd

A novel method to rapidly fit conductance-based models to individual neurons

In this thesis, I present a new method of model optimisation that allows the calibration of conductance-based models of neuronal membrane potential to data from just a single neuron, and achieves good correspondence with the reference data in mere minutes. These properties are desirable because they allow investigations of individual variability among neurons of a given type, of homoeostatic processes and non-synaptic plasticity events, as well as of the contribution of particular neuronal properties to the dynamics of small circuits. In the first chapter, the thesis introduces in detail the working principle of the method, which can be summed up as model optimisation using stimuli to isolate parameter subsets (“MOSTIPS”), and represents a major part of the work and novelty of this project. The second chapter focusses on the construction of accurate models of two mammalian potassium channels which, being ectopically expressed in Xenopus laevis oocytes, served as a validation tool for the new method. In the third chapter, I evaluate the new method, presenting results from fitting models to data from synthetic sources as well as the above-mentioned oocytes. Finally, the fourth chapter contains a number of related results from closed-loop electrophysiology approaches, including extensions to the dynamic clamp protocol for both single neurons and hybrid circuits composed of live and simulated neurons, as well as preliminary results from a closed-loop model fitting approach closely related to the main work presented above. The thesis concludes that the newly developed approaches to model fitting constitute valuable additions to existing methods. The MOSTIPS method achieves tightly constrained parametrisations using both less data and less processing time than classical methods, while the related closed-loop fitting approach produces results that closely follow ongoing changes in evoked activity patterns in real time. Conversely, some issues have been left unanswered, including the contribution of the stimulus generation and selection algorithm, the success of which I have been unable to establish, as well as whether the methods developed herein can reliably identify relevant properties of individual cells. Nevertheless, both the particular methods and the general approach of using prior estimates of the model and its parameter values to propose stimulus patterns represent major advances in the field of neuron model optimisation

Deep Learning and Polar Transformation to Achieve a Novel Adaptive Automatic Modulation Classification Framework

Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal\u27s most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture in which it first removes the bias issue for the estimator of fourth-order cumulants, then extracts polar-transformed information of the received IQ waveform\u27s samples, and finally forms a unique dataset to be used in the labeling stage. The labeling stage utilizes a deep learning architecture. Furthermore, we propose a new approach to increasing the classification accuracy in low signal-to-noise ratio conditions by employing a deep belief network platform in addition to the spiking neural network platform to overcome computational complexity concerns associated with deep learning architecture. In the process of evaluating the contributions, we first study each individual FB AMC classifier to derive the respective upper and lower performance bounds. We then propose an adaptive framework that is built upon and developed around these findings. This framework aims to efficiently classify the received signal\u27s modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between classification accuracy and computational complexity for any observed channel conditions derived from the main receiver\u27s equalizer. This framework also provides flexibility in deploying FB AMC classifiers in various environments. We conduct a performance analysis using this framework in which we employ the standard RadioML dataset to achieve a realistic evaluation. Numerical results indicate a notably higher classification accuracy by 16.02% on average when the deep belief network is employed, whereas the spiking neural network requires significantly less computational complexity by 34.31% to label the modulation scheme compared to the other platforms. Moreover, the analysis of employing framework exhibits higher efficiency versus employing an individual FB AMC classifier. Advisor: Hamid R. Sharif-Kashan

Retinal ganglion cells : physiology and prosthesis

The retina is responsible for encoding different aspects of the visual world. Light enters the eyes and is converted by the photoreceptors into electrochemical signals. These signals are processed by the retinal network and proceed afferently to the brain via the axons of the retinal ganglion cells (RGCs). The RGCs outputs are in the form of action potentials (spikes), which encrypt the visual information in terms of spike shape, firing frequencies, and the firing patterns. When the photoreceptors are gone due to disease, vision is lost. The idea of a retinal prosthesis is to activate the surviving RGCs by electrical stimulation in order to recreate vision. In this thesis, I have studied the physiological properties of the RGCs, and reconstructed natural RGC spike trains by electrical stimulation. Chapter 1 introduces the anatomy of the retina and the retinal neurons. How the RGCs respond to light. Electrical stimulation is also discussed. A brief historical summary of the receptive field properties and cell physiology is also presented. Chapter 2 characterizes the intrinsic properties of 16 morphologically defined types of rat RGCs. The intrinsic properties include the biophysical properties due to morphology and dendritic stratification, in addition to physiological properties such as firing behaviours. These properties are also compared with the cat RGC intrinsic properties in order to investigate the variations between the morphologically similar RGCs of the two species. The results suggest that the RGCs among species, even with similar morphologies, do not have conservative intrinsic properties. Chapter 3 examines the details of the spiking properties of the different rat RGC types. Spikes are initiated at the axonal initial segment. A 'single' spike recorded at the soma consists of an axonal spike and a somatic spike. The existence of the two spikes can be recognized by two humps in the phase plot, and further revealed in the higher derivatives of the membrane potential. A principal component analysis shows that the parameters extracted from the phase plots are very useful for a model-independent rat RGC classification. Chapter 4 establishes the foundations for electrical stimulation of the retina. The question is to what extent optimum placement of the stimulating and reference electrodes might be affected by anatomical location. Here we placed the stimulating electrode above or below the retinal inner limiting membrane and found no statistical difference between the thresholds. In addition, reflective axonal spikes from the cut end are discussed. Chapter 5 combines the knowledge obtained in the previous chapters for the sole purpose of reproducing natural RGC outputs when using electrical stimulation. The light responses of the eye under saccadic movements were recorded and used to form the stimulus patterns. The reconstructions were performed on the brisk-transient (BT) and the brisk-sustained (BS) RGCs. Our results suggested that BT RGCs are more capable of following the stimulated stimulus patterns over a wide range of frequencies than the BS RGCs. Chapter 6 concludes the whole thesis

Assessment of microglia influence in synaptic transmission and early amyloid-β plaque deposition in knock-in mouse models for Alzheimer`s disease

Alzheimer´s disease (AD) is the most common type of dementia representing an estimated 60-80% of all cases, and no cure or successful therapy has been found. Two main hallmarks have been identified in AD histopathology: senile plaques, composed of amyloid-β protein (Aβ), and neurofibrillary tangles, composed of phosphorylated TAU protein. Additionally, genetic studies have shown that immune processes play important roles in AD. Microglial gene expression and function are closely correlated to amyloid pathology and are therefore potential targets for altering the progression of AD. Recently an Amyloid Precursor Protein knock-in line was generated, which, in contrast to transgenic AD mice shows an Aβ pathology without overexpression. This project aims to analyse if microglial cells are active modulators of Aβ plaques and synaptic changes in APP knock-in mice at the early stages of the pathology. Initial characterisation of electrophysiological phenotypes for APPNL-G-F and APPNL-F were studied. Also, dose- and time-dependent effects of the drug PLX5622, which has been shown to specifically deplete microglia, were analysed. APPNL-G-F mice exhibited unaltered synaptic transmission at 3.5 months of age regardless a clear accumulation of hippocampal Aβ plaques. APPNL-F mice showed increased glutamate release probability, unchanged spontaneous excitatory activity and little accumulation of Aβ plaques at 10 months of age. After PLX5622 treatments, surviving microglia tended to be CD68+ in both APP knock-in models. Partial microglia ablation led to aged but not young wild type animals mimicking the increased glutamate release probability and exacerbated the APP knock-in phenotype. Complete ablation was less effective in altering synaptic function, while neither treatment altered plaque load. It is suggested that alteration of surviving microglia towards a phagocytic phenotype, rather than microglial loss, drives age-dependent effects on glutamate release that become exacerbated in AD

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

All-optical interrogation of neural circuits during behaviour

This thesis explores the fundamental question of how patterns of neural activity encode information and guide behaviour. To address this, one needs three things: a way to record neural activity so that one can correlate neuronal responses with environmental variables; a flexible and specific way to influence neural activity so that one can modulate the variables that may underlie how information is encoded; a robust behavioural paradigm that allows one to assess how modulation of both environmental and neural variables modify behaviour. Techniques combining all three would be transformative for investigating which features of neural activity, and which neurons, most influence behavioural output. Previous electrical and optogenetic microstimulation studies have told us much about the impact of spatially or genetically defined groups of neurons, however they lack the flexibility to probe the contribution of specific, functionally defined subsets. In this thesis I leverage a combination of existing technologies to approach this goal. I combine two-photon calcium imaging with two-photon optogenetics and digital holography to generate an “all-optical” method for simultaneous reading and writing of neural activity in vivo with high spatio-temporal resolution. Calcium imaging allows for cellular resolution recordings from neural populations. Two-photon optogenetics allows for targeted activation of individual cells. Digital holography, using spatial light modulators (SLMs), allows for simultaneous photostimulation of tens to hundreds of neurons in arbitrary spatial locations. Taken together, I demonstrate that this method allows one to map the functional signature of neurons in superficial mouse barrel cortex and to target photostimulation to functionally-defined subsets of cells. I develop a suite of software that allows for quick, intuitive execution of such experiments and I combine this with a behavioural paradigm testing the effect of targeted perturbations on behaviour. In doing so, I demonstrate that animals are able to reliably detect the targeted activation of tens of neurons, with some sensitive to as few as five cortical cells. I demonstrate that such learning can be specific to targeted cells, and that the lower bound of perception shifts with training. The temporal structure of such perturbations had little impact on behaviour, however different groups of neurons drive behaviour to different extents. In order to probe which characteristics underly such variation, I tested whether the sensory response strength or correlation structure of targeted ensembles influenced their behavioural salience. Whilst these final experiments were inconclusive, they demonstrate their feasibility and provide us with some key actionable improvements that could further strengthen the all-optical approach. This thesis therefore represents a significant step forward towards the goal of combining high resolution readout and perturbation of neural activity with behaviour in order to investigate which features of the neural code are behaviourally relevant