An Energy Efficient non-volatile FPGA Digital Processor for Brain Neuromodulation

PhD ThesisBrain stimulation technologies have the potential to provide considerable clinical benefits for people with a range of neurological disorders. Recent neuroscience studies have shown that considerable information of brain states is contained in the low frequency local field potential (If-LFP; below 5Hz) recordings with application in real-time closed-loop neurostimulation for treating neurological disorders. Given these signals can be sampled at low sampling rate and hence provide sparse data streams, there is an opportunity to design implantable neuroprosthesis with long battery lifecycles which enables enough processing power to implement long-term, real-time closed loop control algorithms. In this thesis, a closed-loop embedded digital processor has been created for use in rodent neuroscience experiments. The first contribution of this work is to develop a mathematical analytical design approach of feedback controller for suppressing high-amplitude epileptic activity in the neuron mass model to form a better understanding of how to perform a better closed-loop stimulation to control seizures. The second contribution and the third contribution are combined to present an exploratory energy-efficient digital processor architecture built with commercial off-the-shelf non-volatile FPGAs and microcontroller for sparse data processing of brain neuromodulation. A digital hardware design of an exemplar PID control algorithm has been implemented on this proposed digital architecture. A new power computing diagram of this time-driven approach significantly reduced the power consumption which suggests that a digital combined control system of non-volatile FPGAs and microcontroller outweighs a digital control system of microcontroller with microcontroller regarding computing time cost and energy consumption supposing one microcontroller is always required. Taken together, this digital energy-efficient processor architecture gives important insights and viewpoints for the further advancements of neuroprosthesis for brain neurostimulation to achieve lower power consumption for sparse sampling data rate

Mean field modelling of human EEG: application to epilepsy

Aggregated electrical activity from brain regions recorded via an electroencephalogram (EEG), reveal that the brain is never at rest, producing a spectrum of ongoing oscillations that change as a result of different behavioural states and neurological conditions. In particular, this thesis focusses on pathological oscillations associated with absence seizures that typically affect 2–16 year old children. Investigation of the cellular and network mechanisms for absence seizures studies have implicated an abnormality in the cortical and thalamic activity in the generation of absence seizures, which have provided much insight to the potential cause of this disease. A number of competing hypotheses have been suggested, however the precise cause has yet to be determined. This work attempts to provide an explanation of these abnormal rhythms by considering a physiologically based, macroscopic continuum mean-field model of the brain's electrical activity. The methodology taken in this thesis is to assume that many of the physiological details of the involved brain structures can be aggregated into continuum state variables and parameters. The methodology has the advantage to indirectly encapsulate into state variables and parameters, many known physiological mechanisms underlying the genesis of epilepsy, which permits a reduction of the complexity of the problem. That is, a macroscopic description of the involved brain structures involved in epilepsy is taken and then by scanning the parameters of the model, identification of state changes in the system are made possible. Thus, this work demonstrates how changes in brain state as determined in EEG can be understood via dynamical state changes in the model providing an explanation of absence seizures. Furthermore, key observations from both the model and EEG data motivates a number of model reductions. These reductions provide approximate solutions of seizure oscillations and a better understanding of periodic oscillations arising from the involved brain regions. Local analysis of oscillations are performed by employing dynamical systems theory which provide necessary and sufficient conditions for their appearance. Finally local and global stability is then proved for the reduced model, for a reduced region in the parameter space. The results obtained in this thesis can be extended and suggestions are provided for future progress in this area

Stochastic neural network dynamics: synchronisation and control

Biological brains exhibit many interesting and complex behaviours. Understanding of the mechanisms behind brain behaviours is critical for continuing advancement in fields of research such as artificial intelligence and medicine. In particular, synchronisation of neuronal firing is associated with both improvements to and degeneration of the brain’s performance; increased synchronisation can lead to enhanced information-processing or neurological disorders such as epilepsy and Parkinson’s disease. As a result, it is desirable to research under which conditions synchronisation arises in neural networks and the possibility of controlling its prevalence. Stochastic ensembles of FitzHugh-Nagumo elements are used to model neural networks for numerical simulations and bifurcation analysis. The FitzHugh-Nagumo model is employed because of its realistic representation of the flow of sodium and potassium ions in addition to its advantageous property of allowing phase plane dynamics to be observed. Network characteristics such as connectivity, configuration and size are explored to determine their influences on global synchronisation generation in their respective systems. Oscillations in the mean-field are used to detect the presence of synchronisation over a range of coupling strength values. To ensure simulation efficiency, coupling strengths between neurons that are identical and fixed with time are investigated initially. Such networks where the interaction strengths are fixed are referred to as homogeneously coupled. The capacity of controlling and altering behaviours produced by homogeneously coupled networks is assessed through the application of weak and strong delayed feedback independently with various time delays. To imitate learning, the coupling strengths later deviate from one another and evolve with time in networks that are referred to as heterogeneously coupled. The intensity of coupling strength fluctuations and the rate at which coupling strengths converge to a desired mean value are studied to determine their impact upon synchronisation performance. The stochastic delay differential equations governing the numerically simulated networks are then converted into a finite set of deterministic cumulant equations by virtue of the Gaussian approximation method. Cumulant equations for maximal and sub-maximal connectivity are used to generate two-parameter bifurcation diagrams on the noise intensity and coupling strength plane, which provides qualitative agreement with numerical simulations. Analysis of artificial brain networks, in respect to biological brain networks, are discussed in light of recent research in sleep theor

The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space–time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences

Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars

Beyond retigabine: Design, identification, and pharmacological characterization of novel neuronal Kv7 channel activators

The Kv7 subfamily of voltage-gated potassium channels includes 5 members (Kv7.1-Kv7.5) having distinct expression patterns and physiological roles. Kv7.2 and Kv7.3 subunits are mainly expressed in the nervous system, where they underlie the so-called M-current (IKM), a sub-threshold K+ current controlling action potential generation. Neuronal Kv7 potassium channels are critical regulators of neuronal excitability; indeed, loss-of-function mutations in the genes encoding for Kv7.2 and Kv7.3 are responsible for a wide spectrum of early-onset epilepsies. On the other hand, retigabine is a strong activator of the Kv7 currents, representing the first antiepileptic drug acting on Kv7 channels. Approved in 2011 for adjunctive therapy in adults showing drug-resistant partial onset seizures with or without secondary generalization, retigabine suppresses neuronal hyperexcitability by shifting the Kv7.2/3 current activation threshold toward more hyperpolarized potentials, thereby increasing their maximal current. Unfortunately, retigabine, suffers from considerable drawbacks including poor selectivity for Kv7 subtypes, short half-life, poor brain penetration and chemical instability. The latter, represents one of the main clinical concern over retigabine; light exposure may cause photodegradation and oxidation, leading to dimer formation, which induces retinal and mucocutaneous blue-gray discoloration in patients taking the drugs more than 3 years. For these reasons, leading to a progressively reduced use of the drug, the manufacturing company (GSK) has decided to withdraw the drug from the market since June 2017. Since no KCNQ activator is currently available for clinical use, this work originates from our effort to identify novel and safer IKM activators. For this purpose, we synthesized a library of 41 retigabine derivatives, structurally characterized by modification that aim to overcome at least some of the limitations of retigabine and we developed a fluorescence-based assay to rapidly evaluate the effect of these derivatives on Kv7 channel

Neonatal Stimulation of PKC Epsilon Signaling Normalizes Fragile X-Associated Deficits in PVN Oxytocin Expression and Later-Life Social and Anxiety Behavior

Fragile X Syndrome (FXS) is an inherited developmental disorder characterized by disturbances in emotional and social behavior. Our studies have revealed suppressed hippocampal PKCε expression in Fmr1 knockout (KO) mice, the leading model of FXS. To compensate for this deficiency, we stimulated PKCε in neonatal KO mice by administering a selective PKCε activator, dicyclopropyl-linoleic acid (DCP-LA), and studied its effect on ventral hippocampal neurons and a proximal target of the ventral hippocampus, the hypothalamus, which regulates social and emotional behavior. We observed that at postnatal day 18 (P18), vehicle-treated KO mice displayed increased surface localization of the 3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor subunit GluR2 in the ventral CA1 region, indicative of increased neuronal excitability. Since the hippocampus is known to exert an inhibitory influence on the hypothalamus, we tested if this possible CA1 stimulation was associated with a suppression of oxytocin synthesis in the hypothalamus. Intriguingly, the number of oxytocin+ cells in the hypothalamic paraventricular nucleus (PVN) of P20 KO mice was sharply suppressed. However, both the increased surface localization of GluR2 and the suppression of PVN oxytocin+ cells in the KO mice were rescued by DCP-LA treatment from P6-14, to levels comparable to that in the wild-type controls. Moreover, this neonatal treatment regimen was able to fully rescue hyper-anxiety and social behavior deficits in adult (\u3eP60) KO mice. Thus, we present a novel strategy to circumvent aberrant brain development in FXS and accompanying behavioral deficits, by activating PKCε signaling during neonatal development

Investigating the mechanisms of action of phytocannabinoids and a novel cognitive enhancer to target the comorbidity of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common type of epilepsy and exists with memory loss as a comorbidity. The conventional therapy available to treat these disorders achieves only modest therapeutic efficacy at best. This study investigates two potential treatments: phytocannabinoids to alleviate seizures, and a novel cognitive enhancer to restore/halt memory deficits. The anti-convulsant properties of cannabidiol (CBD) were first examined with regards to the neuropathology of two major types of hippocampal interneurons expressing parvalbumin (PV) and cholecystokinin (CCK) which are thought to dysfunction during epilepsy. Immunohistochemistry experiments using an in vivo kainic-acid induced epileptic rat model, revealed that PV- and CCK-immunopositive interneurons were significantly affected during epilepsy. This effect was greatly reduced following CBD treatment, suggesting that CBD exerts a neuroprotective function. The effects of CBD on the intrinsic membrane properties of these interneurons, together with hippocampal pyramidal cells, were further investigated in acute brain slices of rat seizure models of TLE (in vivo kainic acid-induced and in vitro Mg2+ free-induced). Whole-cell recordings revealed that bath application of CBD (10 µM) normalised the firing frequency of epileptic adapting pyramidal cells to healthy control levels. A similar effect was seen in hippocampal CCK-immunopositive Schaffer collateral associated (SCA) interneurons. In contrast, CBD resulted in an increased firing of PV-immunopositive interneurons, thus increasing their excitability and restoring the impaired membrane properties of the cells apparent in the epileptic models. The effects of cannabidivarin (CBDV), a similar cannabinoid compound, on the intrinsic membrane properties of these cell types were also evaluated. Additionally, CBDV affected excitatory postsynaptic currents by reducing excitation. In an attempt to address the memory impairment aspect associated with TLE, I investigated the neuronal effects of a5AM21, a novel potential memory enhancer. Electrophysiological experiments revealed that a5AM21 preferentially acts on 5-containing gamma (γ)-aminobutyric acid (GABA) type A (GABAA) receptors, reducing their inhibitory effects. Furthermore, data obtained using behavioural experiment paradigm, the eight-arm radial maze, suggest a significant improvement in short- and long-term memory retrieval in rats treated with a5AM21. In conclusion, the results reveal the potential mechanisms of action of two therapies to alleviate seizures and memory impairment, and the future goals would be to combine CBD/CBDV and a5AM21 as a promising novel targeted therapy for TLE