Theoretical Investigation of NMDA Effect on the Cerebral Cortex

This thesis examines the dynamical behaviour of incorporating NMDA (an excitatory neurotransmitter) for the electrodynamic model of the cerebral cortex. The model used is the mean-field model developed by Steyn-Ross et al. (2005) which describes the behaviour of the cortex in terms of parameters averaged over spatially localised populations. The behaviour of the model is determined by the four control parameters: inhibitory effect li, subcortical drive s, and NMDA neurotransmitter e ect set by an excitatory factor le and the magnesium concentration C. Adopting this model could give a better understanding of the cortex functionality and the anaesthetic mechanism. The model predicts that there are either one or three stationary states available to the cortex. We identify two of these with highly activated state and a quiescent state and focus on the transition between the two. Theoretical stability predictions (eigenvalue analysis) verified by a numerical simulation show that the system is unstable between the two Hopf bifurcations. In addition, in the stable region the steady states remains stable under a small perturbation, while in the unstable region either a transition between states or a limit cycle (oscillation) occurs depending on the position of the steady state

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Intermittent Theta Burst Stimulation: Application to Spinal Cord Injury Rehabilitation and Computational Modeling

Loss of motor function from spinal cord injuries (SCI) results in loss of independence. Rehabilitation efforts are targeted to enhance the ability to perform activities of daily living (ADLs), but outcomes from physical therapy alone are often insufficient. Neuromodulation techniques that induce neuroplasticity may push the limits on recovery. Neuromodulation by intermittent theta burst transcranial magnetic stimulation (iTBS) induces neuroplasticity by increasing corticomotor excitability, though this has most frequently been studied with motor targets and on individuals not in need of rehabilitation. Increased corticomotor excitability is associated with motor learning. The response to iTBS, however, is highly variable and unpredictable, while the mechanisms are not well understood. Studies have proposed brain anatomy and individual subject differences as a source of variability but have not quantified the effects. Existing models have not incorporated known neurotransmitter changes at the synaptic level to pair mechanisms to cell output in a neural circuit. To use iTBS in practical rehabilitative efforts, the technique must either be consistent, have a predictable responsiveness, or present with enough mechanistic understanding to improve its efficacy. To that effect, this study has two primary objectives for the improvement of rehabilitation techniques. The first is to establish how iTBS affects both a motor target and population that typically undergoes physical rehabilitation often with unsatisfactory outcomes, in this case the biceps brachii in individuals with SCI and relate the empirical effects of iTBS to individual anatomy. This will establish the consistency of the technique and predictability of its effects, relevant to rehabilitative efforts. The secondary objective is to create the foundation of a model that exhibits circuit organization, which would start the development of a motor neuroplasticity functional unit with simulation of the synaptic long-term potentiation (LTP) like effects of iTBS. Summary of Methods: iTBS was performed targeting the biceps, on multiple cohorts, with changes in motor evoked potential amplitude (MEP) tracked after sham and active intervention. This was compared between nonimpaired individuals and those with SCI. Furthermore, iTBS of both biceps and first dorsal interosseus (FDI) was compared to simulation of TMS on MRI derived head models to establish the impact of individualized neuroanatomy. Finally, a motor canonical neural circuit was programmed to display fundamental physiological spiking behavior of membrane potentials. Summary of Results: iTBS did facilitate corticomotor excitability in the biceps of nonimpaired individuals and in those with SCI. iTBS had no group-wide effect on the FDI, highlighting the variability in response to the protocol. TMS response (motor thresholds) and iTBS response (change in MEPs) both were related to parameters extracted from MRI-derived head models representing variations in individual neuroanatomy. The neural circuit model represents a canonical networked unit. In the future, this can be further tuned to exhibit biological variability and generate population-based values being run in parallel, while matching the understood mechanisms of neuroplasticity: disinhibition and LTP. Conclusion: These studies provide missing information of iTBS responsivity by (1) determining group-wide responsiveness in a clinically relevant target; (2) establishing individual level influences that affect responsivity which can be measured prior to iTBS; and (3) beginning design of a tool to test a single neural circuit and its mechanistic responses

Dynamic models of brain imaging data and their Bayesian inversion

This work is about understanding the dynamics of neuronal systems, in particular with respect to brain connectivity. It addresses complex neuronal systems by looking at neuronal interactions and their causal relations. These systems are characterized using a generic approach to dynamical system analysis of brain signals - dynamic causal modelling (DCM). DCM is a technique for inferring directed connectivity among brain regions, which distinguishes between a neuronal and an observation level. DCM is a natural extension of the convolution models used in the standard analysis of neuroimaging data. This thesis develops biologically constrained and plausible models, informed by anatomic and physiological principles. Within this framework, it uses mathematical formalisms of neural mass, mean-field and ensemble dynamic causal models as generative models for observed neuronal activity. These models allow for the evaluation of intrinsic neuronal connections and high-order statistics of neuronal states, using Bayesian estimation and inference. Critically it employs Bayesian model selection (BMS) to discover the best among several equally plausible models. In the first part of this thesis, a two-state DCM for functional magnetic resonance imaging (fMRI) is described, where each region can model selective changes in both extrinsic and intrinsic connectivity. The second part is concerned with how the sigmoid activation function of neural-mass models (NMM) can be understood in terms of the variance or dispersion of neuronal states. The third part presents a mean-field model (MFM) for neuronal dynamics as observed with magneto- and electroencephalographic data (M/EEG). In the final part, the MFM is used as a generative model in a DCM for M/EEG and compared to the NMM using Bayesian model selection

Development of Fast, Distributed Computational Schemes for Full Body Bio-Models and Their Application to Novel Action Potential Block in Nerves Using Ultra-Short, High Intensity Electric Pulses

An extremely robust and novel scheme for computing three-dimensional, time-dependent potential distributions in full body bio-models is proposed, which, to the best of our knowledge, is the first of its kind. This simulation scheme has been developed to employ distributed computation resources, to achieve a parallelized numerical implementation for enhanced speed and memory capability. The other features of the numerical bio-model included in this dissertation research, are the ability to incorporate multiple electrodes of varying shapes and arbitrary locations. The parallel numerical tool also allows for user defined, current or potential stimuli as the excitation input. Using the available computation resources at the university, a strong capability for extremely large bio-models was developed. So far a maximum simulation comprised of 6.7 million nodes has been achieved for a full rat bio-model with a 1 mm spatial resolution at an average of 30 seconds per iteration. The ability to compute the resulting potential distribution in a full animal body allows for realistic and accurate studies of bio-responses to electrical stimuli. For example, the voltages computed from the full-body models at various sites and tissue locations could be used to examine the potential for using nanosecond, high-intensity, pulsed electric fields for blocking neural action or action potential (AP) propagation. This would be a novel, localized, and reversible method of controlling neural function without tissue damage. It could potentially be used in electrically managed pain relief, non-lethal incapacitation, and neural/muscular therapy. The above concept has quantitatively been evaluated in this dissertation. Specifically, the effects of high-intensity (kilo-Volt), ultra-short (∼100 nanosecond) electrical pulses have been evaluated, and compared with available experimental data. Good agreement with available data is demonstrated. It is also shown that nerve membrane electroporation, brought about by the high-intensity, external pulsing, could indeed be instrumental in halting AP propagation. Simulations based on a modified distributed cable model to represent nerve segments have been used to demonstrate a numerical proof-of-concept

Relating macroscopic measures of brain activity to fast dynamic neuronal interactions

The aim of this thesis was to find a systematic relationship between neuronal synchrony and firing rates, that would enable us to make inferences about one given knowledge of the other. Functional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), are sensitive to changes in overall population synaptic activity, that can be interpreted in terms of rate coding for a particular stimulus or task. Characterising the relationship between synchrony and firing rates would facilitate inferences about fast neuronal interactions on the basis of macroscopic measures such as those obtained by fMRI. In this thesis, we used computer simulations of neuronal networks and fMRI in humans to investigate the relationship between mean synaptic activity and fast synchronous neuronal interactions. We found that the extent to which different neurons engage in fast dynamic interactions is largely dependent on the neuronal population firing rates and vice versa, i.e. as one metric changes (either activity or synchrony), so does the other. Additionally, as a result of the strong coupling between overall activity and neuronal synchrony, there is also a robust relationship between background activity and stimulus-evoked activity: Increased background activity increases the gain of the neurons, by decreasing effective membrane time constants, and enhancing stimulus-evoked population activity through the selection of fast synchronous dynamics. In concluding this thesis, we tested and confirmed, with fMRI in humans, that this mechanism may account for attentional modulation, i.e. the change in baseline neuronal firing rates associated with attention, in cell assemblies selectively responding to an attended sensory attribute, enhances responses elicited by presentation of that attribute

Dynamics and precursor signs for phase transitions in neural systems

This thesis investigates neural state transitions associated with sleep, seizure and anaesthesia. The aim is to address the question: How does a brain traverse the critical threshold between distinct cortical states, both healthy and pathological? Specifically we are interested in sub-threshold neural behaviour immediately prior to state transition. We use theoretical neural modelling (single spiking neurons, a network of these, and a mean-field continuum limit) and in vitro experiments to address this question. Dynamically realistic equations of motion for thalamic relay neuron, reticular nuclei, cortical pyramidal and cortical interneuron in different vigilance states are developed, based on the Izhikevich spiking neuron model. A network of cortical neurons is assembled to examine the behaviour of the gamma-producing cortical network and its transition to lower frequencies due to effect of anaesthesia. Then a three-neuron model for the thalamocortical loop for sleep spindles is presented. Numerical simulations of these networks confirms spiking consistent with reported in vivo measurement results, and provides supporting evidence for precursor indicators of imminent phase transition due to occurrence of individual spindles. To complement the spiking neuron networks, we study the Wilson–Cowan neural mass equations describing homogeneous cortical columns and a 1D spatial cluster of such columns. The abstract representation of cortical tissue by a pair of coupled integro-differential equations permits thorough linear stability, phase plane and bifurcation analyses. This model shows a rich set of spatial and temporal bifurcations marking the boundary to state transitions: saddle-node, Hopf, Turing, and mixed Hopf–Turing. Close to state transition, white-noise-induced subthreshold fluctuations show clear signs of critical slowing down with prolongation and strengthening of autocorrelations, both in time and space, irrespective of bifurcation type. Attempts at in vitro capture of these predicted leading indicators form the last part of the thesis. We recorded local field potentials (LFPs) from cortical and hippocampal slices of mouse brain. State transition is marked by the emergence and cessation of spontaneous seizure-like events (SLEs) induced by bathing the slices in an artificial cerebral spinal fluid containing no magnesium ions. Phase-plane analysis of the LFP time-series suggests that distinct bifurcation classes can be responsible for state change to seizure. Increased variance and growth of spectral power at low frequencies (f < 15 Hz) was observed in LFP recordings prior to initiation of some SLEs. In addition we demonstrated prolongation of electrically evoked potentials in cortical tissue, while forwarding the slice to a seizing regime. The results offer the possibility of capturing leading temporal indicators prior to seizure generation, with potential consequences for understanding epileptogenesis. Guided by dynamical systems theory this thesis captures evidence for precursor signs of phase transitions in neural systems using mathematical and computer-based modelling as well as in vitro experiments

Multifunctional nanostructures for intracellular delivery and sensing in electrogenic cells

Biological studies on in vitro cell cultures are of fundamental importance for investigating cell response to external stimuli, such drugs for specific treatments, or for studying communication between cells. In the electrophysiology field, multielectrode array devices (MEA) are the gold standard for the study of large ensambles of electrogenic cells. Thus, their improvement is a central topic nowadays in neuroscience and cardiology [1]. In the last decades, thanks to the adoption of nanotechnologies, the study of physiological and pathological conditions of electro-active cells in culture have becomes increasingly accurate[2], allowing for monitoring action potentials from many cells simultaneously. In fact, nanoscale biomaterials were able to overcome the limitations of previous technologies, paving the way to the development of platforms for interfacing the electrogenic cells at unprecedented spatiotemporal scales. These devices, together with microfluidics, are starting to be used for drug screening and pharmaceutical drug development since they represent a powerful tool for monitoring cell response when cultures are stimulated by target compounds. Many pharmaceutical agents, however, including various large molecules (enzymes, proteins, antibodies) and even drug-loaded pharmaceutical nanocarriers, need to be delivered intracellularly to exercise their therapeutic action inside the cytoplasm[3]. Nanoscale electrodes offer individual cell access and non-destructive poration of the cellular membrane enabling high capability in the delivery of biomolecules. Among all the techniques, electroporation have proven encouraging potential as alternative to the carrier mediated methods for molecular delivery into cultured cells[4]. In this regard, different groups [5][6][7] exploited the integration of nanostructures with delivering capabilities with single-cell specificity and high throughput in biosensing platforms. These efforts provided powerful tools for advancing applications in therapeutics, diagnostics, and drug discovery, in order to reach an efficient and localized delivery on a chip. Despite these new tactics, there is still a critical need for the development of a functional approach that combines recording capabilities of nanostructured biosensors with intracellular delivery. The device should provide for tight contact between cells and electrode so as to enable highly localized delivery and optimal recording of action potentials in order to attain a high degree of prediction for the disease modeling and drug discovery. This \u201con-chip\u201d approach will help to gain deeper insight in several bio-related studies and analyses, providing a comprehensive knowledge of the entire cellular dynamics when selectively stimulated by the desired bio-molecules. In the first part of this dissertation, a solution will be proposed in order to fill this gap and respond to this need in the biology field. In the first chapter, I will describe briefly the principles of action potentials and how neurons and cardiomyocyte are composed, together with the development of electrophysiology and the advent of multielectrode arrays. In the second chapter, more details about fabrication and cell-electrode system modelling will be explained. In the same chapter, I will explore the development of multielectrode arrays up to the present days, along with the advent of nanotechnologies and the related techniques for improving the previous platforms. The different cell poration techniques will be described in order to reach the best recording capabilities without damaging cells. Electroporation, optoporation and spontaneous poration will be presented and the chosen technique for our application (electroporation) will be reviewed more in detail. In the third chapter, different methodologies for intracellular delivery will be explained, focusing also on the electroporation technique. A small paragraph about the integration of these techniques on chip will be inserted to illustrate the state of the art of these devices. The fourth chapter will explicate in details the Microfluidic multielectrode array idea, the approach used in order to fabricate this novel platform from scratch, the experiments carried out to verify its capabilities and the associated results. In the last paragraph, I will discuss how the proposed platform could became suitable for the day to day uses in research activity by employing nanoporous materials. In fact, big efforts are carried out in order to find appropriate metamaterials as substitutes of the 3D counterparts so as to decrease the cost of device manufacturing that makes them unfitting with research activity. As a novel electrode material, nanoporous metals possess unique properties, such as a low fabrication cost, high plasmonic enhancement and large surface-volume ratio[8]. Nanoporous gold behaves like a metamaterial whose effective dielectric response can be tuned accordingly to the wanted use. These properties make the material suitable for multiple biosensing application, from a high-performance and reliable SERS (surface enhanced raman scattering) substrate [9] to an electrode in CMOS MEAs capable of intracellular recordings[10]. All these properties were explored in the last years, but it could be interesting to further study if the characteristics of this material could make it a good photoelectrical modulating material for eliciting electrogenic cells firing activity. In this way, this technology could be in principle easily implemented on commercial CMOS devices, consenting stimulation and recording at single cell level with high-resolution sensors, opening the way to new methodologies for studying electrogenic cells and tissues. Electrical stimulation of excitable cells is the basis for many implantable devices in cardiac treatment and in neurological studies for treating debilitating neurological syndromes. In order to make the technique less invasive, optical stimulation was widely investigated [11]. The non-genetic photostimulation is starting to make its way in the field since it allows to avoid changing the biological framework by using transient thermal or electrochemical outputs from synthetic materials attached to the target cells[12]. If stimulated with impinging light these materials could inject free charges into the solution resulting in an ionic current at the interface able to eliciting of neurons[13] or cardiomyocyte action potentials. Plasmonic porous materials have all the suitable properties to be considered as an appealing tools for charge injection and consequently for stimulation of electrically active cells [14]. Thus, the second part of this dissertation will exploit the capabilities of these plasmonic metamaterials, placing particular emphasis on the possibility of photoelectrochemical modulation. In particular, in the fifth and last chapter I will describe all the properties and application of the porous material and the mechanism of photoemission. In the experimental paragraphs, the free charge photoemission properties of porous gold will be explored together with plasmonic non-genetic photostimulation of the cardiac cells on commercial CMOS MEAs