Browsing by Author "Henriquez, Craig S"
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Item Open Access A Discrete Monolayer Cardiac Tissue Model for Tissue Preparation Specific Modeling(2010) Kim, JongmyeongEngineered monolayers created by using microabrasion and micropatterning methods have provided a simplified in vitro system to study the effects of anisotropy and fiber direction on electrical propagation. Interpreting the behavior in these culture systems has often been performed using classical computer models with continuous properties. Such models, however, do not account for the effects of random cell shapes, cell orientations and cleft spaces inherent in these monolayers on the resulting wavefront conduction. Additionally when the continuous computer model is built to study impulse propagations, the intracellular conductivities of the model are commonly assigned to match impulse conduction velocity of the model to the experimental measurement. However this method can result in inaccurate intracellular conductivities considering the relationship among the conduction velocity, intracellular conductivities and ion channel properties. In this study, we present novel methods for modeling a monolayer cardiac tissue and for estimating intracellular conductivities from an optical mapping. First, in the proposed method for modeling a monolayer of cardiac tissue, the factors governing cell shape, cell-to-cell coupling and the degree of cleft space are not constant but rather are treated as spatially random with assigned distributions. This approach makes it possible to simulate wavefront propagation in a manner analogous to performing experiments on engineered monolayer tissues. Simulated results are compared to reported experimental data measured from monolayers used to investigate the role of cellular architecture on conduction velocities and anisotropy ratios. We also present an estimate for obtaining the electrical properties from these networks and demonstrate how variations in the discrete cellular architecture affect the macroscopic conductivities. The simulation results agree with the common assumption that under normal ranges of coupling strengths, tissues whose cell shapes and connectivity show relatively uniform distributions can be represented using continuous models with conductivities derived from random discrete cellular architecture using either estimates. The results also reveal that in the presence of abrupt changes in cell orientation, local estimates of tissue properties predict smoother changes in conductivities that may not adequately predict the discrete nature of propagation at the transition sites. Second, a novel approach is proposed to estimate intracellular conductivities from the optical mapping of the monolayer cardiac tissue under subthreshold stimulus. This method uses a simplified membrane model, which represents the membrane as a second order polynomial of the membrane potential. The simplified membrane model and the intracellular conductivities are estimated from the optical mapping of the monolayer tissue under the subthreshold stimulus. We showed that the proposed method provides more accurate intracellular conductivities compared to a method using a constant membrane resistance.
Item Open Access An information-theoretic analysis of spike processing in a neuroprosthetic model(2007-05-03T18:53:57Z) Won, Deborah S.Neural prostheses are being developed to provide motor capabilities to patients who suffer from motor-debilitating diseases and conditions. These brain-computer interfaces (BCI) will be controlled by activity from the brain and bypass damaged parts of the spinal cord or peripheral nervous system to re-establish volitional control of motor output. Spike sorting is a technologically expensive component of the signal processing chain required to interpret population spike activity acquired in a BCI. No systematic analysis of the need for spike sorting has been carried out and little is known about the effects of spike sorting error on the ability of a BCI to decode intended motor commands. We developed a theoretical framework and a modelling environment to examine the effects of spike processing on the information available to a BCI decoder. Shannon information theory was applied to simulated neural data. Results demonstrated that reported amounts of spike sorting error reduce mutual information (MI) significantly in single-unit spike trains. These results prompted investigation into how much information is available in a cluster of pooled signals. Indirect information analysis revealed the conditions under which pooled multi-unit signals can maintain the MI that is available in the corresponding sorted signals and how the information loss grows with dissimilarity of MI among the pooled responses. To reveal the differences in non-sorted spike activity within the context of a BCI, we simulated responses of 4 neurons with the commonly observed and exploited cosine-tuning property and with varying levels of sorting error. Tolerances of angular tuning differences and spike sorting error were given for MI loss due to pooling under various conditions, such as cases of inter- and/or intra-electrode differences and combinations of various mean firing rates and tuning depths. These analyses revealed the degree to which mutual information loss due to pooling spike activity depended upon differences in tuning between pooled neurons and the amount of spike error introduced by sorting. The theoretical framework and computational tools presented in this dissertation will BCI system designers to make decisions with an understanding of the tradeoffs between a system with and without spike sorting.Item Open Access Analysis of a High-resolution technique for Estimation of Conduction Velocity Vectors for Closely Spaced Electrodes Using a 2D Cardiac Tissue Model.(2021) Akino, LydiaBecause of the 1KHz sampling rates, standard clinical systems face a challenge of resolving local activation time (LAT) differences (less than 1ms) for closely spaced electrodes. Recently, Gaeta et al designed a novel technique that calculates LAT differences by transformation of bipolar electrogram (EGM) amplitude known as the DELTA (Determination of EGM Latencies by Transformation of Amplitude) method with a resolution less than 1 millisecond. This thesis evaluates the conduction velocity (CV) vectors measured using the DELTA method against CV vectors obtained using the activation times identified from the underlying transmembrane voltage (VMAT) at high temporal resolution and from the unipolar electrograms (UAT) at clinical temporal resolution. A 2D model of both normal and fibrotic cardiac tissue was developed to compute simulated extracellular EGMs and transmembrane potentials. LAT differences were estimated using the DELTA, UAT and VMAT methods for different stimulation sites. The triangulation technique was used to convert these LAT differences to vector maps and the magnitude and angle generated by both methods were compared. From the analysis, it was determined that the DELTA method gave estimates of the conduction velocity vectors with closely spaced electrodes (1mm,1.4mm and 2mm) that were nearly as accurate as the high resolution VMAT method (error less than 5%) in both normal and low-level fibrotic tissue (below level 3). For more complex wavefronts, defined here as wavefronts generated by using more than one source of simulation, the recorded errors with DELTA were slightly higher (5-10%) and followed no specific trend. In all cases, the DELTA method was more accurate in estimating CV magnitude and angle than the standard UAT method using electrodes separated by 2mm or less.
Item Open Access Augmentations vs. Restoration: A computational study of the effects of bacterial sodium channels on cardiac conduction.(2022) Needs, Daniel AllenCardiac arrhythmias, including ventricular tachycardia, ventricular fibrillation, and atrial fibrillation, are associated with ectopic triggers such as those resulting from afterdepolarizations and structural changes within the cardiac changes. While ectopic triggers can be dealt with via radio frequency ablation, structural causes of arrhythmia, such as microscale source-load mismatches, do not have available treatments. Augmentation of cardiomyocytes with exogenous sodium channels such as Nav1.4 or prokaryotic voltage-gated sodium channels, or BacNavs, have shown promise for potentially alleviating these arrhythmias. However, due to size constraints, only the BacNavs are available for the highest efficiency viral vectors for stable transduction. Limitations in the ability to test these channels in adult mammalian cardiac tissue, particularly tissue with source-load mismatches, have led to a lack of understanding about BacNav’s therapeutic value. This dissertation aims to build models of engineered BacNavs and compare their impact in simulated diseased and healthy cardiac tissue with increases of the endogenous Nav1.5 current to probe mechanisms for therapy.Patch clamp data was analyzed to derive steady-state values and kinetics for the activation and inactivation gating of the BacNavs using techniques dating back to Hodgkin and Huxley’s squid axon model. Models using a cubic activation function and only a single slow inactivation channel were best able to replicate the data, including action potential traces and restitution curves for both action potential duration and conduction velocity. The single slow inactivation channel matches what has been observed in crystallography studies of other BacNav channels. Including the derived BacNav model into membrane models for guinea pig and human ventricular myocytes revealed general trends of action potential duration reduction, action potential amplitude increase, and increases in conduction velocity and upstroke velocity. The action potential duration and amplitude trends were more significant for BacNav than Nav1.5, but the endogenous channel was superior for conduction velocity increase. These effects existed despite different responses in relative and absolute current densities between the two membrane models. Despite evidence that late sodium current can lead to afterdepolarizations, BacNav did not increase susceptibility to them in vulnerable midmyocardial cells except at extremely high current densities. Finally, reductions in action potential duration removed alternans present in the restitution curves for single cells. To study how BacNav affected arrhythmias, BacNav was incorporated into one-dimensional cables and two-dimensional tissues with source-load mismatches present, premature stimuli that could induce unidirectional block or channelopathies such as mutations leading to Brugada syndrome. BacNavs outperformed the endogenous channel in source-load mismatches due to increased action potential amplitude and slower inactivation kinetics. These conclusions were stable to spatial heterogeneity in the treatment. It was also able to rescue Brugada syndrome in a dose-dependent manner and narrow the vulnerable window to unidirectional block for one-dimensional cables. In two dimensions, Nav1.5 had a smaller window to spiral wave induction but experienced wave breaks and multiple wavelets, whereas rotors with BacNav-treated cells were stable. These findings help generate hypotheses to be tested experimentally and further refine the model. Further studies may uncover engineering principles for designing optimal sodium channels for specific pathologies.
Item Open Access Bridging Scales: How Microstructural Features Impact Macroscopic Cardiac Propagation(2018) Gokhale, Tanmay AnilCardiac arrhythmias such as atrial fibrillation and ventricular tachycardia are closely associated with microscopic fibrotic changes in cardiac structure that result in a heterogeneous myocardium. While the incidence of fibrosis is correlated with arrhythmia burden and recurrence, the mechanisms linking the two remain poorly understood. Previous experimental and simulation studies have identified changes in local conduction due to micron-scale structural heterogeneities. However, because of the limited ability to simultaneously study conduction over a range of spatial scales, it remains unclear how numerous microheterogeneities act in aggregate to alter conduction on the macroscopic scale. The overall objective of this dissertation is to elucidate and characterize the effect of microfibrosis on cardiac conduction, through the use of computational models and directly paired experimental studies.
The impact of fibrotic collagen deposition on reentrant conduction was first examined in a model of cardiac tissue. The presence of collagenous septa was shown to prolong the cycle length of reentry; the magnitude of reentry prolongation is correlated with the overall degree of fibrosis and the length of individual collagenous septa. Mechanistically, cycle length prolongation is caused by lengthening of the reentrant tip trajectory and alteration of restitution properties. An equivalent homogenized model of fibrosis is unable to recapitulate the observed cycle length prolongation, suggesting that the details of the microstructure greatly impact the observed macroscale behavior. A hybrid model generated by adding discrete heterogeneities to the coarse, homogenized model is able to partially reproduce cycle length prolongation by replicating the lengthened tip trajectory.
In order to examine the mechanisms by which cardiac microstructure influences global conduction, a new framework for paired computational and experimental studies using the engineered-excitable Ex293 cell line was developed. The Ex293 mathematical model incorporates several measures of variation in cellular and tissue electrophysiological properties, and is novel in its use of stochastic variation in a multidimensional model of tissue. Replicating the range of experimentally observed single-cell and macro-scale behavior requires introducing ionic conductance variation between individual cells and between tissues, as well as conductivity variation between tissues.
This framework was then utilized for paired studies in a geometry of idealized fibrosis to examine fibrosis-induced changes in micro- and macro-scale behavior. The presence of microscopic heterogeneities slows conduction and alters the curvature of the macroscopic wavefront. On the microscale, branching of tissue around heterogeneities leads to conduction slowing due to imbalances of electrical source and load, while wavefront collisions at sites of tissue convergence lead to acceleration of propagation. The observed macroscopic behavior is directly attributable to the combination of these microscopic effects and the tortuosity of propagation around heterogeneities. Under diseased conditions involving reduced excitability, alteration of these microscale behaviors leads to reversal of changes in wavefront curvature.
These findings advance our knowledge of the role of myocardial micro-heterogeneities in conduction. Further application of these techniques to examine how the effects of microstructure are dynamically modulated may help improve our understanding of the factors giving rise to cardiac arrhythmia.
Item Open Access Dorsal Column Stimulation for Therapy, Artificial Somatosensation and Cortico-Spinal Communication(2015) Yadav, Amol PrakashThe spinal cord is an information highway continuously transmitting afferent and efferent signals to and from the brain. Although spinal cord stimulation has been used for the treatment of chronic pain for decades, its potential has not been fully explored. Spinal cord stimulation has never been used with the aim to transmit relevant information to the brain. Although, various locations along the sensory pathway have been explored for generating electrical stimulation induced sensory percepts, right from peripheral nerves, to thalamus to primary somatosensory cortex, the role of spinal cord has been largely neglected. In this dissertation, I have attempted to investigate if, electrical stimulation of dorsal columns of spinal cord called as Dorsal Column Stimulation (DCS) can be used as an effective technique to communicate therapeutic and somatosensory information to the brain.
To study the long term effects of DCS, I employed the 6-hydroxydopamine (6-OHDA) rodent model of Parkinson’s Disease (PD). Twice a week DCS for 30 minutes resulted in a dramatic recovery of weight and behavioral symptoms in rats treated with striatal infusions of 6-OHDA. The improvement in motor symptoms was accompanied by higher dopaminergic innervation in the striatum and increased cell count of dopaminergic neurons in the substantia nigra pars compacta (SNc). These results suggest that DCS has a chronic therapeutic and neuroprotective effect, increasing its potential as a new clinical option for treating PD patients. Thus, I was able to demonstrate the long-term efficacy of DCS, as a technique for therapeutic intervention.
Subsequently, I investigated if DCS can be used as a technique to transmit artificial somatosensory information to the cortex and trained rats to discriminate multiple artificial tactile sensations. Rats were able to successfully differentiate 4 different tactile percepts generated by varying temporal patterns of DCS. As the rats learnt the task, significant changes in the encoding of this artificial information were observed in multiple brain areas. Finally, I created a Brainet that interconnected two rats: an encoder and a decoder, whereby, cortical signals from the encoder rat were processed by a neural decoder while it performed a tactile discrimination task and transmitted to the spinal cord of the decoder using DCS. My study demonstrated for the first time, a cortico-spinal communication between different organisms.
My obtained results suggest that DCS, a semi-invasive technique, can be used in the future to send prosthetic somatosensory information to the brain or to enable a healthy brain to directly modulate neural activity in the nervous system of a patient, facilitating plasticity mechanism needed for efficient recovery.
Item Open Access In Vitro Calcium Imaging of Magnetogenetic Ion Channels TRPV1FeRIC and TRPV4FeRIC(2018) Gibbs, EricFerritin-based magnetogenetic ion channels are promising new tools for non-invasive manipulation of ion channel activity. The use of these channels in animals has been promising but in vitro experiments in cultured cells have been inconclusive. This report focuses on channels TRPV1FeRIC and TRPV4FeRIC whose channel activity is reportedly sensitive to an alternating magnetic field (AMF) at 175 MHz. In vitro work on these channels has previously been done, but those experiments did not have the necessary controls and had significant confounding factors. This dissertation addresses these problems and redesigns AMF calcium imaging experiments to more accurately measure an AMF response. After many experiments and careful analysis, it is concluded that 175 MHz AMF exposure does not change intracellular calcium concentration in HEK 293T cells expressing TRPV1FeRIC or TRPV4FeRIC.
Item Open Access Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications(2017) Zhang, XuSpiking neural networks have been used to investigate the mechanisms of processing
in biological neural circuits or to propose hypotheses that can be tested in exper-
iments. Because of their biological plausibility and event-based information trans-
mission, Spiking Neural Networks (SNNs) have suggested as alternatives to Articial
Neural Networks for pattern recognition, classication and function approximation
problems with fewer neurons. In machine learning, SNNs has been shown to be able
to solve pattern and robotic control. For SNNs to be used for such problems, they
must incorporate some mechanism for learning. Current methods to train SNNs use
learning algorithms which adjust the synaptic weights according to an update rule.
In most cases the weights are modied directly. In potential applications such as
driving plasticity in neural culture (in-vitro) and training neuromorphic chips the
directly manipulation of synaptic weights is not possible. Therefore, indirect algo-
rithms, which cause the SNNs to learn based on some biological learning mechanisms
using stimulation of neurons oer signicant advantages over the existing algorithm
for these real world applications.
Indirect algorithms train the neural network by using external stimuli to modulate
the synaptic strengths of a neural network according to synapses intrinsic mechanisms
for plasticity. The training algorithms have been demonstrated in both Integrate and
Fire neurons and more biologically realistic neural networks. In this thesis, four indi-
rect methods to drive the synaptic weights to its desired value in a network through Spike Time Dependent Plasticity (STDP) are developed: Indirect Perturbation, In-
direct Stochastic Gradient, Indirect ReSuMe, and Indirect Training with Supervised
Teaching Signals. These algorithms are used to solve the temporal and spatial input-
output mapping problem using temporal coding. The other type of problem is to
mapping input output ring rates using rate coding.
To test the algorithms, SNNs are used to control both virtual and real world
robots. For the real world robots with SNNs, known and Neurorobots, two types
of robot localization techniques are used: Optitrack, using ceiling mounted cameras
and onboard markers, and embedded cameras. Both small and large SNNs with
biologically realistic neurons are used to drive the neurorobots are modeled with
input coming from Optitrack or the cameras with GPU accelerated SNN simulator.
The results show that the indirect perturbation and indirect stochastic gradient
algorithms can train an SNN to control the robot to nd targets and avoid obstacles
even in the presence of sensor noise. The results also show that indirect training with
supervised training signals algorithm can train a feedforward network with 1000s of
neurons to process and output the correct movement commands to localize a target
using from real time images captured from an embedded camera. Finally. an indirect
version of the Remote Supervised Method (ReSuMe) algorithm was developed using
a more biologically realistic form of Spike-Timing Dependent Plasticity to produce a
specic temporal pattern of spiking from a group of neurons. The indirect algorithms
developed in this thesis may eventually allow the ability to train in vitro and in
vivo biological circuits to perform specic tasks using patterns of electrical or light
stimulation.
Item Open Access Modeling an Excitable Biosynthetic Tissue with Inherent Variability for Paired Computational-Experimental Studies.(PLoS Comput Biol, 2017-01) Gokhale, Tanmay A; Kim, Jong M; Kirkton, Robert D; Bursac, Nenad; Henriquez, Craig STo understand how excitable tissues give rise to arrhythmias, it is crucially necessary to understand the electrical dynamics of cells in the context of their environment. Multicellular monolayer cultures have proven useful for investigating arrhythmias and other conduction anomalies, and because of their relatively simple structure, these constructs lend themselves to paired computational studies that often help elucidate mechanisms of the observed behavior. However, tissue cultures of cardiomyocyte monolayers currently require the use of neonatal cells with ionic properties that change rapidly during development and have thus been poorly characterized and modeled to date. Recently, Kirkton and Bursac demonstrated the ability to create biosynthetic excitable tissues from genetically engineered and immortalized HEK293 cells with well-characterized electrical properties and the ability to propagate action potentials. In this study, we developed and validated a computational model of these excitable HEK293 cells (called "Ex293" cells) using existing electrophysiological data and a genetic search algorithm. In order to reproduce not only the mean but also the variability of experimental observations, we examined what sources of variation were required in the computational model. Random cell-to-cell and inter-monolayer variation in both ionic conductances and tissue conductivity was necessary to explain the experimentally observed variability in action potential shape and macroscopic conduction, and the spatial organization of cell-to-cell conductance variation was found to not impact macroscopic behavior; the resulting model accurately reproduces both normal and drug-modified conduction behavior. The development of a computational Ex293 cell and tissue model provides a novel framework to perform paired computational-experimental studies to study normal and abnormal conduction in multidimensional excitable tissue, and the methodology of modeling variation can be applied to models of any excitable cell.Item Open Access Neuromagnetic Fields and Brain-Inspired Hybrid Analog-Digital Computation(2018) Subramanian, Vivek AnandBrain-inspired computing architectures such as neural networks and neuromorphic chips have demonstrated promise in performing complex pattern recognition tasks by coarsely mimicking synaptic activity in software and hardware. In this dissertation, we take a departure from these more traditional methods which are confined by what we know about the dynamics of synaptic computation and introduce a brain-inspired hybrid analog-digital computing paradigm involving magnetic fields. We first review biomagnetic fields - a wide array of topics is covered to spark the interest of the reader in the field of neuro-biomagnetism and to provide a general overview of the field that explains (1) various techniques to measure, quantify, and model the magnetic signals generated by neurons; (2) how magnetic stimulation can affect neurons; and (3) the clinical relevance of these findings. These highlight the importance of magnetism in biology and neural signal processing and provide motivation for engineering magnetically-based computational devices. We then introduce a new hybrid analog-digital computing device inspired by the interplay between neural activity and its induced magnetic fields. We show that magnetic fields can interact nonlinearly in analog in a ferromagnetic medium. Specifically, the magnetic flux induced by two alternating magnetic fields can be employed to perform an absolute difference, or smooth XOR, operation. The physical structure of the analog device is based on a white matter tractography analysis; hence, we call it the neuromagnetic reactor. We also describe our design of a scalable implementation of a perceptron in hardware, which provides a digital 0-1 output. We demonstrate in a synthetic environment that these two systems together allow an organism to learn from and react appropriately to its environment. Although the design presented here is a proof-of-concept, it can be improved to yield not only new ways to study brain function but also new brain-inspired computing architectures based on magnetic fields.
Item Open Access Non-Linear Adaptive Bayesian Filtering for Brain Machine Interfaces(2010) Li, ZhengBrain-machine interfaces (BMI) are systems which connect brains directly to machines or computers for communication. BMI-controlled prosthetic devices use algorithms to decode neuronal recordings into movement commands. These algorithms operate using models of how recorded neuronal signals relate to desired movements, called models of tuning. Models of tuning have typically been linear in prior work, due to the simplicity and speed of the algorithms used with them. Neuronal tuning has been shown to slowly change over time, but most prior work do not adapt tuning models to these changes. Furthermore, extracellular electrical recordings of neurons' action potentials slowly change over time, impairing the preprocessing step of spike-sorting, during which the neurons responsible for recorded action potentials are identified.
This dissertation presents a non-linear adaptive Bayesian filter and an adaptive spike-sorting method for BMI decoding. The adaptive filter consists of the n-th order unscented Kalman filter and Bayesian regression self-training updates. The unscented Kalman filter estimates desired prosthetic movements using a non-linear model of tuning as its observation model. The model is quadratic with terms for position, velocity, distance from center of workspace, and velocity magnitude. The tuning model relates neuronal activity to movements at multiple time offsets simultaneously, and the movement model of the filter is an order n autoregressive model.
To adapt the tuning model parameters to changes in the brain, Bayesian regression self-training updates are performed periodically. Tuning model parameters are stored as probability distributions instead of point estimates. Bayesian regression uses the previous model parameters as priors and calculates the posteriors of the regression between filter outputs, which are assumed to be the desired movements, and neuronal recordings. Before each update, filter outputs are smoothed using a Kalman smoother, and tuning model parameters are passed through a transition model describing how parameters change over time. Two variants of Bayesian regression are presented: one uses a joint distribution for the model parameters which allows analytical inference, and the other uses a more flexible factorized distribution that requires approximate inference using variational Bayes.
To adapt spike-sorting parameters to changes in spike waveforms, variational Bayesian Gaussian mixture clustering updates are used to update the waveform clustering used to calculate these parameters. This Bayesian extension of expectation-maximization clustering uses the previous clustering parameters as priors and computes the new parameters as posteriors. The use of priors allows tracking of clustering parameters over time and facilitates fast convergence.
To evaluate the proposed methods, experiments were performed with 3 Rhesus monkeys implanted with micro-wire electrode arrays in arm-related areas of the cortex. Off-line reconstructions and on-line, closed-loop experiments with brain-control show that the n-th order unscented Kalman filter is more accurate than previous linear methods. Closed-loop experiments over 29 days show that Bayesian regression self-training helps maintain control accuracy. Experiments on synthetic data show that Bayesian regression self-training can be applied to other tracking problems with changing observation models. Bayesian clustering updates on synthetic and neuronal data demonstrate tracking of cluster and waveform changes. These results indicate the proposed methods improve the accuracy and robustness of BMIs for prosthetic devices, bringing BMI-controlled prosthetics closer to clinical use.
Item Open Access Non-uniform Interstitial Loading in Cardiac Microstructure During Impulse Propagation(2009) Roberts, Sarah F.Impulse propagation in cardiac muscle is determined not only by the excitable properties of the myocyte membrane, but also by the gross and fine structure of cardiac muscle. Ionic diffusion pathways are defined by the muscle's interconnected myocytes and interweaving interstitial spaces. Resistive variations arising from spatial changes in tissue structure, including geometry, composition and electrical properties have a significant impact on the success or failure of impulse propagation. Although much as been learned about the impact of discrete resistive architecture of the intracellular space, the role of the interstitial space in the spread of electrical activity is less well understood or appreciated at the microscopic scale.
The interstitial space, or interstitium, occupies from 20-25% of the total heart volume.
The structural and material composition of the interstitial space is both complex and
heterogeneous, encompassing non-myocyte cell structures and a conglomeration of
extracellular matrix proteins. The spatial distribution of the interstitium can vary from confined spaces between abutting myocytes and tightly packed cardiac fibers to large gaps between cardiac bundles and sheets
This work presents a discrete multidomain formulation that describes the three-dimensional ionic diffusion pathways between connected myocytes within a variable interstitial physiology and morphology. Unlike classically used continuous and discontinuous models of impulse propagation, the intracellular and extracellular spaces are represented as spatially distinct volumes with dynamic and static boundary conditions that electrically couple neighboring spaces to form the electrically cooperative tissue model. The discrete multidomain model provides a flexible platform to simulate impulse propagation at the microscopic scale within a three-dimensional context. The three-dimensional description of the interstitial space that
encompasses a single cell improves the capability of the model to realistically investigate the impact of the discontinuous and electrotonic inhomogeneities of the myocardium's interstitium.
Under the discrete multidomain representation, a non-uniformly described interstitium
capturing the passive properties of the intravascular space or variable distribution and
composition of the extracellular space that encompasses a cardiac fiber creates an
electrotonic load perpendicular to the direction of the propagating wavefront. During
longitudinal propagation along a cardiac fiber, results demonstrate waveshape
alterations due to variations in loads experienced radially that would have been otherwise masked in traditional model descriptions. Findings present a mechanism for eliminating myocyte membrane participation in impulse propagation, as the result of decreased loading experienced radially from a non-uniformly resistive extracellular space. Ultimately, conduction velocity increases by decreasing the "effective" surface-to-volume ratio, as theoretically hypothesized to occur in the conducting Purkinje tissue.
Item Open Access On the Significance of Stimulus Waveform in the Modulation of Oscillatory Activity in Excitable Tissues(2021) Eidum, Derek MitchellElectrical stimulation can influence the natural rhythms of activity in heart and brain tissue and has numerous applications in the treatment of cardiac and neurological conditions. The design and optimization of electrical stimulus treatments relies on the ability of researchers to predict the physiological responses of the target tissue to external stimulation. These responses vary greatly depending on the stimulus waveform and parameters as well as the state of ongoing activity in the target region, in ways that are not yet fully understood. The objective of this dissertation is to examine the theoretical basis for differential responses to rhythmic external stimulation based on the properties of the stimulus and target tissue and provide insights for future stimulus technique design.Synaptic plasticity plays a key role in neurostimulation as it allows for the effects of stimulus treatments to persist long after the stimulus ends. Rhythmic stimulation can entrain natural neural oscillations and produce persistent changes in the frequency content of neural activity. However, the mechanisms behind these changes are largely unknown. To this end, simple neural oscillator models were constructed in order to examine the role of synaptic plasticity and sinusoidal stimulation on the synchronization between oscillating regions. Sinusoidal stimulation of different frequencies and strengths can disrupt the intrinsic patterns of network activity, causing information to propagate through the network via different synaptic paths. These new pathways are reinforced through spike timing dependent plasticity, fundamentally altering the network behavior post-stimulation. The resulting network activity depends on the stimulus strength and frequency as well as the intrinsic frequencies of the neural oscillators and the strength of inter-oscillator coupling. Additionally, the effects of rhythmic stimulation depend on the spatial properties of the applied stimulus. By applying out-of-phase sinusoidal current to transverse pairs of electrodes, electric fields may be generated which maintain an approximately fixed strength but rotate in space. Rotational fields may provide utility in the modulation of spiral wave dynamics in excitable tissues, which are associated with reentrant cycles in cardiac arrythmias as well as a number of processes within the brain. To explore this, spiral waves were generated in computational models of engineered excitable tissue and were subjected to rotating and sinusoidal electric fields of varying strength and frequency. Rotational fields which match the direction of spiral propagation provide significant efficiency gains in entraining spiral frequency when compared to sinusoidal stimulus, while retrograde rotational fields can reverse the direction of spiral propagation. Even in the absence of spiral wave dynamics, rotational field stimulation may provide utility in the modulation of neural oscillations. The response of a neuron external stimulation depends on its orientation relative to the electric field gradient, which gives rise to orientation-dependent responses to stimulus treatments. Rotational fields may therefore improve neurostimulus efficacy by influencing the excitability of neurons regardless of their orientation. To explore how rotating fields influence neural oscillations, two neural network model architectures were utilized: large-scale bursting networks, and networks of linked idealized oscillators with plastic inter-oscillator connections. Networks were subjected to rotational and sinusoidal fields, and their behaviors were measured as a function of stimulus strength, frequency, and orientation, as well as the degree of axonal alignment within the network. In spatially aligned networks, rotational fields entrain oscillations and promote network synchrony regardless of orientation, whereas the effects of sinusoidal fields exhibit strong orientation-dependence. In spatially disordered networks, however, rotational fields promote activity in different neurons at different stimulus phases, resulting in reduced network synchrony. These findings expand our knowledge on the significance of stimulus waveform in the modulation of electrically excitable tissues. The ability to understand and predict physiological responses to stimulation will open new doors in the design and optimization of stimulus techniques to achieve desired outcomes.
Item Open Access Probing Tissue Microstructure Using Susceptibility Contrast Magnetic Resonance Imaging(2016) Dibb, RussellMagnetic resonance imaging is a research and clinical tool that has been applied in a wide variety of sciences. One area of magnetic resonance imaging that has exhibited terrific promise and growth in the past decade is magnetic susceptibility imaging. Imaging tissue susceptibility provides insight into the microstructural organization and chemical properties of biological tissues, but this image contrast is not well understood. The purpose of this work is to develop effective approaches to image, assess, and model the mechanisms that generate both isotropic and anisotropic magnetic susceptibility contrast in biological tissues, including myocardium and central nervous system white matter.
This document contains the first report of MRI-measured susceptibility anisotropy in myocardium. Intact mouse heart specimens were scanned using MRI at 9.4 T to ascertain both the magnetic susceptibility and myofiber orientation of the tissue. The susceptibility anisotropy of myocardium was observed and measured by relating the apparent tissue susceptibility as a function of the myofiber angle with respect to the applied magnetic field. A multi-filament model of myocardial tissue revealed that the diamagnetically anisotropy α-helix peptide bonds in myofilament proteins are capable of producing bulk susceptibility anisotropy on a scale measurable by MRI, and are potentially the chief sources of the experimentally observed anisotropy.
The growing use of paramagnetic contrast agents in magnetic susceptibility imaging motivated a series of investigations regarding the effect of these exogenous agents on susceptibility imaging in the brain, heart, and kidney. In each of these organs, gadolinium increases susceptibility contrast and anisotropy, though the enhancements depend on the tissue type, compartmentalization of contrast agent, and complex multi-pool relaxation. In the brain, the introduction of paramagnetic contrast agents actually makes white matter tissue regions appear more diamagnetic relative to the reference susceptibility. Gadolinium-enhanced MRI yields tensor-valued susceptibility images with eigenvectors that more accurately reflect the underlying tissue orientation.
Despite the boost gadolinium provides, tensor-valued susceptibility image reconstruction is prone to image artifacts. A novel algorithm was developed to mitigate these artifacts by incorporating orientation-dependent tissue relaxation information into susceptibility tensor estimation. The technique was verified using a numerical phantom simulation, and improves susceptibility-based tractography in the brain, kidney, and heart. This work represents the first successful application of susceptibility-based tractography to a whole, intact heart.
The knowledge and tools developed throughout the course of this research were then applied to studying mouse models of Alzheimer’s disease in vivo, and studying hypertrophic human myocardium specimens ex vivo. Though a preliminary study using contrast-enhanced quantitative susceptibility mapping has revealed diamagnetic amyloid plaques associated with Alzheimer’s disease in the mouse brain ex vivo, non-contrast susceptibility imaging was unable to precisely identify these plaques in vivo. Susceptibility tensor imaging of human myocardium specimens at 9.4 T shows that susceptibility anisotropy is larger and mean susceptibility is more diamagnetic in hypertrophic tissue than in normal tissue. These findings support the hypothesis that myofilament proteins are a source of susceptibility contrast and anisotropy in myocardium. This collection of preclinical studies provides new tools and context for analyzing tissue structure, chemistry, and health in a variety of organs throughout the body.
Item Open Access Simultaneous Multiplexing of Movement Execution, Observation, and Reward in Cortical Motor Neurons(2021) Byun, Yoon WooNeural activities of the motor cortices have been traditionally known to represent motor information such as velocity of the movement and muscle force. Recent studies show that motor cortices, including primary motor cortex (M1), also represent non-traditional information such as observed movements of others and reward-related signal. However, how the neurons simultaneously multiplex such non-traditional information along with traditional motor parameters and whether the multiplexing leads to significant interactions are not well understood. Furthermore, understanding how the non-traditional information are encoded and they interact with motor information may help the development of more error-resistant, autonomous brain-to-machine interface and the understanding of underlying mechanism behind joint action and motor skill learning. In this dissertation, we investigate in detail how the observed movements and reward are simultaneously multiplexed along with traditional motor information and how each pair of neural representations interact with each other. First, regarding movement observation, we show that significant fraction of M1 neurons simultaneously encode the presence and direction of the movement of others along with those of self-movements. Neurons respond differently to joint action than to self-movements and show an interaction effect from the two representations of observed and executed movements rather than simple averaging of the two. Some neurons that separately encode observed and executed movements turn to suppress the representation of observed movements in joint action. In simultaneous actions, the representation of self-executed movement gets weaker, which suggests an interaction between two information and may possibly lead to behavioral interference. Preferred directions also change to be decoupled for noncongruent joint actions as to allow simultaneous multiplexing of both information with phase difference, while being synced for congruent ones. Conditional probabilities from the distribution of encoding neurons suggest a shared circuitry for movement observation, execution, and simultaneous actions. Shared circuitry with interactions between representations may explain why people can perform movements freely while watching others move; yet if the interaction between the two goes up due to simultaneous occurrence, it may result in interferences in behavior. Second, regarding the multiplexing of reward-related signal with movement signals, we show that both signals are multiplexed in individual and population neurons in M1 and S1. The activity of neural population in M1 and S1 distinguished whether the reward timing before the delivery of the reward. Furthermore, reward per se, reward anticipation, and reward prediction error (RPE) were encoded along with the motor information. The encoding of the reward-related signal interacted with the motor information in that the preferred direction changed when the reward was omitted. Change of spatial tuning of neurons due to reward prediction error signifies that there is interaction between the neural representation of reward and motor information, which may impact and underlie motor skill learning. In conclusion, both observed movements and reward are simultaneously multiplexed with traditional motor information. Co-representation of the two non-traditional information then leads to interaction between them and the motor information. Such interaction suggest that such simultaneous multiplexing may lead to behavioral interferences and motor skill learning.
Item Open Access Study of Lorentz Effect Imaging and Neuronal Current MRI Using Electromagnetohydrodynamic Models(2013) Pourtaheri, NavidNeuronal current MRI (ncMRI) is a field of study to directly map electrical activity in the brain using MRI, which has many benefits over functional MRI. One potential ncMRI method, Lorentz effect imaging (LEI), has shown promise but needs a better theoretical understanding to improve its use.
We develop three computational models to simulate the LEI experiments of an electrolyte filled phantom subject to a current dipole based on: ion flow, particle drift, and electromagnetohydrodynamics (EMHD). With comparative experimental results, we use the EMHD model to better understand the Lorentz effect over a range of current strengths. We also quantify the LEI experimental images and assess ways to measure the underlying current strength, which would greatly benefit comparative brain mapping.
EMHD is a good predictor of LEI signal loss. We can measure the underlying current strength and polarity in the phantom using LEI images. We can also use trends from the EMHD model results to predict the required current density for signal detection in future LEI experiments. We can also infer the electric field strength, flow velocity, displacement, and pressure from the predicted current magnitude in an LEI experiment.
The EMHD model provides information that greatly improves the utility and understanding of LEI. Future study with our EMHD model should be performed using shorter dipole lengths, higher density and lower strength of current sources, and varying current source frequencies to understand LEI in the setting of mapping brain activity.
Item Open Access The Effect of Structural Microheterogeneity on the Initiation and Propagation of Ectopic Activity in Cardiac Tissue(2010) Hubbard, Marjorie LetitiaCardiac arrhythmias triggered by both reentrant and focal sources are closely correlated with regions of tissue characterized by significant structural heterogeneity. Experimental and modeling studies of electrical activity in the heart have shown that local microscopic heterogeneities which average out at the macroscale in healthy tissue play a much more important role in diseased and aging cardiac tissue which have low levels of coupling and abnormal or reduced membrane excitability. However, it is still largely unknown how various combinations of microheterogeneity in the intracellular and interstitial spaces affect wavefront propagation in these critical regimes.
This thesis uses biophysically realistic 1-D and 2-D computer models to investigate how heterogeneity in the interstitial and intracellular spaces influence both the initiation of ectopic beats and the escape of multiple ectopic beats from a poorly coupled region of tissue into surrounding well-coupled tissue. An approximate discrete monodomain model that incorporates local heterogeneity in both the interstitial and intracellular spaces was developed to represent the tissue domain.
The results showed that increasing the effective interstitial resistivity in poorly coupled fibers alters the distribution of electrical load at the microscale and causes propagation to become more like that observed in continuous fibers. In poorly coupled domains, this nearly continuous state is modulated by cell length and is characterized by decreased gap junction delay, sustained conduction velocity, increased sodium current, reduced maximum upstroke velocity, and increased safety factor. In inhomogeneous fibers with adjacent well-coupled and poorly coupled regions, locally increasing the effective interstitial resistivity in the poorly coupled region reduces the size of the focal source needed to generate an ectopic beat, reduces dispersion of repolarization, and delays the onset of conduction block that is caused by source-load mismatch at the boundary between well-coupled and poorly-coupled regions. In 2-D tissue models, local increases in effective interstitial resistivity as well as microstructural variations in cell arrangement at the boundary between poorly coupled and well-coupled regions of tissue modulate the distribution of maximum sodium current which facilitates the unidirectional escape of focal beats. Variations in the distribution of sodium current as a function of cell length and width lead to directional differences in the response to increased effective interstitial resistivity. Propagation in critical regimes such as the ectopic substrate is very sensitive to source-load interactions and local increases in maximum sodium current caused by microheterogeneity in both intracellular and interstitial structure.