A Mechanistically Guided Approach to Treatment of Multi-Wavelet Reentry: Experiments in a Computational Model of Cardiac Propagation

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the United States today. However, treatment options remain limited despite the enormous magnitude of both AF prevalence and the associated economic cost. Of those treatment options that are available, ablation-based interventional methods have demonstrated the highest rates of long-term cure. Unfortunately, these methods have substantially lower efficacy in patients with heavier burdens of disease, thus leaving the most affected individuals with the least hope for successful treatment. The focus of this research is to develop a mechanistically guided approach towards the treatment of multi-wavelet reentry (MWR), one of the primary drivers of AF. For this purpose, we use a computational model of electrical propagation in cardiac tissue to simulate both episodes of fibrillatory activity and the ablative treatment thereof. We demonstrate that the probability of forming the reentrant circuits necessary for continuous electrical activity is a function of the shape and size of a tissue as well as its underlying cellular properties. Ablation at tissue sites with high probability of circuit formation more efficiently reduces the overall duration of fibrillatory episodes than ablation at sites with low probability. We then propose and validate in silico a parameter-based metric for predicting the propensity of an individual tissue to support fibrillation, which we term the fibrillogenicity index. Using this metric, we develop an algorithm for prospectively determining optimized, tissue-specific ablation patterns. Finally, we examine the relationship between multi-wavelet reentry and focal drivers, and demonstrate that MWR and fibrillatory conduction exist along a continuum. We examine the complex interplay between functional and structural substrates within fibrillating tissue and define the mechanisms by which they promote the perpetuation of AF. These findings present a novel theoretical framework for understanding treatment of multi-wavelet reentry driven AF and provide a set of testable predictions that can serve to guide the design of future experimental studies aimed at advancing the rational design of patient-specific ablation sets for treating AF

Cardiac Arrhythmias

This book is useful for physicians taking care of patients with cardiac arrhythmias and includes six chapters written by experts in their field. Chapter 1 discusses basic mechanisms of cardiac arrhythmias. Chapter 2 discusses the chronobiological aspects of the impact of apnoic episodes on ventricular arrhythmias. Chapter 3 discusses navigation, detection, and tracking during cardiac ablation interventions. Chapter 4 discusses epidemiology and pathophysiology of ventricular arrhythmias in several noncardiac diseases, methods used to assess arrhythmia risk, and their association with long-term outcomes. Chapter 5 discusses the treatment of ventricular arrhythmias including indications for implantation of an AICD for primary and for secondary prevention in patients with and without congestive heart failure. Chapter 6 discusses surgical management of atrial fibrillation

Multi-Scale Mathematical Modelling of Brain Networks in Alzheimer's Disease

Perturbations to brain network dynamics on a range of spatial and temporal scales are believed to underpin neurological disorders such as Alzheimer’s disease (AD). This thesis combines quantitative data analysis with tools such as dynamical systems and graph theory to understand how the network dynamics of the brain are altered in AD and experimental models of related pathologies. Firstly, we use a biophysical neuron model to elucidate ionic mechanisms underpinning alterations to the dynamics of principal neurons in the brain’s spatial navigation systems in an animal model of tauopathy. To uncover how synaptic deficits result in alterations to brain dynamics, we subsequently study an animal model featuring local and long-range synaptic degeneration. Synchronous activity (functional connectivity; FC) between neurons within a region of the cortex is analysed using two-photon calcium imaging data. Long-range FC between regions of the brain is analysed using EEG data. Furthermore, a computational model is used to study relationships between networks on these different spatial scales. The latter half of this thesis studies EEG to characterize alterations to macro-scale brain dynamics in clinical AD. Spectral and FC measures are correlated with cognitive test scores to study the hypothesis that impaired integration of the brain’s processing systems underpin cognitive impairment in AD. Whole brain computational modelling is used to gain insight into the role of spectral slowing on FC, and elucidate potential synaptic mechanisms of FC differences in AD. On a finer temporal scale, microstate analyses are used to identify changes to the rapid transitioning behaviour of the brain’s resting state in AD. Finally, the electrophysiological signatures of AD identified throughout the thesis are combined into a predictive model which can accurately separate people with AD and healthy controls based on their EEG, results which are validated on an independent patient cohort. Furthermore, we demonstrate in a small preliminary cohort that this model is a promising tool for predicting future conversion to AD in patients with mild cognitive impairment

The electrophysiological effects of Endothelin-1 in human atrial myocytes

Introduction: Chronic heart failure (CHF) is associated with an increased incidence of atrial fibrillation (AF) and elevated levels of catecholamines and endothelin-1 (ET-1), each of which affects the atrial L-type calcium current (ICaL) and consequently action potentials. Hypotheses: ET-1 modulates the effects of isoproterenol (ISO) on ICaL and action potentials in human atrial myocytes. Methods: Atrial myocytes were isolated enzymatically from samples of right atrial appendage obtained from consenting patients in sinus rhythm undergoing cardiac surgery. The nystatin-perforated whole cell patch clamp technique was used at 37ºC to record ICaL and action potentials in voltage-clamp and current-clamp mode respectively. Results: The current-voltage relationship of ICaL was bell-shaped, peaking at +10 mV with a current density of -4.8±0.4 pA/pF (mean± s.e.m., n=89 cells, 34 patients). ISO, 0.1 nM to 1 µM, increased peak ICaL in a concentration-dependent manner (n=4-46 cells) with a maximum response of 250± 53% above control and an approximate EC50 of 0.06 µM. Isoproterenol at 0.05 µM significantly increased peak ICaL from -4.7± 0.4 to -12.2± 0.9 pA/pF (P<0.05, Students t-test; n=64 cells). This adrenergic effect was reversed by ET-1 at all concentrations tested from 0.01 to 10 nM and was partially reversible upon ET-1 washout and in the presence of the specific ET-A receptor antagonist, FR139317 (n=5-12 cells). Neither ET-1 alone nor the ET-B receptor agonist Sarafotoxin S6c, at 10 nM, had an effect on ICaL. Isoproterenol (0.05 µM) prolonged the action potential duration at 50% repolarisation (APD50) from 30± 7 to 46± 7 ms (P< 0.05, n=15 cells), but had no effect on APD90 nor the cellular ERP. These adrenergic effects on APD50 and SDs were also abolished by ET-1 at 10 nM (P< 0.05, n=15 cells). Superfusion with ET-1 (10 nM) alone had no significant effect on APD50, APD90, nor ERP (n=21 cells). There were no significant interactions between these electrophysiological effects and diseases states or chronic pre-operative drug therapy. Spontaneous activity, defined as a depolarisation occurring during phase 3 of action potential repolarisation or a depolarisation of greater than 3 mV amplitude during phase 4, frequently interrupted action potential recordings during, but not prior to, superfusion with ISO. Using a repetitive stimulation protocol, ISO at 0.05 µM produced spontaneous depolarisations in 5 of 7 cells studied (P< 0.05, chi-2 test). Endothelin-1 at 10 nM abolished these depolarisations in all 5 cells (P< 0.05). Superfusion with ET-1 (10 nM) alone was associated with spontaneous depolarisations in significantly fewer cells (P< 0.05, n=2 of 13 cells). In a retrospective univariate analysis, patient comorbidity and pre-operative drug therapy were not found to influence the electrophysiological effects observed. Conclusions: ET-1 reversed adrenergically induced increases in peak ICaL, APD50 and SDs in human atrial myocytes. This anti-adrenergic effect may be expected to influence the occurrence of AF in patients irrespective of comorbidity or pre-operative drug therapy

Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation

Clinical decision support systems (clinical DSSs) are widely used today for various clinical applications such as diagnosis, treatment, and recovery. Clinical DSS aims to enhance the end‐to‐end therapy management for the doctors, and also helps to provide improved experience for patients during each phase of the therapy. The goal of this chapter is to provide an insight into the clinical DSS associated with the highly prevalent heart rhythm disorder, atrial fibrillation (AF). The use of clinical DSS in AF management is ubiquitous, starting from detection of AF through sophisticated electrophysiology treatment procedures, all the way to monitoring the patient\u27s health during follow‐ups. Most of the software associated with AF DSS are developed based on signal processing, image processing, and artificial intelligence techniques. The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. The chapter continues with a background on AF and some relevant mechanisms. Finally, a couple of clinical DSS used today in regard with AF are discussed, along with some proposed methods for potential implementation of clinical DSS for detection of AF, prediction of an AF treatment outcome, and localization of AF targets during a treatment procedure

Doctor of Philosophy

dissertationFibrillation is defined as turbulent cardiac electrical activity and results in the inability of the myocardium to contract. When fibrillation occurs in the ventricles, it is known as ventricular fibrillation (VF). The consequence of VF is sudden death unless treated immediately. Fibrillation can also occur in the atria and is known as atrial fibrillation (AF). The consequences of atrial fibrillation (AF) are less immediate; however, it leads to increased risk of stroke. Despite the impact of fibrillatory arrhythmias, there are many gaps in our mechanistic knowledge of these arrhythmias. The purpose of this dissertation is to study through several projects how different cardiac substrates help initiate and/or sustain fibrillation. The first project examined several properties of the ventricular conduction system during VF. The conduction system coordinates excitation and consequently coordinates the contraction of the ventricles. Despite the conduction system's unique structure, its role in VF remains unclear. We examined the proximal conduction system and found that it develops a more rapid activation rate than the ventricular myocardium during prolonged VF, and may be driving the arrhythmia. The second and third projects examined the effects of fibrosis on electrical conduction to initiate and/or sustain AF. Despite fibrosis being associated with AF, it is still unknown whether it is a byproduct of an underlying heart disease and does not in itself promote AF, or if it affects the organization of conduction during fibrillation to promote AF. In the second project we studied the effect of fibrosis on conduction following different types of triggers. We found that fibrosis causes transverse conduction slowing following premature stimulation, which makes AF more likely to initiate. As AF persists, single episodes of AF last longer before the patient transitions into normal sinus rhythm, and in some cases AF can become permanent. The third project examined why some patients may never transition from AF to normal sinus rhythm. Specifically, this project found that regions of dense fibrosis anchor high-frequency activation that may be driving the arrhythmia. These studies showed that fibrosis causes conduction changes that make AF more likely to initiate and to be sustained

Modelling the interaction between induced pluripotent stem cells derived cardiomyocytes patches and the recipient hearts

Cardiovascular diseases are the main cause of death worldwide. The single biggest killer is represented by ischemic heart disease. Myocardial infarction causes the formation of non-conductive and non-contractile, scar-like tissue in the heart, which can hamper the heart's physiological function and cause pathologies ranging from arrhythmias to heart failure. The heart can not recover the tissue lost due to myocardial infarction due to the myocardium's limited ability to regenerate. The only available treatment is heart transpalant, which is limited by the number of donors and can elicit an adverse response from the recipients immune system. Recently, regenerative medicine has been proposed as an alternative approach to help post-myocardial infarction hearts recover their functionality. Among the various techniques, the application of cardiac patches of engineered heart tissue in combination with electroactive materials constitutes a promising technology. However, many challenges need to be faced in the development of this treatment. One of the main concerns is represented by the immature phenotype of the stem cells-derived cardiomyocytes used to fabricate the engineered heart tissue. Their electrophysiological differences with respect to the host myocardium may contribute to an increased arrhythmia risk. A large number of animal experiments are needed to optimize the patches' characteristics and to better understand the implications of the electrical interaction between patches and host myocardium. In this Thesis we leveraged cardiac computational modelling to simulate \emph{in silico} electrical propagation in scarred heart tissue in the presence of a patch of engineered heart tissue and conductive polymer engrafted at the epicardium. This work is composed by two studies. In the first study we designed a tissue model with simplified geometry and used machine learning and global sensitivity analysis techniques to identify engineered heart tissue patch design variables that are important for restoring physiological electrophysiology in the host myocardium. Additionally, we showed how engineered heart tissue properties could be tuned to restore physiological activation while reducing arrhythmic risk. In the second study we moved to more realistic geometries and we devised a way to manipulate ventricle meshes obtained from magnetic resonance images to apply \emph{in silico} engineered heart tissue epicardial patches. We then investigated how patches with different conduction velocity and action potential duration influence the host ventricle electrophysiology. Specifically, we showed that appropriately located patches can reduce the predisposition to anatomical isthmus mediated re-entry and that patches with a physiological action potential duration and higher conduction velocity were most effective in reducing this risk. We also demonstrated that patches with conduction velocity and action potential duration typical of immature stem cells-derived cardiomyocytes were associated with the onset of sustained functional re-entry in an ischemic cardiomyopathy model with a large transmural scar. Finally, we demonstrated that patches electrically coupled to host myocardium reduce the likelihood of propagation of focal ectopic impulses. This Thesis demonstrates how computational modelling can be successfully applied to the field of regenerative medicine and constitutes the first step towards the creation of patient-specific models for developing and testing patches for cardiac regeneration.Open Acces

Linear and nonlinear approaches to unravel dynamics and connectivity in neuronal cultures

[eng] In the present thesis, we propose to explore neuronal circuits at the mesoscale, an approach in which one monitors small populations of few thousand neurons and concentrates in the emergence of collective behavior. In our case, we carried out such an exploration both experimentally and numerically, and by adopting an analysis perspective centered on time series analysis and dynamical systems. Experimentally, we used neuronal cultures and prepared more than 200 of them, which were monitored using fluorescence calcium imaging. By adjusting the experimental conditions, we could set two basic arrangements of neurons, namely homogeneous and aggregated. In the experiments, we carried out two major explorations, namely development and disintegration. In the former we investigated changes in network behavior as it matured; in the latter we applied a drug that reduced neuronal interconnectivity. All the subsequent analyses and modeling along the thesis are based on these experimental data. Numerically, the thesis comprised two aspects. The first one was oriented towards a simulation of neuronal connectivity and dynamics. The second one was oriented towards the development of linear and nonlinear analysis tools to unravel dynamic and connectivity aspects of the measured experimental networks. For the first aspect, we developed a sophisticated software package to simulate single neuronal dynamics using a quadratic integrate–and–fire model with adaptation and depression. This model was plug into a synthetic graph in which the nodes of the network are neurons, and the edges connections. The graph was created using spatial embedding and realistic biology. We carried out hundreds of simulations in which we tuned the density of neurons, their spatial arrangement and the characteristics of the fluorescence signal. As a key result, we observed that homogeneous networks required a substantial number of neurons to fire and exhibit collective dynamics, and that the presence of aggregation significantly reduced the number of required neurons. For the second aspect, data analysis, we analyzed experiments and simulations to tackle three major aspects: network dynamics reconstruction using linear descriptions, dynamics reconstruction using nonlinear descriptors, and the assessment of neuronal connectivity from solely activity data. For the linear study, we analyzed all experiments using the power spectrum density (PSD), and observed that it was sufficiently good to describe the development of the network or its disintegration. PSD also allowed us to distinguish between healthy and unhealthy networks, and revealed dynamical heterogeneities across the network. For the nonlinear study, we used techniques in the context of recurrence plots. We first characterized the embedding dimension m and the time delay δ for each experiment, built the respective recurrence plots, and extracted key information of the dynamics of the system through different descriptors. Experimental results were contrasted with numerical simulations. After analyzing about 400 time series, we concluded that the degree of dynamical complexity in neuronal cultures changes both during development and disintegration. We also observed that the healthier the culture, the higher its dynamic complexity. Finally, for the reconstruction study, we first used numerical simulations to determine the best measure of ‘statistical interdependence’ among any two neurons, and took Generalized Transfer Entropy. We then analyzed the experimental data. We concluded that young cultures have a weak connectivity that increases along maturation. Aggregation increases average connectivity, and more interesting, also the assortativity, i.e. the tendency of highly connected nodes to connect with other highly connected node. In turn, this assortativity may delineates important aspects of the dynamics of the network. Overall, the results show that spatial arrangement and neuronal dynamics are able to shape a very rich repertoire of dynamical states of varying complexity.[cat] L’habilitat dels teixits neuronals de processar i transmetre informació de forma eficient depèn de les propietats dinàmiques intrínseques de les neurones i de la connectivitat entre elles. La present tesi proposa explorar diferents tècniques experimentals i de simulació per analitzar la dinàmica i connectivitat de xarxes neuronals corticals de rata embrionària. Experimentalment, la gravació de l’activitat espontània d’una població de neurones en cultiu, mitjançant una càmera ràpida i tècniques de fluorescència, possibilita el seguiment de forma controlada de l’activitat individual de cada neurona, així com la modificació de la seva connectivitat. En conjunt, aquestes eines permeten estudiar el comportament col.lectiu emergent de la població neuronal. Amb l’objectiu de simular els patrons observats en el laboratori, hem implementat un model mètric aleatori de creixement neuronal per simular la xarxa física de connexions entre neurones, i un model quadràtic d’integració i dispar amb adaptació i depressió per modelar l’ampli espectre de dinàmiques neuronals amb un cost computacional reduït. Hem caracteritzat la dinàmica global i individual de les neurones i l’hem correlacionat amb la seva estructura subjacent mitjançant tècniques lineals i no–lineals de series temporals. L’anàlisi espectral ens ha possibilitat la descripció del desenvolupament i els canvis en connectivitat en els cultius, així com la diferenciació entre cultius sans dels patològics. La reconstrucció de la dinàmica subjacent mitjançant mètodes d’incrustació i l’ús de gràfics de recurrència ens ha permès detectar diferents transicions dinàmiques amb el corresponent guany o pèrdua de la complexitat i riquesa dinàmica del cultiu durant els diferents estudis experimentals. Finalment, a fi de reconstruir la connectivitat interna hem testejat, mitjançant simulacions, diferents quantificadors per mesurar la dependència estadística entre neurona i neurona, seleccionant finalment el mètode de transferència d’entropia gereralitzada. Seguidament, hem procedit a caracteritzar les xarxes amb diferents paràmetres. Malgrat presentar certs tres de xarxes tipus ‘petit món’, els nostres cultius mostren una distribució de grau ‘exponencial’ o ‘esbiaixada’ per, respectivament, cultius joves i madurs. Addicionalment, hem observat que les xarxes homogènies presenten la propietat de disassortativitat, mentre que xarxes amb un creixent nivell d’agregació espaial presenten assortativitat. Aquesta propietat impacta fortament en la transmissió, resistència i sincronització de la xarxa