The impact of coronary bypass surgery on myocardial perfusion and function

One hundred and fourteen patients were studied before and after coronary bypass surgery using radionuclide techniques to assess myocardial perfusion and function. At baseline and at follow up visits each patient was assessed by a clinician. Regional and global myocardial perfusion was evaluated at rest and at peak exercise using a total of 60MBq of thallium 201. Rest and exercise thallium scintigraphy was repeated 9 months after CABG (late follow up). In order to assess myocardial hibernation redistribution of rest images were acquired at baseline before the exercise images. Left and right ventricular ejection fractions (LVEF and RVEF) were measured by radionuclide ventriculography performed at baseline on a separate day from the thallium images. Regional ventricular wall motion was assessed on a continuous loop cine display of the 24 frames of the radionuclide ventriculogram with the aid of standard fourier amplitude and phase images. After the completion of the rest radionuclide ventriculogram dobutamine was administered at doses of 5-10mug/kg/min and the effect of the dobutamine on left ventricular regional wall motion was assessed. Radionuclide ventriculography was repeated 6 weeks (early follow up) and approximately 9 months (late follow up) after surgery. Coronary bypass surgery produced a marked improvement in symptoms and exercise tolerance. Before the operation 98% of the patients described symptoms of angina. After bypass surgery 71% were completely angina free (p<0.001). Exercise time using a standardised protocol increased from 296.2 + 87 seconds prior to surgery to 357 +/- 98 seconds after surgery (p<0.001). There was only minor variation in rest LVEF: LVEF at baseline was 32% (+10%). This rose to 34% (+/-11%) at the early follow up study (p=0.02). However by late follow up LVEF had;returned to 33% (p=n.s.d. for change from II baseline). In contrast there was a sharp fall in RVEF: RVEF was 33% (+8%) at baseline; by early follow up RVEF had fallen to 27% (+/-7%), p<0.001. There was no recovery in RVEF at late follow up, 26% (+/- 7%), p<0.001 for change from baseline. This fall in RVEF was not related to the preoperative LVEF, total bypass time, total cross clamp time, or grafting of the right coronary artery. The study confirmed major deterioration in septal function following coronary bypass surgery, previously detailed by other authors. There was also a minor deterioration in anterior and inferior regional function. However, posterolateral regional wall motion improved following CABG. Only 1 of the 25 (4%) septal territories that had impaired regional wall motion demonstrated improved function at early follow up. In comparison, 14 of the 20 (70%) posterolateral territories that had impaired regional wall motion at baseline demonstrated improved function at early follow up (p<0.001). The total exercise myocardial perfusion score derived from the thallium scintigrams improved following CABG. The improved perfusion was most noted in the inferior and posteroseptal regions. There was no change in the mean perfusion score in the anterior, posterolateral and septal territories. Rest redistribution imaging did identify patients that demonstrated an increased LVEF at early follow up. In the group of patients who demonstrated a reversible defect from rest to redistribution, LVEF rose from 32% (+/- 13%) to 36% (+/-13%) at early follow up, p=0.004. However at late follow up this was not sustained and LVEF fell back to (32%), p<0.01 for change from early follow up. In contrast amongst those who did not demonstrate reversibility from the rest to redistribution image there was no change in LVEF; at baseline LVEF was 33% (+/-10%), at early follow up 33% (+/-11%) and at late follow up 33% (+/-12%). Similarly, those patients who demonstrated improvement in regional wall motion with dobutamine also demonstrated an initial rise in LVEF at early follow up with a subsequent return to baseline values. In this group LVEF was 27% (+/-8%) III at baseline and 30% (+/-10%) at early follow up (p<0.01). By late follow up LVEF had fallen back to 28% (+/-13%), p<=0.04). Coronary bypass surgery has little effect on left ventricular ejection fraction. Right ventricular ejection fraction falls sharply, and the fall is sustained at least nine months after the operation. Septal regional function deteriorates after bypass surgery, while posterolateral function seems to improve. Global myocardial perfusion indices improve following CABG, particularly in the inferior and posteroseptal regions. Both rest redistribution thallium scintigraphy and low dose dobutamine radionuclide ventriculography can identify a group of patients whose LVEF improves 6 weeks after operation. However this improvement is not sustained, and by nine months after operation LVEF returns to baseline values

Evolving dynamic networks: An underlying mechanism of drug resistance in epilepsy?

This is the final version. Available on open access from Elsevier via the DOI in this recordAt least one-third of all people with epilepsy have seizures that remain poorly controlled despite an increasing number of available anti-epileptic drugs (AEDs). Often, there is an initial good response to a newly introduced AED, which may last up to months, eventually followed by the return of seizures thought to be due to the development of tolerance. We introduce a framework within which the interplay between AED response and brain networks can be explored to understand the development of tolerance. We use a computer model for seizure generation in the context of dynamic networks, which allows us to generate an ‘in silico’ electroencephalogram (EEG). This allows us to study the effect of changes in excitability network structure and intrinsic model properties on the overall seizure likelihood. Within this framework, tolerance to AEDs – return of seizure-like activity – may occur in 3 different scenarios: 1) the efficacy of the drug diminishes while the brain network remains relatively constant; 2) the efficacy of the drug remains constant, but connections between brain regions change; 3) the efficacy of the drug remains constant, but the intrinsic excitability within brain regions varies dynamically. We argue that these latter scenarios may contribute to a deeper understanding of how drug resistance to AEDs may occur.Medical Research Council (MRC)Royal SocietyWellcome TrustEngineering and Physical Sciences Research Council (EPSRC

Emergent Phenomena From Dynamic Network Models: Mathematical Analysis of EEG From People With IGE

In this thesis mathematical techniques and models are applied to electroencephalographic (EEG) recordings to study mechanisms of idiopathic generalised epilepsy (IGE). First, we compare network structures derived from resting-state EEG from people with IGE, their unaffected relatives, and healthy controls. Next, these static networks are combined with a dynamical model describing the ac- tivity of a cortical region as a population of phase-oscillators. We then examine the potential of the differences found in the static networks and the emergent properties of the dynamic network as individual biomarkers of IGE. The emphasis of this approach is on discerning the potential of these markers at the level of an indi- vidual subject rather than their ability to identify differences at a group level. Finally, we extend a dynamic model of seizure onset to investigate how epileptiform discharges vary over the course of the day in ambulatory EEG recordings from people with IGE. By per- turbing the dynamics describing the excitability of the system, we demonstrate the model can reproduce discharge distributions on an individual level which are shown to express a circadian tone. The emphasis of the model approach is on understanding how changes in excitability within brain regions, modulated by sleep, metabolism, endocrine axes, or anti-epileptic drugs (AEDs), can drive the emer- gence of epileptiform activity in large-scale brain networks. Our results demonstrate that studying EEG recordings from peo- ple with IGE can lead to new mechanistic insight on the idiopathic nature of IGE, and may eventually lead to clinical applications. We show that biomarkers derived from dynamic network models perform significantly better as classifiers than biomarkers based on static network properties. Hence, our results provide additional ev- idence that the interplay between the dynamics of specific brain re- gions, and the network topology governing the interactions between these regions, is crucial in the generation of emergent epileptiform activity. Pathological activity may emerge due to abnormalities in either of those factors, or a combination of both, and hence it is essential to develop new techniques to characterise this interplay theoretically and to validate predictions experimentally

Phase-field modeling of the thermo-electro-mechanically coupled behavior of ferroelectric materials

Ferroelectric materials are widely used in engineering and science applications due to their large nonlinear thermo-electro-mechanical coupling. Of interest recently, has been the study of the giant electrocaloric effect, a large adiabatic temperature change with the application of an electric field, due to its possible application for solid-state cooling. The electrocaloric effect is maximized near phase transitions, where entropy jumps contribute to a large nonlinear effect. This dissertation develops a continuum phase-field model for the thermo-electro-mechanically coupled behavior for ferroelectric materials. The model is derived from thermodynamic considerations and based on a phenomenological free energy function. The finite element method is applied to solve the governing equations for a selected set of boundary value problems. Mechanical displacement, electric potential, polarization and temperature are used as degrees of freedom in the formulation of the finite element implementation of the model. The a geometry for an isothermal stable two-dimensional ferroelectric to paraelectric phase boundary is developed, along with appropriate boundary conditions, and simulated using the nonlinear finite element method for a variety of ferroelectric domain widths. The dependence of the phase coexistence temperature, boundary energy, entropy jump across the boundary and closure domain shape on the ferroelectric laminate domain width is quantified. A simulation of the motion of the phase boundary through the material under entropy/heat input control is demonstrated. Next, a realistic electrocaloric cooling device based on a multilayer ferroelectric capacitor is simulated through a full thermodynamic refrigeration cycle. The model geometry and boundary conditions are chosen to match realistic device configurations. The device is driven through a cycle with two adiabatic and two constant electric field legs, and compared with the analytically computed ideal plane strain electrocaloric cooling cycle. Several inefficiencies arise in the device, including incomplete transformation, entropy loss due to phase boundary motion, and high energy zones with large stresses and closure domains at the electrode tip. Lastly, motivated by potential uses as actuators, the domain structure in three-dimensional ferroelectric nanodots is modeled by cooling from a paraelectric phase. The expected vortex domain structure forms in sufficiently small dots, but distorts upon further cooling to room temperature. The room temperature transfer of dots to a rigid substrate and actuation via an out-of-plane electric field leads to incomplete domain switching, thereby reducing actuator displacements.Engineering Mechanic

Treatment effects in epilepsy:a mathematical framework for understanding response over time

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom

The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement: The code and synthetic networks generated are available upon request.Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.Medical Research Council (MRC)Epilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome TrustInnovate U

Effects of Selected Organic Polyelectrolytes on Biological Systems

Sanitary and Public Health Engineerin