Cardiac left atrium CT image segmentation for ablation guidance

Catheter ablation is an increasingly important curative procedure for atrial fibrillation. Knowledge of the local wall thickness is essential to determine the proper ablation energy. This paper presents the first semi-automatic atrial wall thickness measurement method for ablation guidance. It includes both endocardial and epicardial atrial wall segmentation on CT image data. Segmentation is based on active contours, Otsu's multiple threshold method and hysteresis thresholding. Segmentation results were compared to contours manually drawn by two experts, using repeated measures analysis of variance. The root mean square differences between the semi-automatic and the manually drawn contours were comparable to intra-observer variation (endocardium: p = 0.23, epicardium: p = 0.18). Mean wall thickness difference is significant between one of the experts on one side, and the presented method and the other expert on the other side (

Computational modelling of epileptic seizure dynamics and control

Epilepsy is a neurological condition affecting about 50 million people worldwide. It is a condition of the nervous system in which neuronal populations alternate between periods of normal ongoing electrical activity and periods of paroxysmal activity. Computational models provide a powerful framework both for simulating dynamics of neuronal networks involved in epileptic seizures and for testing new treatment options. The aims of this thesis are to better understand the mechanisms involved in the dynamics of epileptic seizures and to devise stimulation paradigms that may be applied to control epileptic seizures, both by studying and applying computational models. Computational models can be used as descriptive tools, where observed phenomena are captured in a model. In Chapter 2 of the thesis activity-dependent channels are introduced in a realistic neuronal model, eliciting both steady state and limit cycle behaviour. The effect on the distribution of ictal and inter-ictal length is shown. In Chapter 3 we use a model at another level of abstraction. A model of coupled oscillators-rotators shows a variety of different dynamic behaviour patterns. It is interesting that, although the system may change states, the total activity of the system does not necessarily change. A correspondence between the realistic model and the analytical model is studied in Chapter 4. We show that within the given parameter space and studying the system at the level of general system dynamics, we infer that the analytical model will behave in a similar fashion as the realistic neuronal model. Neuronal models can also be used as prescriptive tools. In Chapter 5 we propose a reactive control paradigm of epileptic seizures. A control module applies counter stimuli to abort seizures. It is shown that the proposed method is able to control the system, while delivering a minimum amount of energy to the system. It will be favourable to detect a seizure before it actually manifests itself and prevent it from initiating. In Chapter 6 we propose a method to apply stimulation when detecting a baseline shift before the seizure, caused by the decrease of inhibition. It is shown that when applying stimulation with a proper polarity and ratio between cell populations, the moment of the first seizure can be postponed. Observed physiological phenomena can be connected to each other based purely on clinical evidence, without understanding the dynamics of the underlying mechanism. Computational models can be used to provide insight into those unknown mechanisms. In Chapter 7, we aim to connect two phenomena, namely high frequency oscillations (HFOs) and epileptic seizures. We present a possible underlying mechanism that gives rise to both phenomena. This study shows that microscopic features, like gap-junction dynamics, may be connected to macroscopic phenomena like HFOs and seizures by across-scale computational models. In conclusion we have improved the insight into dynamic mechanisms underlying epilepsy, using computational models. In the course of the thesis, computational modelling moved from a descriptive tool capturing observed epileptic phenomena, via a predictive tool, proposing different stimulation paradigms, to a prospective tool, that may be able to explain and predict epileptic phenomena

Plasticity-modulated seizure dynamics for seizure termination in realistic neuronal models

In previous studies we showed that autonomous absence seizure generation and termination can be explained by realistic neuronal models eliciting bi-stable dynamics. In these models epileptic seizures are triggered either by external stimuli (reflex epilepsies) or by internal fluctuations. This scenario predicts exponential distributions of the duration of the seizures and of the inter-ictal intervals. These predictions were validated in rat models of absence epilepsy, as well as in a few human cases. Nonetheless, deviations from the predictions with respect to seizure duration distributions remained unexplained. The objective of the present work is to implement a simple but realistic computational model of a neuronal network including synaptic plasticity and ionic current dynamics and to explore the dynamics of the model with special emphasis on the distributions of seizure and inter-ictal period durations. We use as a basis our lumped model of cortical neuronal circuits. Here we introduce 'activity dependent' parameters, namely post-synaptic voltage-dependent plasticity, as well as a voltage-dependent hyperpolarization-activated current driven by slow and fast activation conductances. We examine the distributions of the durations of the seizure-like model activity and the normal activity, described respectively by the limit cycle and the steady state in the dynamics. We use a parametric γ-distribution fit as a quantifier. Our results show that autonomous, activity-dependent membrane processes can account for experimentally obtained statistical distributions of seizure durations, which were not explainable using the previous model. The activity-dependent membrane processes that display the strongest effect in accounting for these distributions are the hyperpolarization-dependent cationic (I(h)) current and the GABAa plastic dynamics. Plastic synapses (NMDA-type) in the interneuron population show only a minor effect. The inter-ictal statistics retain their consistency with the experimental data and the previous model