Biomedical Engineering - Theses
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ItemEpileptic seizures: mechanisms and forecasting( 2018)Seizure forecasting, like weather forecasting, was once considered the domain of charlatans and purveyors of science fiction. However, neuroscience has now advanced to the point of translating seizure forecasting research into widely available clinical applications. Just like weather apps that report the probability of rain on a given day, it is now conceivable that devices will inform people with epilepsy about their current likelihood of having a seizure. This information could be life-changing: restoring a sense of control and the ability to participate in everyday activities. Over 65 million people around the world have epilepsy; one third cannot control their seizures with medication. The unpredictability of seizures can be devastating, leading to persistent anxiety, exclusion from day-to-day life, serious injury or death. The aim of this thesis is to develop a clinically useful framework for forecasting seizures. The presented research addresses several key questions towards this goal: What drives seizure transitions? Are there underlying rhythms governing seizure onset? If underlying rhythms exist, how can they be integrated into a single determination of an individual's seizure likelihood? By presenting answers to these questions this thesis aims to form the basis for an innovative approach to seizure forecasting.
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ItemTracking the changes of brain states during epileptogenesis by probing( 2017)Epilepsy affects around 50 million people worldwide. Brain injuries and lesions, such as traumatic brain injury, central nervous system infection and stroke, are associated with higher risk of developing epilepsy. It is hypothesised that electrically-induced brain responses (probing responses) can reflect physiological changes during epileptogenesis, which may provide insights into epileptogenesis and possible new therapies. A systematic continuous longitudinal study of probing responses in a well-controlled environment with both normal and epileptic brains is presented. The objective of this study is to demonstrate how electrically-induced neural responses (probing) track the physiological changes in the brain during epileptogenesis. Intrahippocampal tetanus toxin rat models were used as the model of epilepsy. Control rats received injections without tetanus toxin. Epidural electrodes were implanted to deliver electrical stimulation and record EEG over periods of 9 to 10 weeks in a room with controlled temperature and automatic dark/light switching. It is demonstrated that probing responses can expose sleep/wake cycles, recovery from surgery and epileptogenesis over the study period. The differences between probing responses in sleep and wake states are quantified by a two-level mixed-effects linear regression model. The changes of probing responses related to the recovery from surgery are modelled by a modified logistic function. The probing responses related to epileptogenesis in tetanus toxin models are uncovered after the effects of surgical recovery and sleep/wake cycle are removed. The changes can be observed in the infradian time scale and several markers are found that are associated with the time of the peak of seizure occurrence and the time of seizure remission. This study is the first step to identify stages of epileptogenesis using probing responses. The potential clinical application of probing can be a biomarker for epileptogenesis, a prognostic tool to assess whether epilepsy will develop after a trauma, a way to predict whether remission will occur after posttraumatic epilepsy has developed or a biomarker to assess the effectiveness of traumatic brain injury therapies.
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ItemValidating MEG and EEG finite element head models using a controlled rabbit experiment of skull defects( 2015)Epilepsy affects 20 million people world-wide. When treatment of focal epilepsy with anti-epileptic drugs is ineffective, resective surgery may be considered. It is then essential to accurately determine the location of the seizure focus. Magnetoencephalography (MEG) and electroencephalography (EEG) allow us to reconstruct the location of event-related brain activity using a volume conductor model of the head. The objective of this thesis is to validate MEG and EEG finite element head models using a rabbit experiment of skull defects. An in vivo rabbit experiment was developed that allowed recording high-resolution MEG and EEG above two conducting skull defects. An implantable, coaxial current source was constructed and placed at a series of positions in the cortex under the skull defects. An agarose gel was developed that provided a time-stable conductivity that mimicked different tissue types in the skull defects. A node-shifted, cubic finite element mesh of the head was generated, which differentiated nine tissue types. For the first time, in vivo, experimental evidence was provided of the substantial influence of skull defects on MEG signals. The MEG signal amplitude reduced by as much as 20%, while the EEG signal amplitude increased 2-10 times. The MEG signal amplitude deviated more from the intact skull condition when the source was central under a skull defect. Using the exact finite element head model, forward simulation of the MEG and EEG signals replicated the experimentally observed characteristic magnitude and topography changes due to skull defects. When skull defects, with their physical conductivity, were incorporated in the head model, location and orientation errors during reconstruction were mostly eliminated. The conductivity of the skull defect material non-uniformly modulated its influence on MEG and EEG signals and source reconstruction. The concordance of experimental measurements of the influence of skull defects on MEG and EEG signals and finite element simulations of exactly that experiment validated the finite element head modelling technique. Detailed finite element head models can improve non-invasive MEG- and EEG-based diagnostic localisation of brain activity, such as epileptic discharges.
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ItemTracking neural dynamics( 2014)Epilepsy affects around 1% of the world's population, of which roughly a third are refractory to treatment. A major impediment to the development of effective treatments for these patients is the lack of understanding of the mechanisms underlying the disorder. In this thesis, a model-based approach is developed to provide some insight into these mechanisms. Initially, it is demonstrated that parameters linked to physiology from nonlinear models of the brain can be estimated accurately under various conditions. An animal model of epilepsy is used to illustrate that this computational model of the brain can be used to demonstrate variability in seizure mechanisms between animals, and over time. In particular, it is demonstrated that seizures group across days in the animal model, and that for each group, different physiological mechanisms are involved. This provides evidence that in the animal model studied seizures evolve, and that this evolution is linked to seizure grouping. With the knowledge that seizures are subject-specific and evolve over time, it may be possible to develop techniques that use control system theory to continuously alter therapy based on estimated physiological parameters inferred from recorded EEG, to provide treatments that are more efficacious. odel of epilepsy is used to illustrate that this computational model of the brain can be used to demonstrate variability in seizure mechanisms between animals, and over time. In particular, it is demonstrated that seizures group across days in the animal model, and that for each group, different physiological mechanisms are involved. This provides evidence that in the animal model studied seizures evolve, and that this evolution is linked to seizure grouping. With the knowledge that seizures are subject-specific and evolve over time, it may be possible to develop techniques that use control system theory to continuously alter therapy based on estimated physiological parameters inferred from recorded EEG, to provide treatments that are more efficacious.