Biomedical Engineering - Theses
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ItemCycles and Seizure Forecasting in Epilepsy( 2023-08)People with epilepsy were once termed “lunatics” because their seizures appeared to occur in synchrony with the lunar cycle. The cause was sinning at the wrong time of the lunar phase: “If they [the planets] should scrutinize while the Moon is putting an end to a certain phase, they produce maniacs, ecstatics, epileptics, those who chant”. This conviction was consistent with medicine at the time, which believed that the moon caused an unnaturally moist brain, leading to epileptic seizures. Nowadays, we know that most people with epilepsy have at least one seizure cycle, the periods of which are unique to the individual (7 days, 10 days, 20 days, 28 days, etc.). Yet we have a very limited understanding of where they derive from and how to best understand them for seizure forecasting and other applications. This thesis aims to expand our scientific understanding of the co-existing factors and mechanistic drivers of multiday cyclical patterns in epilepsy while investigating their utility in improving the performance of seizure forecasting algorithms. The presented research addresses this overarching aim by answering three questions: (1) What physiological signals correlate with cyclical patterns in epileptic seizures? (2) Can physiological signals correlating with cyclical patterns in epileptic seizures be tracked and utilised to improve seizure forecasting algorithms? (3) How can we begin investigating the systemic mechanisms of cycles in epilepsy and beyond? By answering these questions, this thesis aspires to give future researchers the foundational knowledge and resources needed to gain greater insight into cycles in epilepsy and mammalian biology.
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ItemPerturbation-based biomarkers outperform passive ones as indicators for changes in cortical excitability and seizure transitions( 2023-06)Epilepsy is a neurological disorder that affects patients differently and manifests as spontaneous recurrent seizures. It is a prevalent condition that affects about 50 million people globally, but its exact aetiology and pathophysiology often remain elusive. Functional imaging techniques allow us to observe alterations in brain activity during seizures. For instance, EEG data reveals hyper-excitable and hyper-synchronised neuronal firing in the brain. This thesis explores the potential of using biomarkers to track cortical excitability and detect state transitions in epileptic models. Nine biomarkers were developed based on theoretical concepts such as Critical Slowing Down (CSD) and signal processing methods. The performances of these biomarkers were evaluated in neural mass models, animal models of epilepsy and human data. In this thesis, we first employed two mesoscopic neural mass models to study state transitions in a controlled way. Through simulation and perturbation, we found that biomarkers can anticipate state transitions before they occur. They are effective when a system undergoes a critical transition, but less so when the system jumps between multiple stable states due to stochastic noise. Overall, active biomarkers with perturbations outperform passive biomarkers in terms of accuracy and robustness. We also investigated how biomarkers and perturbations can be used to identify state transitions using experimental data. We contrasted active and passive biomarkers on various time scales: pre-seizure scale, circadian cycle and lifespan scale in animal data. We found that active biomarkers with perturbations are superior to passive biomarkers in monitoring state transitions and giving early warnings before seizures. Moreover, by examining two datasets of human epileptic transitions, we further assess the possibility of adopting biomarkers in clinical studies. It is observed that changes in biomarkers differ depending on both patients and seizures. We propose that seizure transitions are not only patient-dependent but also seizure-dependent. Through this thesis, we have shown that biomarkers can detect the underlying changes that precede a seizure by measuring cortical excitability with perturbations. By applying a small perturbation, it is possible to probe changes in brain states by measuring the response to perturbation, or cortical excitability. Understanding changes in cortical response to perturbation during brain state transitions may yield important insights into brain disorders. The methods employed in this project are anticipated to be applicable to clinical settings for seizure forecasting and epilepsy management.
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ItemNovel seizure risk markers( 2022)Epilepsy is one of the most common severe neurological diseases and is characterized by recurring seizures. Currently, about 70 million people worldwide live with epilepsy and over 30% of them cannot be adequately treated with medication. The unpredictability of seizures is a severely debilitating aspect of epilepsy that significantly impacts the quality of life of patients. Consequently, there is a clinical need to find new markers that are useful for seizure forecasting. High-frequency activity (HFA) is a newly proposed biomarker for epilepsy, but its predictive value in seizure forecasting remains uncertain. Emerging new evidence has shown that ambient air pollution affects the central nervous system, but little is known about its effect on epileptic seizures. The goal of this thesis is to investigate potential novel markers for improving seizure forecasting and epilepsy management. To achieve this goal, the work of this thesis addresses several key questions: How do HFA rates and locations change over time, and how do these changes correspond with seizures? Can HFA forecast seizures? Is ambient air pollution associated with the risk of epileptic seizures? By addressing these fundamental questions, this thesis aims to provide the basis for formulating an innovative approach to improve seizure forecasting and control. In the first research chapter, the spatiotemporal profiles of HFA are investigated using long-term intracranial EEG. The results show that HFA rates have post-implantation variability, periodic cycles, and patient-specific relationships with seizures. These findings caution against using HFA as a presurgical metric without testing its reliability over time and suggest that tracking and utilising cycles of HFA rates may offer an exciting new opportunity to track cycles of seizures. In the second research chapter, a real-time phase estimation approach and seizure forecasting framework are developed using instantaneous HFA rates and phases of HFA cycles. The results show that HFA can be a useful biomarker to forecast seizures in patients with refractory epilepsy. The proposed real-time phase estimation approach can estimate the HFA phase over time with high accuracy and can be generalized to other seizure risk markers. In the third research chapter, ambient air pollutants are explored as potential seizure risk factors using a participant-time-stratified case-crossover design with conditional Poisson regression models. The results show that elevated ambient carbon monoxide (CO) concentrations, though within the Australian air quality standard, may be associated with increased risks of epileptic seizures; no significant associations were found in the other studied air pollutants (nitrogen dioxide, ozone, sulphur dioxide, and particulate matter less than 10 micrometers in diameter). These findings may have important clinical and public health implications and may offer potential new leads to improve seizure forecasting and prevention. Overall, these studies provide new evidence that HFA and ambient CO may serve as potential novel seizure risk markers. It is our hope that the work of this thesis contributes towards real-life seizure forecasting, informing new strategies to reduce the uncertainty of seizures, and eventually improving epilepsy management and the quality of life for people with epilepsy.
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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.