Medicine (St Vincent's) - Theses
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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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ItemThe prolonged ambulatory electroencephalography in genetic generalised epilepsies: characteristics and predictors of prognosis( 2015)INTRODUCTION: Epilepsy is a common and serious neurological disorder affecting 65 million people worldwide. Idiopathic (genetic) generalised epilepsy (GGE) constitutes 15-20% of patients with epilepsy. The electroencephalogram (EEG) plays a crucial role in the diagnosis and classification of epilepsy. Bilateral, symmetrical and synchronous generalised spike-wave activity is considered to be the electrographic hallmark of GGE. However, atypical EEG and clinical features as well as the value of EEG in predicting long-term prognosis of IGE have not been well studied in the past. This thesis presents the first detailed and systematic study on typical and atypical EEG findings, atypical focal seizure symptoms and prognosis of GGE based on quantified, 24-hour, ambulatory EEG data. AIMS AND METHODS: (a) To quantify the typical EEG abnormalities in GGE, describe the circadian variations of epileptiform discharges and explore the differences among syndromes: I prospectively recruited and studied a cohort of patients diagnosed with GGE and classified into syndromes based on International League against Epilepsy criteria. All patients had 24-hour ambulatory EEG recordings according to a standard protocol. I quantified EEG abnormalities and used analysis of variance test to explore the differences among syndromes. (b) To quantify the atypical EEG abnormalities in GGE and to explore the relationship between atypical EEG findings and clinical variables: I used generalised linear mixed models to explore the influence of clinical variables (syndrome, age, state of arousal, number of antiepileptic drugs, seizure-free duration, epilepsy duration) on the outcome of atypical EEG characteristics. (c) To explore the association between seizure-free duration and EEG parameters: I analysed the EEG predictors of seizure recurrence with stepwise Cox proportional hazards regression model. (d) To evaluate focal seizure symptoms among patients diagnosed with GGE and to explore the association between focal seizure symptoms and focal epileptiform discharges as well as seizure-free duration: I elicited focal seizure symptoms (FSS) using a standardised, validated questionnaire. Chi-square test for independence was used to explore the relationship between focal epielptiform discharges and FSS. Regression analysis was conducted to examine the relationship between the duration of seizure freedom and FSS. RESULTS: A total of 120 patients were recruited, of which 13 had normal ambulatory EEGs. The final cohort consisted of 33.3% males and 66.7% females with mean age of 28.5±10.7 years (range, 13-58). The mean age of seizure onset was 13.3±5.1. (a) The vast majority (96%) of epileptiform discharges are symmetric in amplitude with fronto-central maximum in topography. Two-thirds of discharges occur in sleep. Epileptiform discharges demonstrate circadian patterns with four peaks; before midnight, after midnight, early morning and afternoon. There are significant differences in spike densities among syndromes. In general quantified epileptiform activity is higher in JAE and JME than CAE and GTCSO. (b) 66% of 24-hour EEG recordings show atypical abnormalities, significantly influenced by the state of arousal. (c) Longer generalised paroxysms are associated with shorter duration of seizure freedom in GGE. (d) 52% of patients report focal seizure symptoms. There is no association between focal seizure symptoms and focal epileptiform discharges. However, focal seizure symptoms are associated with shorter seizure-free duration. CONCLUSION: The results demonstrate the value of prolonged EEG as a biomarker of diagnosis and potentially prognosis. Prolonged EEG recordings have demonstrated: 1) There are circadian patterns in the occurrence of epileptifom discharges. 2) Atypical EEG features and FSS are common in GGE. 3) Recognition of these variations is important to avoid misdiagnosis and inappropriate choice of antiepileptic drugs.