AbstractThe work presented in this thesis focuses on two clinical populations: people with idiopathic epilepsy and people with psychogenic nonepileptic seizures (PNES). The overarching objectives are to improve the understanding of the psychiatric and cognitive characteristics of these disorders and to advance the recognition and diagnosis of these disorders.
Thesis objective I: to improve the understanding of the psychiatric and cognitive
characteristics of people with epilepsy or PNES
Impaired performance on neuropsychological tests has been often observed in people with PNES, and their level of functioning across multiple cognitive domains is often comparable to that of people with epilepsy. This has been ascribed to the influence of emotional processes, psychopathology, or misdirected effort or attention. However, empirical support for these claims is scarce. A further issue of concern is suicidality, as rates are elevated in both clinical populations. Identification of risk factors for suicidality is essential to inform risk prevention strategies.
Methods and results
A prospective observational case-control study was performed to explore the extent to which measures of depression, anxiety, dissociation, medically unexplained somatic symptoms, suicidality, and past exposure to traumatic events impact on cognitive functioning in people with PNES as compared to those with epilepsy (Chapter 5). 73 patients (epilepsy n = 34; PNES n = 39) were recruited. Correlations between cognitive and psychiatric measures were explored within each group, and correlational differences between groups were assessed. Results revealed distinct correlation patterns between groups in the relationship between traumatic life experiences and cognitive switching ability, and between the number of medically unexplained somatic symptoms and vocabulary ability.
In two further studies, a retrospective cohort design was implemented to identify demographic and clinical risk factors for suicidal ideation and suicide attempts in epilepsy and PNES (Chapter 7), and to quantify the risk imposed by concurrent PNES on suicide attempts in people with epilepsy (Chapter 6). Data on 2460 people with epilepsy, PNES or concurrent diagnosis who attended South London and Maudsley Hospital in the last 15 years were analysed. Findings revealed the existence of general as well as disorder-specific risk factors for suicidality (Chapter 7) and showed that people with PNES and people with concurrent epilepsy and PNES are at significantly increased risk of suicide attempt-related hospitalisation than people with epilepsy alone (Chapter 6).
Preliminary evidence was identified for the interplay between cognitive functioning and two relevant clinical characteristics of PNES: the tendency to experience a high number of medically unexplained physical symptoms and the experience of trauma. The presence of concurrent PNES in people with epilepsy increases the odds of suicide attemptrelated hospitalisation; shared as well as disorder-specific demographic and clinical risk factors can be identified for each population.
Thesis objective II: to advance the recognition and diagnosis of epilepsy and PNES
Differentiating between epileptic and psychogenic nonepileptic seizures represents a considerable challenge in clinical practice, resulting in frequent misdiagnosis and long diagnostic delays. Identifying novel markers with diagnostic relevance would represent a valuable resource to aid clinical decision-making. Quantitative markers extracted from resting-state
electroencephalogram (EEG) may reveal subtle neurophysiological dynamics that are diagnostically relevant.
Methods and results
A systematic review was performed to summarise evidence on EEG markers extracted from visually normal, interictal resting-state recordings in adults with epilepsy or PNES (Chapter 9). 26 studies were identified. Results indicate that oscillations along the theta frequency (4-8 Hz) and indices of EEG slowing may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend.
Two observational, retrospective diagnostic accuracy studies were subsequently performed to test the clinical validity of resting-state EEG markers for the differential diagnosis of epilepsy and PNES. Clinical EEG data were collected for 179 consecutive patients with a suspected diagnosis of epilepsy or PNES who were not taking any medications at the time of EEG; 148 age- and gender-marched patients ultimately received a diagnosis and were included in the analyses.
The first study (Chapter 10) was a hypothesis-driven diagnostic accuracy study based on the systematic review findings. A machine learning pipeline was implemented. Results suggested that EEG markers that were previously identified as promising diagnostic indicators (i.e., theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and PNES.
The next study (Chapter 11) was a data-driven diagnostic accuracy study; based on a high number of quantitative EEG features and a machine learning pipeline, it assessed whether previously unexplored measures show higher promise as diagnostic markers. Results indicated that identifying markers that show good correlation with a categorical diagnostic label is challenging due to a considerable overlap in presentations between diagnostic classes, and to the presence of patient sub-groups that bias feature selection towards the identification of transient, yet more dominant EEG dynamics.
A nearly unexplored field of research was identified, which is the implementation of resting-state EEG markers to help distinguishing between people with epilepsy and people with PNES. Markers that were identified in the context of previous epilepsy research were found to have limited clinical validity for this classification task. A search for alternative diagnostic markers uncovered the challenges involved and generated useful recommendations for further research.
|Date of Award||1 Mar 2023|
|Supervisor||Allan Young (Supervisor) & John Shotbolt (Supervisor)|