Seizure prediction and control in epilepsy

The first part of my thesis presents an overview of the different approaches used in the past two decades in the attempt to forecast epileptic seizure on the basis of intracranial and scalp EEG. Past research could reveal some value of linear and nonlinear algorithms to detect EEG features changing over different phases of the epileptic cycle. However, their exact value for seizure prediction, in terms of sensitivity and specificity, is still discussed and has to be evaluated. In particular, the monitored EEG features may fluctuate with the vigilance state and lead to false alarms. Recently, such a dependency on vigilance states has been reported for some seizure prediction methods, suggesting a reduced reliability. An additional factor limiting application and validation of most seizure-prediction techniques is their computational load. For the first time, the reliability of permutation entropy [PE] was verified in seizure prediction on scalp EEG data, contemporarily controlling for its dependency on different vigilance states. PE was recently introduced as an extremely fast and robust complexity measure for chaotic time series and thus suitable for online application even in portable systems. The capability of PE to distinguish between preictal and interictal state has been demonstrated using Receiver Operating Characteristics (ROC) analysis. Correlation analysis was used to assess dependency of PE on vigilance states. Scalp EEG-Data from two right temporal epileptic lobe (RTLE) patients and from one patient with right frontal lobe epilepsy were analysed. The last patient was included only in the correlation analysis, since no datasets including seizures have been available for him. The ROC analysis showed a good separability of interictal and preictal phases for both RTLE patients, suggesting that PE could be sensitive to EEG modifications, not visible on visual inspection, that might occur well in advance respect to the EEG and clinical onset of seizures. However, the simultaneous assessment of the changes in vigilance showed that: a) all seizures occurred in association with the transition of vigilance states; b) PE was sensitive in detecting different vigilance states, independently of seizure occurrences. Due to the limitations of the datasets, these results cannot rule out the capability of PE to detect preictal states. However, the good separability between pre- and interictal phases might depend exclusively on the coincidence of epileptic seizure onset with a transition from a state of low vigilance to a state of increased vigilance. The finding of a dependency of PE on vigilance state is an original finding, not reported in literature, and suggesting the possibility to classify vigilance states by means of PE in an authomatic and objectic way. The second part of my thesis provides the description of a novel behavioral task based on motor imagery skills, firstly introduced (Bruzzo et al. 2007), in order to study mental simulation of biological and non-biological movement in paranoid schizophrenics (PS). Immediately after the presentation of a real movement, participants had to imagine or re-enact the very same movement. By key release and key press respectively, participants had to indicate when they started and ended the mental simulation or the re-enactment, making it feasible to measure the duration of the simulated or re-enacted movements. The proportional error between duration of the re-enacted/simulated movement and the template movement were compared between different conditions, as well as between PS and healthy subjects. Results revealed a double dissociation between the mechanisms of mental simulation involved in biological and non-biologial movement simulation. While for PS were found large errors for simulation of biological movements, while being more acurate than healthy subjects during simulation of non-biological movements. Healthy subjects showed the opposite relationship, making errors during simulation of non-biological movements, but being most accurate during simulation of non-biological movements. However, the good timing precision during re-enactment of the movements in all conditions and in both groups of participants suggests that perception, memory and attention, as well as motor control processes were not affected. Based upon a long history of literature reporting the existence of psychotic episodes in epileptic patients, a longitudinal study, using a slightly modified behavioral paradigm, was carried out with two RTLE patients, one patient with idiopathic generalized epilepsy and one patient with extratemporal lobe epilepsy. Results provide strong evidence for a possibility to predict upcoming seizures in RTLE patients behaviorally. In the last part of the thesis it has been validated a behavioural strategy based on neurobiofeedback training, to voluntarily control seizures and to reduce there frequency. Three epileptic patients were included in this study. The biofeedback was based on monitoring of slow cortical potentials (SCPs) extracted online from scalp EEG. Patients were trained to produce positive shifts of SCPs. After a training phase patients were monitored for 6 months in order to validate the ability of the learned strategy to reduce seizure frequency. Two of the three refractory epileptic patients recruited for this study showed improvements in self-management and reduction of ictal episodes, even six months after the last training session.

Epileptic and developmental encephalopathies: clinical and genetic study of adult patients attending a tertiary Epilepsy Centre

Objectives: To fully re-evaluate patients with early-onset epilepsy and intellectual disability with neurological, neurophysiological and neuropsychological examination in order to contribute to expanding the phenotypic spectrum of known epileptic encephalopathy (EE)-related genes and to identify novel genetic defects underlying EEs. Methods: We recruited patients with epilepsy and intellectual disability (ID) referring to our Epilepsy Centre. Patients underwent full clinical and neurophysiologic evaluation. When possible they underwent neuroradiologic investigations. Selected cases also underwent genetic analysis. Results: We recruited 200 patients (109 M, 91 F; mean age 36 years old). Mean age at epilepsy onset was 4 years old. The degree of ID was borderline in 4.5% of patients, mild in 25%, moderate in 38% and severe in 32.5%. EEG showed epileptiform abnormalities in 79.5% of patients. One hundred and thirty-one patients out of the 200 recruited (65.5%) did not have an aetiological diagnosis. All the patients underwent full clinical reassessment and when necessary they performed neuroradiologic and genetic investigations as well. We identified 35 patients with a genetic aetiology. In 8 cases a structural brain lesion was observed. In 33 patients, a genetic aetiology was identified. In 2 patients with drug-resistant seizures video-EEG allowed the identification of non-epileptic seizures, and in one patient we discontinued anti-epileptic drugs. In these patients, the aetiological diagnosis was made after 30 years (range 9-60 years) from the disease onset. Conclusions: In a population of 200 adult patients with epilepsy and ID, an aetiological cause was identified in 45 patients after 30 years from the disease onset. Aetiological diagnosis, especially if genetic, has significant positive implications for patients, even if it has been made after years from the beginning of the disease. Benefits include better-focused antiepileptic drug (AED) choice, sparing of further unnecessary investigations and improved knowledge of comorbidities.