Functional brain connectivity to reveal the relationships between seizures, cognition and behaviour in children with epilepsy

Additional Supervisor: Dr Javier Escudero Rodriguez (University of Edinburgh)

Background

Epilepsy is one of the most common neurological disorders. It causes devastating effects on the quality of life of its patients, which are particularly dire in the case of children. This PhD project will apply mathematical time series analysis tools to electroencephalogram (EEG) recordings to better understand the causes of cognitive and behavioural impairments in children with epilepsy. This is an exciting collaboration between clinicians and engineers, it will benefit from the availability of already collected clinical datasets at the Centre for Clinical Brain Sciences, and it will train the PhD student in a suite of quantitative methods that includes computational and digital signal analysis tools, and connectivity analysis.

The EEG is highly relevant to epilepsy since it is routinely used in the clinic to detect ictal activity. However, epilepsy leads to other much more subtle effects in the EEG than seizures (van Diessen et al., 2016). Previous EEG studies in children with epilepsy have shown a relationship between impairments, frequent epileptic discharges and severity of the background abnormality in EEGs but whether this relationship is epilepsy specific or not has not been investigated yet. A prompt identification and monitoring of comorbid cognitive and behavioural impairments is urgently needed (Braun, 2017).

The fact that epilepsy might affect non-local brain connections, leading to diffuse damage, might explain some of the developmental problems in children with epilepsy (Stam, 2014). This suggests that computationally driven connectivity analysis of EEG activity in children with epilepsy is a prime candidate to reveal this currently hidden relationships between epilepsy, impairments and EEG abnormalities. In this sense, it is important to emphasise that the feasibility of this project is strengthen by pilot results by our team where we have found modest, but robust, correlations between basic classical EEG features and cognitive scores (Kinney-Lang et al., 2017).

Thus, we propose to analyse already available resting-state EEG activity from children with epilepsy. All analysis will be appropriately cross-validated and will benefit from the close supervision of both clinical (RC) and an engineering (JER) supervisors. First, the EEG datasets will be preprocessed following state-of-the-art approaches. Clinically relevant events will be delineated and classical EEG analyses (e.g., power, average ERPs, etc.) will be applied as benchmarks to the more advanced connectivity investigations. Then, we will implement state-of-the-art functional connectivity analysis. We will assess the significance of the results, compare them with classical tools, and discuss the relevance of findings.

We expect to achieve a rigorous validation of the techniques and an increased understanding of this major aspect of epilepsy. Our approach agrees with the currently view of epilepsy as a network disease (Stam, 2014). Due to the routine use of EEG to screen for ictal activity in epilepsy, we are confident that our methodology could find a rapid acceptance in the field.

Aims

We hypothesise that functional connectivity analysis of routinely collected EEGs in children with epilepsy will reveal EEG markers of impairments by exploiting diffuse brain connectivity changes caused by the disease.

1. Apply quantitative methods to characterise the EEG activity in children with epilepsy.
2. Compare traditional methods with new approaches based on connectivity.
3. Identify potential markers of impairments in the EEG of children with epiplesy.

Training outcomes

- Being able to evaluate functional brain connectivity from time series.
- Being able to analyse both visually and quantitative EEG recordings.
- Being able to run statistically analysis of multivariate features from diverse populations samples.
- Being able to manage diverse data sources of both clinical and non-clinical origin.
- Being able to apply clinical research protocols and regulations.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you are encouraged to contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine