An investigation of functional brain networks in drug resistant and well-controlled Idiopathic Generalised Epilepsy

  • Emily Pegg

Student thesis: Phd


Epilepsy is one of the most common neurological disorders, estimated to affect 70 million people worldwide (Ngugi et al., 2010). Around 20 % of people with Idiopathic Generalised Epilepsy (IGE) continue to have seizures despite treatment with antiepileptic medication (Brodie et al., 2012). The mechanisms of epilepsy drug resistance remain poorly understood. Previous studies have primarily investigated potential cellular or genetic explanations for drug resistance. Epilepsy is regarded as a network disorder in which seizures arise via transient, abnormal, hypersynchronous activity of large-scale neuronal brain networks. An increasing body of literature demonstrates that people with epilepsy have different resting state networks than people without epilepsy. This thesis aims to investigate whether network alterations are also implicated in drug resistance. Resting state networks in people with well-controlled IGE, drug resistant IGE, and healthy controls were compared using spectral power analysis and graph theoretical analysis of data derived from EEG and fMRI. Converging evidence from the results demonstrated large-scale network alterations in people with IGE compared to controls. In particular, in IGE, there was a suggestion of greater cortical hyperexcitability and an alteration in the topology of the network, which had a more regular configuration. One of the studies also suggested that network topology in well-controlled IGE differed from controls, but not between controls and drug resistant IGE. We posit that this is due to a drug induced network alteration in people who respond to medication which stabilises the network, rendering it less susceptible to the seizure state. The cause of drug resistance in some people with IGE remains unknown, but may involve a complex interplay between multifarious brain networks, influenced by inherent epilepsy severity. The results of this thesis are of potential importance in furthering knowledge of how drug resistance arises and as a possible basis for an epilepsy biomarker.
Date of Award1 Aug 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorMonty Silverdale (Supervisor), Rajiv Mohanraj (Supervisor) & Jason Taylor (Supervisor)


  • Network analysis
  • Graph theory
  • Drug resistant epilepsy
  • Epilepsy

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