Generative models of abnormal brain dynamics in paediatric epilepsy: The role of nodes and states in generating epileptic activity.

Lead Research Organisation: University College London
Department Name: Institute of Health Informatics

Abstract

Project Motivation
In about a third of the >500,000 patients in the UK living with epilepsy, no current drug treatments will stop seizures completely. For some of these patients, identifying the 'epileptogenic zone' - the putative source of the epileptic brain activity - and neurosurgically removing it can cure their epilepsy. Identification of the epileptogenic zone often involves extensive data collection from patients, including recording intracranial electrophysiological data with stereo-encephalography recordings (sEEG).

Our overall aim is to aid the identification of key nodes within the epileptic network from sEEG recordings of spontaneous, and stimulated interictal brain activity. We will achieve this aim by addressing the following key objectives.
Objective 1) Characterise the temporal and spatial variability of spontaneously occurring interictal epileptiform discharges (IEDs).
Objective 2) Use IED variability to develop patient-specific brain-state classifiers to predict IED features from preceding background sEEG.
Objective 3) Test the classifiers' predictive validity against epileptiform discharges induced by single pulse electrical stimulation (SPES). Using Dynamic Causal Modelling, also estimate synaptic parameters underlying the classified brain states.
Objective 4) Simulate, in silico, network responses to stimulation in order to target SPES. Identify nodes whose stimulation will yield maximum information gain about synaptic network parameters.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021612/1 01/04/2019 30/09/2027
2421753 Studentship EP/S021612/1 28/09/2020 30/09/2024 Jamie Norris